<figure><img src="image_31.png" /><figcaption>**Figure 27.** (a) Ochratoxin A, (b) OPIF and (c) TPIF of ochratoxin for different excitation wavelengths, (d) ochratoxin TPIF signal as function of excitation power density using 710 nm as excitation, (e) zearalenone, (f) zearalenone OPIF signal for different excitation wavelengths, (g) SHG of zearalenone during two-photon absorption using different illumination wavelengths, (h) close-up of (g).</figcaption></figure>

ongoing development of high power LEDs, also UV LEDs, drives miniaturization and integration within handheld devices [310]. Although, it should be taken into account that LEDs feature a broader emission spectrum that might induce additional background fluorescence signals, or requires the implementation of additional optical filters.

(2) Advances in illumination and light collection optics, including freeform optical design, benefits the excitation power density, illumination beam properties and light collection efficiencies.

(3) Natural fluorescence emission signals typically show a low emission power, requiring a sensitive detection, which often results in the trade-off between detector size, sensitivity and spectral resolution. The increased availability of miniaturized spectrometer modules benefits the industrial integration of fluorescence spectroscopy.

(4) An ongoing trend towards the use of ML processing and AI models can be observed enhancing the detection algorithms, boosting the sensitivity and selectivity of the instruments.

In addition to the discussed one-and two-photon induced spectral measurement, fluorescence lifetime measurements are being investigated for aflatoxin detection, implying the measurement of time-resolved spectra [321]. However, since this technology requires consecutive fluorescence measurements, it is currently less suitable for in-line and real-time industrial implementation.

**Concluding remarks**

Fluorescence spectroscopy offers a non-destructive sensing of fluorescent mycotoxins. In general, when considering food products, the mycotoxin fluorescence is not observed directly, but its influence on the natural background fluorescence is monitored. TPIF requires significantly higher excitation power densities and shows lower fluorescence emissions, but might offer a more selective excitation, dependent on the products and the corresponding electron transition selection rules.
# 17. Raman spectroscopy for precise food quality assessment

Francesca Venturini

School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland
TOELT LLC, Research and Development, 8600 Duebendorf, Switzerland

E-mail: francesca.venturini@zhaw.ch

## Status

RS is a powerful analytical technique for the quality control of food because it provides specific quantitative information on the chemical composition and on structural information of the substances present, thus representing a molecular fingerprint.

The main advantages of RS are that it is non-invasive, non-disruptive, works also on opaque samples, does not require any sample preparation, has a high specificity, and is fast. For these reasons, RS is emerging as an alternative method to conventional analytical techniques.

RS has demonstrated being able to play an important role in food quality assessment. For example, it was successfully applied to the quality assessment of meat during processing and storing (freezing) [322]. The most useful Raman bands for determining the secondary structure of proteins are those of amide I (1645–1685 cm⁻¹) and amide III (1200–1350 cm⁻¹) while those for lipid monitoring oxidation are at 800–1800 and 2800–3000 cm⁻¹ [323]. The analysis of fatty acids is used for the assessment of geographical origin, thermal stability, and adulteration in vegetable oils [324, 325]. The relevant bands correspond to C=C stretching and bending modes, C–C and C=O stretching modes, C–H and CH2 twisting and scissoring modes (in 965–1750 cm⁻¹). In alcoholic beverages and alcohol-water binary systems RS allows monitoring fermentation, detection of counterfeited or adulterated liquors, and discrimination between liquors of similar characteristics [326]. In distilled alcoholic beverages it is essential to monitor the concentration of ethanol and methanol. The ethanol spectrum has one strong band (879 cm⁻¹), assigned to C–C stretching, and two weaker ones (1030, 1079 cm⁻¹) assigned to C–O stretching and CH3 rocking, respectively. Methanol has a very strong band at 1019 cm⁻¹, corresponding to C–O stretching [327]. In fermented alcoholic beverages and yeast fermentation processes the glucose and sugars in addition to ethanol are monitored to optimize the production [328]. This is also true for the analysis of honey since different sugars (fructose, glucose, or cheap sweeteners) are characterized by different bands (in the range 700–1700 cm⁻¹): their relative intensities provide information on adulteration, origin, or physicochemical characterization [329]. In the dairy industry, RS allows to determine intrinsic analytes such as milk components (protein, fats, and lactose) and adulterants and contaminants (non-dairy proteins, microorganisms, antibiotics, and pesticides) [330]. Finally, RS plays an important role in agriculture because it can detect biological and chemical pollutants [331].

## Current and future challenges

Despite the large number of advantages, the diffusion of RS in food applications has been hampered so far by two major challenges: the inherently weak signal, which is 10⁻⁶–10⁻⁹ times weaker than Rayleigh scattering, and the presence of fluorescence, which is stronger and broader than the Raman fingerprint and can completely mask it.

To cope with these challenges the conventional approach is to carefully design the optical setup. The most critical decision is the choice of the excitation wavelength. In fact, to reduce or eliminate the contribution of fluorescence, it is advantageous to choose an excitation wavelength in the NIR (typically 785 nm or 1064 nm). This comes to a price, however, since the intensity of the Raman signal is inversely proportional to the fourth power of the wavelength. A further parameter is the laser intensity on the sample since the Raman signal is proportional to the excitation intensity. Unfortunately, food samples are prone to photobleaching and thermal degradation. Therefore, the laser power and the spot size (influenced by the design of the excitation optics) need to be chosen such as to not affect the sample. A further challenge to be taken into account when designing the optical setup is that the correlation between the absolute intensity of the Raman band and the analyte concentration is not always straightforward. The Raman intensity depends not only on the analyte concentration but also on other factors, such as the laser power, optical geometry, and instrumental effects. The most critical of these is the laser stability. To compensate for this effect, strategies include the determination of ratios between intensities of different bands and the use of internal or external reference substances.
<figure><img src="image_32.png" /><figcaption>**Figure 28.** Overview of the most common strategies to enhance the Raman signal. CERS: cavity-enhanced Raman spectroscopy, TERS: tip-enhanced Raman spectroscopy, RRS: resonant Raman spectroscopy, CARS: coherent anti-Stokes Raman spectroscopy, FERS: fiber-enhanced Raman spectroscopy, SERS: Raman spectroscopy.</figcaption></figure>

Finally, an additional challenge is related to the frequent existence of overlapping bands, which influence the spectrum. This makes it difficult to use polynomial-based methods to automatically correct for baseline effects and requires the use of mathematical band-narrowing and resolution-enhancement methods.

These challenges have stimulated a lot of studies on the improvement of the experimental setup including special signal-enhancement schemes and on the development of advanced signal processing techniques, focused on reducing the noise, removing the background (for example due to fluorescence) and extracting the relevant information.

### Advances in science and technology to meet challenges

The advances in science and technology can be divided into strategies to improve the signal-to-noise ratio by enhancing the weak Raman signal (figure 28) or suppressing the background, and development of post-processing algorithms to extract the desired information from the data.

The most frequent approach to enhance the Raman response is surface-enhanced RS (SERS), which exploits the interaction of the sample with metallic nanoparticles. Similarly, tip-enhanced RS (TERS), a variant of SERS, uses a metallic tip to achieve nanometric spatial resolution. The applications range from the quantification of the relative contents of carotenoids, oleic acid, and phenols [332] to the detection of pollutants, additives, and heavy metals in various food or agricultural products [333, 334].

Other enhancement schemes are based on the choice of special excitation wavelength(s) to achieve resonance effects. These include resonance RS (RRS) and coherent anti-Stokes RS (CARS). RRS, in which the excitation wavelength corresponds to electronic transition, is particularly successful in the analyses of carotenoids and other pigments in foods, whose absorption bands are in the visible. For example, changes in carotenoid concentration can be used as indication of oxidation in olive oil (figure 29) [335]. CARS is a four-wave mixing process, which uses two excitation beams, a pump and a Stokes one, whose energy difference corresponds to a molecular vibrational energy. CARS has been successfully used to distinguish different edible oils and detect their adulteration [336].

In cavity-enhanced RS (CERS) the signal amplification is achieved through the enhancement of the light-sample interaction and laser intensity using multi-pass cells and cavities. Fiber-enhanced RS (FERS) works on a similar basis, with light guided into capillary, fibers and hollow-core photonic crystals. Both techniques, appropriate for gaseous samples, are of great interest to food (postharvest) chain management because they allow to monitor multiple gases simultaneously with one instrument. The most important gases are oxygen, carbon dioxide, and ethylene. These gases play an important role in the inhibition of bacterial growth and the ripening process [337].
<figure><img src="image_33.png" /><figcaption>**Figure 29.** Raman spectra of EVOO fresh and after ageing showing the effect of oxidation on the carotenoids' bands. Reproduced from [335]. CC BY 4.0.</figcaption></figure>

Advancements specifically targeted to improve the signal-to-noise ratio by reducing the fluorescence background include time-gated (TG) RS, which exploits the different time scales of fluorescence and Raman processes, shifted excitation Raman difference spectroscopy (SERDS) and modulated Ramans spectroscopy, which take advantage of the difference in spectral behavior of fluorescence and Raman scattering [338].

The improvements of post-processing algorithms include denoising, and background-suppression algorithms [339]. Due to the complexity arising from overlapping bands, multivariate data analysis methods are frequently necessary [340]. ML is well suited to capture complex relationships within large sets of spectra and is therefore intensively used in combination with most of the previously mentioned enhancement techniques. The newest approaches exploit deep learning for classification and regression tasks [341].

## Concluding remarks

Despite the numerous applications already existing, RS still has the potential to become a leading analytical technology for precise food quality control. Its high specificity, non-invasive, and non-disruptive nature provides a quantitative molecular fingerprint which contains information on the chemical and structural information. This is particularly relevant for food analysis, due to the complex composition and variability of substances to be monitored. The advances in the measuring and sampling techniques mentioned before, and the increased availability of components (lasers, detectors) at affordable prices, contribute to the appearance of more and more Raman setups and commercial instruments. This is bringing Raman out of the research lab into the field. The larger datasets available and the advances in algorithm development and deep learning data analysis capabilities additionally push the creation of more sophisticated models. As a result, the adoption of Raman as an analytical tool is expected to achieve a prominent role in food applications.
# 18. Raman spectroscopy for soil inspection—pathway towards on-site field measurements

Kay Sowoidnich, Martin Maiwald and Bernd Sumpf

Ferdinand-Braun-Institut (FBH), Leibniz-Institut für Höchstfrequenztechnik, Gustav-Kirchhoff-Str. 4,
12489 Berlin, Germany

E-mail: kay.sowoidnich@fbh-berlin.de, martin.maiwald@fbh-berlin.de and tdlas@gmx.de

## Status

Soil is of global importance in today's agricultural practice to secure the food supply for a steadily increasing population. Detailed compositional information is crucial for protection and sustainable use of soils and can also provide a better understanding of complex soil systems thus generating valuable inputs for on-site decision support tools to control fertilization and irrigation management in PA. Established analytical techniques for soil assessment are mostly based on the collection of mixed specimens from rather large areas (on the order of 10.000 m²) and have only limited ability to adequately capture the present spatial heterogeneity on agricultural fields. Unfortunately, the expensive, time-consuming and labor-intensive nature of such methods prevents the simple increase of the number of collected samples.

Optical techniques are able to provide a suitable alternative, particularly if they have the capability for field deployment. Element-specific methods, as e.g. X-ray fluorescence or laser-induced breakdown spectroscopy, are valuable tools to determine total mass fractions of a wide range of elements found in soils but they are unfortunately unable to assess the molecular binding form of these elements. Molecule-specific techniques can close this gap by providing information about the soil molecular composition. RS is such a method that is based on the excitation of molecular vibrations within the probed sample thus providing a fingerprint on molecular level. Specific advantages include the capability for rapid, contact-less and non-destructive analysis and low interference by water in the Raman fingerprint range, the latter making the technique particularly suitable for the analysis of soil with varying moisture contents, as e.g. present under field conditions.

The benefits of RS for molecule-specific soil inspection are exploited in a wide range of application areas. Beside the agricultural sector, e.g. for the characterization of phosphorous compounds [342, 343], this also includes soil analysis within archaeological sites [344], investigations of terrestrial analogues in preparation for space exploration [345] and soil forensics [346]. Moreover, Raman spectroscopic studies on soil are reported to assess traffic pollutants in urban soil [347], for the detection of microplastics within soil [348] as well as for the quantification of soil gases [349].

## Current and future challenges

Despite the above-mentioned advantages, RS is currently still underexplored in the field of soil science. One major issue is that the weak Raman signals can easily be masked by interference from fluorescence originating from soil organic matter and clay minerals [350]. Selected approaches are proposed to address this fluorescence issue. Mathematical methods for background subtraction are easy to implement but frequently have difficulties in addressing strong fluorescence interference. Microscopic Raman investigations permit to focus on very small areas with diameters around 1 µm and thus enable to diminish the extent of fluorescence from out-of-focus regions. Nevertheless, residual fluorescence contributions can still overwhelm weak Raman signals even when confocal Raman microscopy is applied [343]. Prolonged sample exposure to the excitation laser radiation ('fluorescence bleaching') has the potential to reduce fluorescence intensity but may cause detrimental effects like sample heating, modification or damage. Another possibility to address the fluorescence issue is the usage of deep UV excitation wavelengths below 250 nm that permit for a spectral separation of the Raman signals from the fluorescence emission. However, it is reported that the high energy of the laser photons may cause decomposition of organic matter within the soil, sometimes even when the sample was cooled down to -100 °C [342]. In case of on-site soil investigations, e.g. on agricultural fields, ambient light poses a further challenge to the Raman investigations. This type of interference can also mask the Raman signals of interest and further requires short exposure times to avoid saturation of the charge-coupled device (CCD) detector [351].

For representative assessment of the soil molecular composition, the present spatial heterogeneity at multiple length scales needs to be considered as well. To address the variability on the micrometer scale, a sufficiently large measurement spot size, e.g. 100 µm diameter, is a suitable methods while spatial variations on the millimeter scale can be captured using a raster scan approach, e.g. applying a 10 × 10-point grid pattern [352].
<figure><img src="image_34.png" /></figure>

**Figure 30.** Schematic workflow for shifted excitation Raman difference spectroscopy (SERDS) demonstrated for a soil sample. Two Raman spectra are recorded using two slightly shifted excitation wavelengths (spectral shift: ca. 10 cm⁻¹, corresponding to 0.6 nm at 785 nm excitation). Raman signals follow the shift in excitation wavelength while non-Raman contributions remain unaffected. Subtraction of the two Raman spectra generates a SERDS difference spectrum separating Raman signals from interferences. A SERDS spectrum in conventional form is obtained by reconstruction of the derivative-shaped SERDS difference spectrum, e.g. using numerical integration. Contributions from the soil constituents quartz, calcite and organic carbon can be identified by SERDS, e.g. using reference SERDS spectra of pure materials or literature data. Asterisks indicate Raman signals of the sapphire optical window used to cover the soil sample.

Moreover, the detection of species that are only present at low abundance, e.g. nutrients or pollutants, may be challenging. Here, methods for Raman signal enhancement are beneficial, e.g. SERS, as demonstrated for the quantification of available nitrogen in soil [353] or the detection of pesticides [354]. However, in most cases an extraction procedure is required and the resulting solution (e.g. filtered supernatant) is then brought in contact with SERS-active metal nanoparticles for Raman analysis.

## Advances in science and technology to meet challenges

The major challenge of interference by fluorescence and ambient light can effectively be addressed by SERDS [355]. In addition, long excitation wavelengths around 785 nm are particularly advantageous. As shown for selected soils, when using excitation wavelengths above 700 nm fluorescence emission can be much lower compared to excitation wavelengths below 600 nm, thus already enabling a reduction in fluorescence intensity [356]. Moreover, 785 nm is an established excitation wavelength in RS and it still maintains compatibility with sensitive silicon-based CCD detectors. Shorter excitation wavelengths in the red spectral range, e.g. at 671 nm, may be beneficial to access the high wavenumber region with the potential to specifically address organic components or to determine the soil moisture content.

SERDS applies two slightly shifted laser wavelengths, e.g. 784.6 and 785.2 nm, and two Raman spectra are recorded subsequently. Raman signals directly follow the shift in laser excitation wavelength while other contributions remain unaffected. A subtraction of the two Raman spectra thus separates the Raman spectroscopic information from such interferences. To obtain a Raman spectrum in conventional form, the derivative-shaped SERDS difference spectrum can be reconstructed, e.g. using numerical integration. It should be noted that shot noise contributions caused by LIF cannot be removed and are thus carried through into the SERDS spectrum. Nevertheless, soil constituents can be identified based on reference spectra obtained from pure materials as exemplarily shown for the soil minerals quartz and calcite (see figure 30). Originally applied using bulky tuneable laser sources, only the recent availability of compact dual-wavelength diode lasers has unlocked the potential of SERDS for portable instruments [357].

A recent proof-of-concept study highlights the capability of SERDS for detecting the soil constituents quartz, feldspar and hydroxyapatite [358]. Systematic laboratory investigations on 150 soil samples collected from an agricultural field in Germany demonstrate that 13 different soil minerals and amorphous carbon
can be detected and that even quantitative analysis is possible, e.g. for the prediction of the soil organic matter [352] or soil carbonate content [350]. The technique is subsequently translated towards the field environment by deploying a portable SERDS system for on-site analysis at selected measurement positions [357].

Based on the successful field study, a modified version of the portable SERDS instrument for the integration into a sensing platform to enable continuous soil monitoring across the field is currently under development. To fully exploit the benefits of the technique, a holistic approach combining sensitive hardware with advanced data analysis methods (ML) and sensor data fusion is pursued. The larger focal depth required for non-contact continuous measurements comes at the expense of increased spot sizes resulting in reduced power density. However, this effect can be compensated by using higher power dual-wavelength diode lasers [359]. During the movement, the two Raman spectra required for SERDS are not recorded at the same position but this issue may be addressed by fast spectral acquisition in the kilohertz range using a charge-shifting technique [360].

## Concluding remarks

SERDS is demonstrated as an efficient tool to extract Raman signals of soil constituents from interfering backgrounds, e.g. fluorescence and ambient light. The technique is able to identify and differentiate a wide range of soil minerals (including silicates, titanium dioxides, phosphates and carbonates) as well as organic carbon. Beside this qualitative analysis, the obtained molecular fingerprint also enables quantitative measurements, e.g. as shown for the prediction of the soil organic matter and the soil carbonate content. The development of a portable SERDS system specifically designed for soil inspection opens the avenue for on-site soil assessment directly on agricultural fields. An advanced SERDS instrument as part of a sensing platform for continuous soil monitoring, e.g. across agricultural fields, is currently under construction. The results highlight the capability of SERDS and show great promise to transfer the technique to further applications in the agro-food sector (and beyond). Here, successful proof-of-concept experiments are conducted for the analysis of plant leaves and apples [351] as well as animal feed pellets [361].

## Acknowledgment

The authors gratefully acknowledge Maria Krichler for software development, and Lucas Wittenbecher and Thomas Filler for development engineering support. We also would like to thank all involved colleagues from the project consortium I4S (Intelligence for Soil) for sample collection and preparation, and the landowner Golo Philipp for access to the agricultural field. This study is funded by the Federal Ministry of Education and Research (BMBF) within the projects RaMBo (Grant Nos. 031A564C, 031B0513C and 031B1069C) within I4S as part of the funding measure BonaRes (Soil as a Sustainable Resource for the Bioeconomy) and Research Fab Microelectronics Germany—FMD (Grant No. 16FMD02).
# 19. On-site detection of water pollutants: the potential and limitations of optical spectroscopy

Matthieu Roussey and Kai-Erik Peiponen

Department of Physics and Mathematics, Center for Photonics Sciences, University of Eastern Finland, PO Box 111, 80101 Joensuu, Finland

E-mail: matthieu.roussey@uef.fi and kai.peiponen@uef.fi

## Status

Environmental pollution is at the heart of most research related to climate change and the European Union focused numerous activities on such a crucial field impacting global health, diversity, and food resources [362]. Detecting water pollutant with optical tools is not an obvious choice since water absorbs most of NIR and mid-infrared (MIR) light, where the main spectral features of organic materials are located. However, photonic methods are known to be versatile providing different and complementary ways to these drawbacks with the advantage of being non-invasive, i.e. the sample is not destroyed, and therefore several optical measurements using similar or different methods can be done consecutively.

Spectroscopic analysis is the study of the response in wavelength of an object to an illumination. The source must therefore be broad (or tuneable), e.g. white light, and a dispersive element, e.g. a grating or a prism, must be used. A reference is also mandatory to normalize the spectra correctly, in the case of water, it can be, for instance, deionized water. The spectral response varies from one pollutant to another, but it is to be noted that most of them present absorption peaks in the MIR range [363].

Nowadays, two main robust methods are driving the research on pollutants having the form of particle or affecting objects that can be further analyzed: RS (which can be extended to surface enhance RS) and FT infrared (FTIR) [364, 365] spectroscopy. Both methods give unique signature of constituents of matter in a reliable way. However, both are laboratory techniques requiring expertise in the measurement operation itself and in the sample preparation, which can be laborious and cost ineffective. Commercial portable and even handheld FTIR and Raman spectrometers have been developed for chemical species analysis of solid-, liquid- and gaseous samples. These have been used for product quality control, e.g. in agriculture, petroleum, pharmaceuticals etc. Unfortunately, FTIR detection of contaminants in water is ineffective because the IR probe radiation is strongly absorbed by water itself. The drawback with RS is the fluorescence of some organic materials in water may dominate the Raman signal, especially when short wavelengths are used as probe. Using longer probe wavelength (SWIR) does not improve the method, because the signal is absorbed within a small layer of water or organic material before reaching a microplastic. Hence, the Raman signal can be weak and problematic to analyze regarding quality of a complex water sample.

Other techniques, like digital holography or fluorescence spectroscopy, can greatly improve the situation in some cases, e.g. the detection of microplastics. Some devices exist for on-field measurements, typically optrodes [366]. They are complex optical devices usually based on chemical transducers allowing, optically, to measure one specific constituent. It means that the amount of optrodes must be multiplied by the number of pollutants to be measured.

Spectroscopic methods are interesting only if one can analyze and understand the detected signals. Methods based on different order of derivatives of measured spectrum, PCA, generalized two-dimensional correlation spectroscopy and nowadays ML, data mining and AI help to obtain faster and more reliable or targeted results [367, 368]. Combining mathematical, physical, and chemical understanding of a sample yields a possible simplification of the measurement methods, which constitutes a great step forward for in-situ measurements.

## Current and future challenges

Open water is, by definition, a complex medium in which are present useful nutrients, but also numerous particles of different sizes, shapes, and compositions leading to difficult-to-interpret signals independently on the method used for analysis. Moreover, water can be colored, hazy in liquid form but we also must consider vaporous and solid form of water. In the first one, very thin and light particles can be carried over long distances while in the later form, bacteria and other pollutants can be imprisoned for extremely long periods. Current climate changes make even less stable the equilibrium between what is discarded by humankind in nature, i.e. pollutants, life (fauna and flora) in water bodies, and what is released by melting ice, without counting on the temperature variations affecting the ecosystem, the water flow around the Earth, and the exchange of water between ground and ocean [369]. In other words, more pollutants in water are moving faster.
<figure><img src="image_35.png" /><figcaption>**Figure 31.** Illustration of a possible scheme to study and understand pollutants in water.</figcaption></figure>

The first challenge one faces in building a device for the detection and identification of pollutants is simply the type of pollutant (figure 31). It can be chemical and completely dissolved in water, which is what happens for instance in case of accidental pollution. An example of future impacting case could be a leakage happening at an energy station supplying the new carburants such as methane and nitrous oxide that can dissolve in water. But pollutants can be particles of a few nanometers or several millimeters such as (micro)plastics, metal, other organics. Pollutants are also, in some cases, complex entities due to a long stay in water, i.e. a particle on which a biofilm has grown and includes bacteria, viruses, other particles. Each of these pollutants requires a specific monitoring technique.

From the optics point of view, it can be a spectral measurement, a transmittance measurement, an imaging of the scattering, or some more complex measurements such as Raman or FTIR spectroscopies. Such methods operate well in limited cases. Typically, when the volume to be analyzed is not too large and the matrix not too complex. It means that some sample preparation (filtering, cleaning, sorting) is required, which makes the work laborious and often expensive. It makes then obvious that another challenge is the volume measurement. Some methods for microplastic detection, for instance, have used dyeing to recognize the particles [370]. Another issue is the portability of the device. It requires light sources, detectors, and robust components which are usually not compatible with accurate spectroscopic measurements.

Finally, VNIR spectroscopy are not always adapted to the analysis of pollutants simply because of the frequency range in which the elements show their main spectral features. However, techniques based on combining both imaging and spectroscopy allows for identification and quantification. Moreover, a mathematical analysis of signals can be done to observe variations corresponding to a specific condition of the monitored environment or an element in it. Usually, such a method is then not sufficient to give a clear identification but allows for very fast screening of large volumes.

### Advances in science and technology to meet challenges

New technologies offer advantages that can overcome the limitations of the current devices. We will here name three of them: (1) HSI [371] allows the measurement of a spectrum for each pixel of an image, which means that a relatively large area can be screened at once. It leads to the identification of particles or elements mixed in a liquid, for instance. The price is a huge computational memory and a lack of resolution. This technique has been recently demonstrated has very efficient for the identification of microplastics directly in water [372]. (2) Ultra high-resolution imaging allows counting and localization of particles. Moreover, the method leads to a knowledge of the shape and motion of the particles together with their environment. Unfortunately, the technique does not work for chemicals dissolved in water and does not give a reliable identification of the particles since it often operates at only one single wavelength [373]. (3) LiDARs can probe very large volumes at the price of a lower resolution and a lack of selectivity because of the wavelength [175].

One can see that those three methods are together solving most of the current challenges, and therefore they must be combined. To achieve this, one must have an *a priori* knowledge of the studied environment (reference) and define the problematic to target specific pollutants (database). In this case, one can operate
on a few wavelengths which is simplifying the use of LiDARs, allows ultra-high imaging devices, and reduce the memory required by HSI since it will be no longer needed to record an entire spectral cube [372]. For instance, Garaba et al state that ‘1070 nm is the best wavelength to discriminate plastics from extremely turbid water and also determining submerged plastics less than 5 cm’ [374].

However, research is progressing rapidly, and combination of techniques and methods is now possible by the novel method of prototyping using 3D printing allowing fast development of light devices, by integrated optics leading to lower needs in power consumption (even power by solar cells) and smaller devices that can be parallelized for more efficient measurements. One short term solution for water pollution monitoring is, for instance, the development of a modular sensor, i.e. a combination of different systems in a modular way, allowing the simultaneous measurement of different pollutants as proposed in the EU project IBAIA [375].

## Concluding remarks

Water pollution is due to actions of mankind but also nature, such as, acid rain resulting from eruption of a volcano. Pollution particles in atmosphere can settle down over natural water bodies and oceans. Polluted soil in turn can induce pollution of groundwater sources. This a vicious circle accelerated by the climate change. A difficult and long road towards efficient portable device for spectroscopic measurements remains in front of us. We can however dream of miniaturized compact tools operating at lower power can further be embarked in submarines, boats, buoys, or even on UAVs, or drones, that give access to large area. Systems could be powered by solar or wind energy with data transferred to bigger units for further analysis. The key-solution will certainly come from a multidisciplinary collaboration between researchers in integrated solutions and AI and ML algorithms for a fast and optimized data processing. Often, a compromise must be made to achieve a portable device for in-situ measurements, and this is made through discussions with companies, organizations, and governments taking care of water quality to answer the question of what is the real need? This brings us to the starting point, i.e. the definition of the problematic and the cause of the pollution.

## Acknowledgments

This article is based on knowledge acquired over the years working on large consortia projects CEIWA funded by Business Finland (Grant No. 541/31/2021), IBAIA funded by the European Union's Horizon Europe research and innovation programme (Grant Agreement No. 101092723), and the Academy of Finland PREIN flagship (Grant No. 20166).
## 20. FTIR-based quality control of commodities

Antoni Femenias<fnref n="1" />, Polina Fomina<fnref n="1" />, Vjekoslav Kokoric<fnref n="2" />, Robert Stach<fnref n="2" /> and Boris Mizaikoff<fnref n="1" />,<fnref n="2" />

E-mail: antoni-1.femenias@uni-ulm.de, polina.fomina@uni-ulm.de, vjekoslav.kokoric@hahn-schickard.de, robert.stach@hahn-schickard.de and boris.mizaikoff@uni-ulm.de

### Status

FTIR spectroscopy has a long-standing history in commodity quality control since its first use in the late 1970s [376]. Thereafter, advances in optical components and computing have accelerated the evolution into easier-to-use and affordable FTIR devices establishing the method in a wide variety of application scenarios [377]. Current interest in FTIR spectroscopy in agriculture and food industries is driven by its inherent advantages over conventional dispersive IR technologies, offering improved signal-to-noise ratio, faster data acquisition, multianalyte analysis, and higher spectral resolution [378]. In addition, miniaturization potential of FTIR devices and modern chemometrics/algorithms enables rapid on-site/in-field monitoring of quality, safety, and fraud parameters from wide range of commodities and is nowadays considered a viable alternative vs. conventional chemical methods [376, 379]. While FTIR spectroscopy usually accesses limits of detection (LODs) down to the ppm and occasionally ppb level, several analytical methods based on, e.g. LC-MS are significantly more sensitive. However, these methods are time-sensitive, off-site and require an appropriate laboratory infrastructure and trained personnel [380, 381].

Figure 32 schematically shows the light path within the most commonly used sampling approach in combination with FTIR—attenuated total reflection (ATR) including the radiation source, the ATR assembly and the detector. Using ATR is the most versatile sampling approach in commodity analysis suitable for a wide range of sample aggregate states including liquids, gels, pastes, powders, and homogenized solids.

FTIR spectroscopy is a fast, selective, and non-destructive approach providing access to a ‘molecular fingerprint’ of the sample facilitating quality control of commodities. This has led to studies applying FTIR technology to a wide range of commodities (e.g. cereals, vegetables, dairy products, fats, oils, nuts, etc.) [378]. Particular interest has focused on using portable and even hand-held FTIR-ATR systems using versatile waveguide-assisted sampling interfaces [379, 383].

The combination of FTIR spectroscopy and chemometrics has been proven as a viable tool for commodity quality control along the supply chain. Spectral databases for specific parameters of commodities can be established harnessing minute variations in the dataset to detect minor alterations [384]. Furthermore, the evaluation process can be automated, thus facilitating high-throughput screening in agricultural applications, and in particular for various commodities. Combining chemometric strategies and FTIR techniques have demonstrated their potential for, e.g. the analysis of proteins in milk and dairy products [385]. Similarly, commodities such as peanuts and maize were investigated within such a screening scenario. A decision tree method proposed by Kos et al demonstrated the classification of maize samples at 1750 and 500 µg kg⁻<fnref n="1" /> thresholds for the mycotoxin deoxynivalenol (DON) resulting from fungal contamination with an accuracy of 79 and 85%, and peanut samples for the mycotoxin aflatoxin B1 (AFB1) at 8 µg kg⁻<fnref n="1" /> with an accuracy of 77%. This confirms the suitability of the method for classifying samples at the respective regulatory limits [386]. An overview of the reported studies using FTIR for monitoring quality, safety, adulteration, and other parameters in food commodities is given in table 1.

### Current and future challenges

The translation of technology from the research laboratory into industry is a crucial step for advancing FTIR technology to be adopted by regulatory measures imposed by respective agencies. From a device perspective regarding portability and design, the transition of FTIR devices from the laboratory into the field addressing real-world samples remains challenging for accurate and reliable commodity analytics. Portable device technology requires miniaturization of optics, overall device dimensions, and the use of correspondingly integrated microelectronics. In addition, bright wideband yet miniaturized light sources and detectors are crucial. Miniaturized interferometers may take advantage of Fabry-Pérot configurations in lieu of conventional Michelson interferometers, yet, always at the trade-off of reduced spectral resolution. Recent advances in light sources (e.g. MEMS-based emitters) and detectors (e.g. pyroelectrics) offer reduced production and overall device costs, albeit at the compromise of increased noise and lower spectral

<footnote n="1">¹Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany</footnote>

<footnote n="2">²Hahn-Schickard, Sedanstraße 14, 89077 Ulm, Germany</footnote>
<figure><img src="image_36.png" /><figcaption>**Table 1.** Selected applications using Fourier transform infrared spectroscopy for monitoring quality, safety, adulteration and other parameters in food commodities. Adapted from Cebi *et al* [383].</figcaption></figure>

<table><thead><tr><th>Commodity</th><th>Parameters analyzed</th><th>Equipment</th><th>Type of analysis</th></tr></thead><tbody><tr><td>Cereals</td><td>Mycotoxins, proteins, lipids, starch</td><td>FTIR-ATR, FTIR</td><td>Quality and safety control, authentication</td></tr><tr><td>Nuts</td><td>Lipids, proteins</td><td>FTIR</td><td>Quality control, adulteration</td></tr><tr><td>Milk</td><td>Fatty acids</td><td>FTIR-ATR</td><td>Safety control, adulteration</td></tr><tr><td>Vegetables</td><td>pH, °Brix, carbohydrates profile, nitrate, phenols, citric acid</td><td>FTIR-ATR</td><td>Quality, safety and maturity control, classification</td></tr><tr><td>Fats and oils</td><td>Total fat, fatty acid composition, fatty acid profiles</td><td>FTIR-ATR, FTIR</td><td>Quality, safety and adulteration control</td></tr></tbody></table>

resolution. However, if these limitations are addressed more efficient, compact, and versatile portable FTIR spectrometers are certainly feasible [383].

Besides the technology, the application of FTIR devices in agricultural scenarios has its own challenges. The complexity of food matrices limits the discrimination and identification of spectral fingerprints associated with target compounds. This complexity frequently requires complementary sample preparation schemes including but not limited to extraction, purification, and clean-up to separate target analytes prior to the analysis. In addition, spectral information may be extracted using deconvolution techniques and chemometric algorithms [387]. Last but not least, while preconcentration schemes may indeed enhance the sensitivity and reduce interferences and sample matrix effects, several food contaminants need to be detected at ppm-to-ppt concentration levels (e.g. mycotoxins in cereals) requiring outstanding performance of these strategies [388].

Environmental effects (e.g. temperature, humidity, etc.) can negatively influence the spectral data quality, thus data pretreatment strategies are crucial to mitigate those effects and establish sufficiently robust calibration and classification models. A main issue when establishing such models for food analysis are limited and unbalanced datasets and the inherent variability of natural food products. Several studies reported rather sparse datasets to train the associated models, which are of insufficient quality for in-field analysis scenarios [389]. In addition, only few studies include validation procedures to test the robustness of the established models via external samples [390]. Yet, robust chemometric algorithms modeling the variability of commodities and associated matrices can respond to the analytical problems found in food analysis, such as changes in the refractive index at high analyte concentrations, matrix effects interferences and overlapped peaks, natural sample variability, non-linearity between the spectral data and sample concentration and variable selection. Evidently, advanced data evaluation models are crucial to increase the
adoption of FTIR-based analysis technologies specifically for commodity analysis, and more generally in agricultural applications.

### Advances in science and technology to meet challenges

With a growing world population and shrinking cultivation areas due to climate change and urbanization, safe food supplies remain a grand challenge also in view of increasingly strict regulations and limited resources for food production.

An area reporting significant progress in assistance of analytical techniques such as FTIR-based technologies is ML, which includes the application of robust and innovative data analysis strategies for improved predictions and decisions based on FTIR-derived data. For example, Tsakanikas et al [391] developed a high-performance ML workflow for raw food categorization. The workflow was based on PLSs regression and SVM classification for multi-class categorization of seven raw food types. The classification performances obtained exceeded 84% accuracy for the different commodities. Conversely, Öner et al [388] tested different ML approaches including adaptive boosting (AdaBoost), Random Forests, SVM, and multilayer perceptron (MLP) for classifying DON-contaminated maize samples at EU regulatory limits with MLP demonstrating >91% correctness of the classification. These results confirm that ML approaches are indeed valuable assets for extracting crucial information from FTIR-based data. It should be noted that such algorithms may be implemented on microcontrollers, which facilitates an additional level of device integration in a cost-effective way.

An essential advancement is the development of faster integration times facilitating real-time operations. Real-time analysis is particularly crucial in food processing industries interested in on-the-fly quality control enabling rapid decision-making, production optimization, cost-effectiveness and minimization of waste. With faster integration times and improved chemometric tools, quality parameters may be obtained within a fraction of the time conventional techniques currently require [392, 393].

A major evolution of FTIR technology is the development of portable and even handheld devices. These devices offer on-site capabilities and are ideally capable of considering chemical and biological parameters in complex matrices. Advances in this field should be focused on the development of dedicated analyzers including tailored miniaturized light sources and detectors for specific targets analytes considering constraints related to the required wavelength regime [383, 394].

Another recent development in the field of solid heterogeneous sample analysis is FTIR-based HSI (FTIR-HSI). This technique is particularly applicable to samples such as food powders during industrial processing. By combining the power of FTIR spectroscopy with imaging capabilities, FTIR-HSI enables the analysis of the composition and quality of food products in real-time [395].

Lastly, FTIR micro-spectroscopy has emerged as a valuable tool for analyzing samples at a microscopic level. This technique provides detailed information on the molecular composition of small samples and thin layers with dimensions reaching 10 µm enabling to study commodities at a micro-level [396]. FTIR microscopy allows the identification of inorganic and organic compounds within a sample matrix (e.g. particulates) or the analysis of small commodity particles (e.g. single kernels), which translates into minimum sample requirements [397].

### Concluding remarks

FTIR is a well-established spectroscopic technology, yet for the analysis of commodities and monitoring/quality control still an emerging tool. Food industries, agriculture and authorities increasingly demand robust and reliable devices for monitoring food quality parameters in the field, which renders advances in FTIR technology crucial toward adoption as a validated analytical method, e.g. in quality control. FTIR-based techniques advantageously provide rapid data acquisition times, high sample throughput, are reproducible and require minimal-to-no sample preparation. Hence, they are increasingly adopted as a routine tool for but not limited to analyzing food adulteration, food quality, food authenticity and food safety.

The transition of FTIR-based technologies into the food industry hinges on robust spectral data pretreatment, evaluation, and mining, which aid in minimizing sample preparation and separation strategies. ML algorithms have the potential to overcome current data mining limitations toward enhanced accuracy and efficiency, if appropriate trainings data is available. The combination of faster signal integration times with improved chemometric tools potentially allows for real-time operation and on-the-fly analysis.

Packaging advanced data mining technology with portable FTIR systems renders on-site analysis more accessible and user-friendly and allows for operation with non-trained staff. Finally, FTIR HSI facilitates real-time analysis of heterogeneous solid samples. While FTIR micro-spectroscopy provides the molecular composition of minimal sample volumes, particulate samples or single kernels at the micrometer level.
In summary, while being well established in routine analytical laboratories, FTIR spectroscopy may also evolve into a widely adopted molecular analysis technique for controlling and monitoring the quality and safety of a wide range of food and feed commodities.

Acknowledgment

This work was supported by the Europe Union's Horizon 2020 Research and Innovation Programme project PHOTONFOOD (Grant Agreement No. 101016444) and is part of the Photonics Public Private Partnership. This work was partially supported by the Norwegian Research Council through the project SFI Digital Food Quality (Project No. 309259).
## 21. Optical fiber sensors for precision agriculture

Maria Konstantaki and Stavros Pissadakis

Institute of Electronic Structure and Laser (IESL), Foundation for Research and Technology- Hellas (FORTH), 70013, Heraklion, Crete, Greece

E-mail: mkonst@iesl.forth.gr and pissas@iesl.forth.gr

### Status

Due to the continuously growing global population, the demand for food production is constantly increasing, while challenges in the availability and cost of fertile soil, irrigation water and farming nutrients impose additional hurdles. Furthermore, the climate change and the requirement for environmental sustainability constitute the adoption of new and improved agricultural practices a necessity. In this content, the scope of PA is the optimum use of technological innovations in order to maximize agricultural yield while minimizing resources spent [398]. Traditional agriculture is a highly empirical process, whereas PA focuses into the parameterization and optimization of the agricultural practices using new technologies such as sensors, ML and biochemistry. In general, agriculture has been a sector slow in adopting technological advances, with, mainly, traditional agricultural scientists, i.e. agronomists, conveying new tools and methods in the field. However, the aforementioned challenges impose the involvement of specialist from diverse domains such as photonics and information science.

In recent years, various agricultural tasks have substantially advanced by the implementation of PA including field mapping, soil analysis, weather monitoring, labor and equipment management [399]. The wide spread of smartphones and mobile apps, the rapid commercialization of UAVs, the miniaturization of sensing probes and systems, and the suppressed cost of the above due to commoditization, render specific PA technologies a plausible solution even for small scale farms and non-experienced users.

Sensors constitute important parts of the PA value chain, facilitating the direct interaction with the measurands under consideration, while being interrogated in continuous or periodic mode. Optical fiber sensors (OFSs) in particular, is a mature technology with many applications in diverse application fields such as structural health monitoring, biomedical and environmental sensing [400]. OFSs operate remotely, without requiring electrical power at the measurement location, are immune to electromagnetic interference, they have small size and weight, are flexible at installation and induce minimum disturbance. They can provide real time monitoring of parameters and can cover a large area either through distributed measurements, or by exploiting their multiplexing capabilities. Although OFS penetration in PA is still at an early stage, there is great potential and investments in sensors for PA is expected to grow in the coming years.

### Current and future challenges

In the last decade, an increasing number of reports have been published on the development of OFSs for PA, encompassing a broad range of agricultural practices such as soil and irrigation assessment, plant growth monitoring, pathogen tracing and measurement of elemental nutrients, while exploiting a number of different optical and transduction schemes.

The irrigation process is instrumental for crop production, simultaneously impacting water resources sustainability, thus soil moisture constitutes a key parameter in PA. Different OFS techniques [401] have been examined, for soil assessment, including an optical fiber distributed temperature sensor that determines soil moisture through the thermal response of a buried optical fiber cable which is subjected to heat pulses [402]. Leone et al [403] have reported on a temperature compensated fiber Bragg grating humidity sensor, measuring soil volumetric water content values between 0% and ~37% employing a polymer micro-porous membrane and a functional packaging (see figure 33(a)). In the area of plant pathogen monitoring, an optical fiber distributed acoustic sensor aided by convolutional neural network (CNN) models was developed for the early detection of red palm weevil in large farms [404] (see figure 33(b)), while detection of Tomato leaf curl New Delhi virus (ToLCNDV) DNA has been presented using a localized surface plasmon resonance based U-bent fiber optic sensor [405]. Following the endorsement of drones in chemical pesticides field spraying, a long period grating OFS has been proposed for tracing spraying droplet distribution [406].

Optical fibers have also been proposed for observing plant growth and evaluating nutrient strategy results since, compared to destructive sampling, they offer a quick and less costly alternative. Indicatively, reports exist on the use of OFS to determine calcium concentration within hydroponic nutrient solutions [409] and macro-nutrients such as total nitrogen (TN) in soil [410]. Promising examples of OFSs have also been demonstrated to actively measure plant growth. This approach applies to both underground measurements, for imaging of plant root growth using a distributed strain fiber optic sensor [411], and
<figure><img src="image_37.png" /><figcaption>**Figure 33.** (a) Sensing principle of fiber optic thermo-hygrometer for soil moisture monitoring. Reproduced from [403]. CC BY 4.0. (b) Approach for Red Palm Weevil detection using optical fiber distributed acoustic sensing and machine learning. Reproduced from [404]. CC BY 4.0. (c), (d) Different configurations of wearable Bragg gratings sensors for plant growth monitoring. Reproduced from [407]. CC BY 4.0. © [2023] IEEE. Reprinted, with permission, from [408].</figcaption></figure>

on-specimen wearable sensors based on fiber Bragg gratings in different configurations [407, 408] (see figures 33(c) and (d)). Furthermore, OFSs well established in other major application fields e.g. structural health monitoring are also adopted within PA practices. For example, OFSs designed for structural health monitoring are employed for detecting leakages in water pipes of irrigation systems [412], or damages in agricultural structures [413].

## Advances in science and technology to meet challenges

The penetration of OFSs in the now-emerging and multi-disciplinary field of PA is dependent upon three major conditions: functionality, operational characteristics and cost of the developed sensors. Functionality will define the applicability of OFS designs into specific agricultural practices, while exploiting existing or new transduction mechanisms. It is expected that OFSs will be able to provide multi-parametric information -not being available before- in processes such as pesticide spraying, insect monitoring, thermal stress and nutrients circulation, readily providing unique insights to the farmer/agronomist that otherwise would require years of observation (see figure 34). Low cost, reliable probes with simple operation and interrogated by smartphone/tablet based read-out units are cornerstone characteristics that will enable agronomists and non-specialized farmers to utilize OFSs, maximizing their impact to the agricultural community and relevant market sectors. Such a broader adoption of OFSs into PA will allow farmers and agronomists to trace crucial parameters of their crops and fields, accelerating good practices related to prevention of harvest losses, optimal and sustainable use of resources and increase of production rate/profit.

The successful evolution of OFSs into PA is expected to lead to the realization of sensors that will monitor in-situ important functions (i.e. solar radiation, growth, hydration, or photosynthesis) at the crop, plant or tree, inaugurating the protocol 'fiber-to-the-tree (FttT)'. Future FttT OFSs may be realized in the form of needle or attachable pad, possibly exhibiting biodegradable functions [414], and will provide indispensable information on the impact of common agricultural practices, in conjunction with data collected from other OFSs, placed in air or into the soil around the specimens. OFSs, spatially dispersed over a large field, may operate over a fiber-wired or wireless grid, setting up an IoT sensing network for PA (figure 34). FttT OFSs may fuse technologies from Optofluidics [415], Optoacoustics, ML, and new Bio-/Chemo-sensing transduction materials, operating from the visible up to the far-infrared bands [416], where complex spectral signatures can be used for tracing substances with higher specificity. Well studied optical platforms such as those of Bragg and long period gratings, plasmonics, microstructured, multi-core and polymer optical fibers, and resonators (i.e. Fabry-Perot, ring, etc) can be used for developing OFSs that will offer measuring capabilities in the open field which have been available only in laboratory environment before [417], thus revolutionizing the field of PA.
<figure><img src="image_38.png" /><figcaption>**Figure 34.** Schematic on existing and emerging applications of optical fiber sensors within the context of precision agriculture.</figcaption></figure>

**Concluding remarks**

OFSs technology can evolve as a major foundation for the field of PA, prompting its broader adoption by agronomists and farmers, while maximizing the benefits for harvest yield, quality of crops, use of artificial and natural resources, and the environment. It is worthy to say that the plethora of commercialized and experimental OFS devices which already exist can constitute a sound background for their successful implementation in PA applications, minimizing technological risk and development costs, without compromising performance for the endusers. Emerging and future OFSs for PA applications will engage several optical, material, and data processing technologies so to provide new and multi-parametric sensing operations, accessible to non-specialized users, while imparting them with knowledge that would be hard to acquire using standard observation techniques, and the methods currently available. The engagement of diverse technologies into OFSs, will inherently offer them high versatility, allowing them to be applied with small adaptations in open fields, greenhouses, urban farming, or even vertical farming plants, covering the majority of PA cases.

**Acknowledgments**

Funding by ACTPHAST4.0 EU Horizon2020 Programme, Grant Agreement No. 779472 and LASERLAB-EUROPE, Grant Agreement No. 871124, is acknowledged.
## 22. Machine learning techniques applied to spectral data

Umberto Michelucci¹,² and Francesca Venturini¹,³

¹TOELT LLC, Research and Development, 8600 Duebendorf, Switzerland

²Computer Science Department, Lucerne University of Applied Sciences and Arts, 6343 Rotkreuz, Switzerland

³School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland

E-mail: umberto.michelucci@toelt.ai, and francesca.venturini@zhaw.ch

### Status

ML including more recently deep learning, has found extensive applications in the fields of agriculture and food technology [418, 419]. In agriculture some examples include the identification of early signs of crop diseases [420], enabling timely intervention, and reducing crop loss or even animal intrusion [421]. It can aid in optimizing resource allocation by predicting crop yields [422] based on spectral information [423], contributing to more efficient farming practices. In the food industry, ML models applied to spectral data can distinguish between different food types and assess their quality attributes [424], such as freshness, ripeness, adulteration, nutritional content, and chemometric analysis [425]. This technology enhances food safety and quality control processes, ensuring that consumers receive products of the highest standard. Overall, the use of ML on spectral data in food and agriculture empowers stakeholders with valuable insights, enabling them to make data-driven decisions that improve crop yields, product quality, and resource management. Currently used ML techniques include PCA for dimensionality reduction, SVMs for classification tasks, and CNNs to mention only a few.

Unfortunately so far, the applications have primarily focused only on classification [426] and regression tasks [427]. Trained ML models serve as valuable tools, simplifying and expediting specific tasks as described above, however, there is a notable gap in utilizing ML to gain insights into the chemical and physical processes that underlie those events. For example, while ML excels at distinguishing between high and low-quality food products, it has yet to be effectively employed to comprehend the chemical processes responsible for the transformation of a good product into a deteriorated. It is widely acknowledged that ML can be a potent tool for aiding the food and agriculture industries. Nevertheless, the next significant frontier that promises to revolutionize these fields lies in harnessing its capabilities to enhance our understanding of the physico-chemical processes in food production and processing, as well as agricultural production.

Spectral data is the ideal candidate to profit from the power of ML because it contains a large number of specific information it has multi-dimensional information (for example the spectra can be captured under excitation with different wavelengths, in two dimensions as in excitation-emission matrices, or in multiple dimensions, as in HSI) and can be acquired non-destructively, rapidly, and remotely (for example through satellites).

### Current and future challenges

As mentioned above, currently the application of ML to spectral data in the field of food technology and agriculture, although extensive, is limited to classification and regression tasks. There is a significant gap in leveraging this technology to gain deeper insights into the intricate chemical and physical processes underlying these events. The major challenges to close this gap can be summarized in six main points (figure 35).

(1) **Extraction of physical and chemical information from spectra:** the development of novel methods, potentially rooted in explainability approaches, is imperative. The methods require the creation of datasets containing spectra paired with informative chemical and physical data, as in [428, 429], which can serve as labels for training ML algorithms.

(2) **Feature selection:** the challenge of feature selection involves identifying relevant spectral bands responsible for or involved in specific physical and chemical processes. Existing feature selection methods have primarily been designed for selecting single features in moderately low-dimensional datasets, such as medical patient information [430]. Adapting these methods for selecting ranges of continuous features, like ranges of wavelengths in spectral data, remains an ongoing challenge.

(3) **Intelligent data processing:** many essential data preparation steps still rely on outdated and inadequate processing algorithms. New approaches, such as generative AI algorithms, have demonstrated remarkable success in tasks like background subtraction and denoising [431, 432], thus warranting adoption for more efficient data processing.
<figure><img src="image_39.png" /><figcaption>**Figure 35.** Overview of the major challenges in using machine learning techniques on spectral data.</figcaption></figure>

(4) **Specialized neural network architectures for spectral data:** developing neural network architectures optimized for 1D and 2D data (other than images) is crucial for effective spectral data analysis in food and agriculture. These architectures must be tailored to efficiently process and extract insights from the unique characteristics of spectral data. Current metrics developed in computer vision for 2D images are inadequate for 2D spectral data like excitation-emission matrices.

(5) **Sparsity of data:** the acquisition of a large amount of spectral data always requires time. The datasets are therefore frequently sparse and may be unbalanced for the proper application of ML methods.

(6) **Data standardization, interoperability, and scalability:** ensuring standardized formats and seamless data exchange is essential for integration with agricultural systems, while scalability and efficient processing are needed to manage growing data volumes. Real-time processing is also critical for timely applications like pest detection and quality assessment, requiring advancements in ML/DL algorithm design and system architecture.

In summary, while ML has already contributed substantially to the fields of food technology and agriculture, the formidable challenge lies in expanding its application beyond classification and regression. Leveraging the capabilities of spectral data and addressing the associated challenges in information extraction, data processing, feature selection, and neural network architecture design holds the promise of unlocking deeper insights into the complex chemical and physical processes governing these vital industries.

**Advances in science and technology to meet challenges**

The advances in science can be classified into six areas.

(1) **Extraction of physical and chemical information from spectra:** explainability approaches have gained traction [428, 429], but they only explain how models (in particular neural networks) work, and are not yet able to explain the underlying physical and chemical processes. Techniques like saliency maps [433, 434], feature selection [435] approaches and SHAP values [436] have great potential but more work is necessary to tailor them to photonics use-cases. Much more work should be done in creating datasets that will enable ML approaches to delve deep into the physical and chemical processes.

(2) **Feature selection:** feature selection in the context of spectral data analysis remains a challenging and evolving area of research. While existing methods excel in selecting single features in moderately low-dimensional datasets (e.g. medical patient information [430]), adapting these techniques to select relevant spectral bands poses ongoing challenges. One promising area of research, especially in the field of spectral data, is the use of genetic algorithms in conjunction with deep learning models to determine the fitness of the proposed band populations [437]. This approach will pave the road to new ways of understanding which spectral bands are relevant for the underlying physical and chemical processes.

(3) **Intelligent data processing:** the field of intelligent data processing has witnessed remarkable advancements, especially in the context of spectral data analysis [438, 439]. Outdated and inadequate processing algorithms could be replaced by more efficient and accurate methods, with generative AI algorithms playing a pivotal role, although those advanced methods have not yet reached the general scientific community, and remain in the status of proof of concepts that are difficult, if not impossible,
to adapt to general use cases. More research on general algorithms that work on a variety of data and spectrum types is still needed to be able for the scientific community to profit from them.

(4) **Specialized neural network architectures for spectral data:** to harness the full potential of spectral data analysis in food and agriculture, specialized neural network architectures optimized for 1D data have emerged as a key focus area [432]. These architectures are designed to efficiently process and extract insights from the unique characteristics of spectral data, such as wavelength patterns and spectral signatures. Researchers are developing tailored models, including 1D-CNNs and recurrent neural networks (RNNs), that can effectively handle spectral data's sequential nature. 1D-CNNs are particularly well-suited for spectral data since they preserve the correlation between adjacent wavelengths through one-dimensional convolutional operations.

(5) **Sparsity of data:** this challenge can be addressed by merging data from multiple sensors and by data augmentation techniques, that can increase the size of datasets synthetically.

(6) **Data standardization, interoperability, and scalability:** establishing unified data standards and adopting open-source, modular frameworks are possible approaches to address this challenge. The realization of efficient data processing allows efficient handling of growing data volumes and the combination of data from multiple sources (data fusion), such as hyperspectral, and conventional imaging, for a more comprehensive analysis of, for example, food quality and crop health.

**Concluding remarks**

Firstly, the advances in extracting physical and chemical information from spectra have shown promise with explainability approaches. However, there remains a crucial need to develop datasets that enable a deeper understanding of the underlying physical and chemical processes. This would open the road to exploit cross-interferences in the processes to extract more information rather than having to compensate for them. Secondly, intelligent data processing is making incredible strides, with generative AI algorithms poised to revolutionize the field. Nevertheless, these methods are yet to permeate the broader scientific community, necessitating further research to make them widely applicable. Thirdly, feature selection in spectral data remains a challenge. The integration of genetic algorithms with deep learning models offers an exciting avenue for selecting relevant spectral bands, potentially enhancing our comprehension of the underlying processes. Lastly, specialized neural network architectures tailored for 1D spectral data have emerged as a critical focus area. These architectures can efficiently handle the unique characteristics of spectral data, such as wavelength patterns and spectral signatures. In summary, there is still ample room for further research and development in these four areas to fully unlock the potential of spectral data analysis and its applications in various scientific domains.
## 23. Methods of data classification in spectral studies of food

Sławomir Paśko and Anna Pakuła

Institute of Micromechanics and Photonics, Warsaw University of Technology ul. Św. A. Boboli 8, 02-525 Warsaw, Poland

E-mail: slawomir.pasko@pw.edu.pl and anna.pakula@pw.edu.pl

### Status

Spectrometers, multi- and hyperspectral cameras are examples of photonics devices that have been successfully used in food-related research for many years. The data they produce is described as multidimensional. Often, the first operation performed on the data is to reduce their dimensionality. A popular algorithm in this area is PCA [440–443]. This algorithm facilitates the identification of principal components, which are linear combinations of the original variables and have the highest variance in the data set. This method can be used as a form of preprocessing before the actual data analysis. Dimension reduction reduces this process's computational and memory complexity and avoids over-fitting the model. Reducing the number of dimensions to two or three allows the data to be presented visually, making human analysis possible. In addition to this method, popular approaches are LDA [443, 444], in which linear combinations of variables are found that maximally separate the different classes in the data [445, 446], or independent component analysis (ICA), which is a technique for extracting the independent components of a signal [447, 448].

In the next step, the data are analyzed. Here, a cluster analysis called clustering can be used [449]. This method is used to group similar samples based on the calculated distance between them. There is no single algorithm here that can be called universal. There are many and choosing the right one depends on the characteristics of the data. A simple and computationally efficient one is the kmeans method [441]. It works best when the clusters are spherical and reasonably homogeneous. Another method is hierarchical clustering analysis (HCA) [441, 450]. It is used because it allows the creation of a hierarchy of clusters, which is a tree-like morphology. The data from this analysis can later be presented in the form of a graph called a dendrogram. HCA is divided into two types, agglomerative and divisive clustering. In the first case, there is no need to specify the number of clusters in advance. The process starts with single samples progressively combined with others on the basis of similarity. In the second case, specifying the number of clusters is needed because the process starts with one cluster divided into smaller groups based on internal differences. Among cluster analysis methods, density-based clustering (DBC) [451] or spectral clustering (SC) [452] are also popular in spectroscopy.

Discriminant analysis, factor analysis or regression can also be used to analyze the data. Discriminant analysis is aimed at finding some combination of variables that best separates sample classes. A well-known method is SVMs [443, 444, 453, 454], in which the operation is based on finding hyperplanes that separate different classes of samples in a multidimensional space. It copes well with non-linear relationships between variables. Also worth mentioning is principal component discriminant analysis [445], which combines PCA with discriminant analysis. Factor analysis is a technique that aims to identify the latent factors that make up the observed variables.

### Current and future challenges

Supervised methods in AI are playing an increasingly important role in the analysis of multidimensional data. This trend is clearly visible in publications from recent years [455–458]. This also applies to the interpretation of results, which for a long time was left to humans, but now AI is entering this area strongly. When teaching the network today, it is important to ensure that the data are balanced. When they are not, correct classification can be difficult or compromised. Various techniques are used to balance the data, including undersampling, oversampling or weighted cost functions. Although much has already been done, this area is a challenge for the future. The same can be said for methods to reduce the multidimensionality of data. Here, more ‘intelligent’ methods would be useful, which would be better able to extract information relevant to the study than the current algorithms.

Multidimensional data are often very difficult to interpret, especially the data obtained in the food industry. This is because they reflect complex patterns and relationships that describe the object or compound under study. These relationships can be difficult for humans to detect and subsequently model. High hopes can be placed on deep learning algorithms, which may, in future, be able to deal well with these complex relationships at a more general level. These algorithms are currently being used in spectroscopy, but this relates to specific applications. Graph-based methods, as well as hybrid ones, can be expected to develop.
Another thing that will be a challenge in the future is the scalability of multidimensional data analysis systems. Data sets are becoming larger due to the use of more and more precise and faster instruments [459]. The algorithms that have been used so far may not cope with them, and the challenge will be to develop new, more effective ones.

The use of these techniques in industry is only justified if data acquisition and processing will be realized in real time, looking of course from the point of view of the requirements of a problem under analysis. For example, in nut sorters, where several hundred of them can pass through the device in a second, the acquisition and classification time cannot be longer than a few milliseconds. Such a limitation forces the use of dedicated solutions, where the amount of collected data is limited only to those carrying information about the state of the product, on the basis of which the algorithm that is part of the system can make a quick decision. A challenge in this area is to develop solutions that would be able, based on data from laboratory equipment, to suggest in which bands or wavelengths useful the process information is located and what combination of these bands or wavelengths will give the best results.

### Advances in science and technology to meet challenges

The key thing in all research is the quality of the input data. Observing the constant progress in technology, including spectrometry, it can be expected that these instruments will be a source of more accurate and detailed data. More accurate data may allow to better distinguish the individual components of the spectral distribution and their values, which may have a positive impact on better results obtained from data dimensionality reduction or classification methods.

Similar progress is observed in the area of algorithms. It should be taken into account that especially algorithms related to AI will continue to develop very dynamically. These algorithms will be used not only for simple data analysis but the entire measurement and analysis process can be expected to be automated. The algorithms will select optimal parameters for the supervised algorithm and device. This can help both shorten the analysis time and improve the results. Taking the automation path further, we can think about measuring instruments and computing stations in the context of IoT [460]. In this system, locally made measurements would be processed by automated modules specialized in this type of data.

### Concluding remarks

Classification methods are a key element of data science. Without them, analysing the data collected by the instruments would be difficult, if not possible. Thanks to their use, it has become possible to widely use complex spectral techniques in the food-related area, among others. The use of AI methods in this area has greatly contributed to increasing the efficiency of classification. The expected further development of instruments and methods, including new classification algorithms, will undoubtedly increase the popularity of this technique and make the data obtained more precise and, therefore, the systems based on them more reliable.

Spectral methods are not only and exclusively spectrometers, multi- and hyperspectral cameras, but also high-speed industrial devices based on selected wavelengths. This area can be expected to develop as the number of food products being inspected increases and as the stages of production at which the inspection is carried out increase. With this growth will come the need to develop more or adapt current classification methods.

### Acknowledgment

The Authors would like to acknowledge the financial support of the Institute of Micromechanics and Photonics statutory grant.
# Chapter 3: Miniaturization and handheld units

## 24. Miniature spectrometers, multispectral sensors and the food chain

Richard A Crocombe¹ and Ellen V Miseo²

¹Crocombe Spectroscopic Consulting, Greater Boston, MA, United States of America

²Consultant, Greater Boston, MA, United States of America

E-mail: racrocombe@gmail.com and Ellen.miseo@gmail.com

### Status

In discussing miniature spectrometers, especially in the NIR region, it is important to realize that there is a variety of designs for NIR spectrometers, used for different purposes [461]. Specifically, the main traditional NIR applications are in food, feed and agriculture [462], and many of these are **quantitative**, the result of many years accumulation of data, careful instrument and sampling optimization, and are used for transactional and quality applications. As a result, migrating those applications to a miniature or portable spectrometer are unlikely, though newer semi-quantitative applications can be developed. A second large application area is **qualitative**, especially material identification and related areas (detection of contamination or counterfeit materials, etc) [463]. Many of these applications fall into the category of ‘screening in the field’ and are therefore amenable to the use of portable spectrometers. The third major area is process monitoring and analysis [464], where many other considerations come into play, including ability to operate in hazardous areas, long term stability, insensitivity to vibration, temperature and humidity changes, fiber optic interfaces, etc.

Portable spectroscopic instruments [465], in general, do not have spectroscopic performance of lab instruments, but are optimized for use in the field by non-specialists. However, they are ruggedized, often come with built-in or cloud-based qualitative and new quantitative applications, and provide instant answers at the point of need. A wide variety of technologies are used, with newer generations of these instruments becoming ever smaller [466, 467]. There are performance trade-offs with size reduction (spectral range, spectral resolution and signal-to-noise), and so a judgment has to be made as to whether the performance is ‘good enough’ for a particular application (figure 36). Attention also has to be paid to the sampling interface [468] to ensure that quality spectra are obtained, for instance from a representative sample, with minimal stray light, etc. In many cases, these instruments can operate in ‘point-and-shoot’ mode [469], i.e. they do not require a sample to be handled or prepared in any way and can possibly be interrogated by optical spectroscopy inside a glass bottle or plastic envelope.

Further advances include continuing miniaturization, the use of smartphone interfaces [470], applications of data processing in the cloud, and newer photonic approaches. These approaches may enable the manufacture of significantly lower cost instruments, leading to their wider use and development of new application areas [471]. Two significant areas are the emergence of very low-cost multispectral sensors [472, 473] and planar geometry PICs (also known as silicon photonics) [474].

### Current and future challenges

Two challenges stand out: the technologies to miniaturize and cost-reduce spectrometers further (described below), and the development of **applications** to deliver actionable answers to the operator, and not just spectra (figure 37).

Identification or classification (i.e. what group does this belong to) of a material is qualitative analysis [475]. The spectra of pure materials, or those which are identified by a brand name (and likely to be homogeneous), are compared with spectra in a database (a spectral library). The library should include spectra of all the substances that might be found in that environment, and not just the ideal analytes. The main application effort involves acquisition of well-characterized, genuine, samples, running their spectra, and building a library or calibration. For a specific application, a careful choice of instrument may be required, depending on the system under investigation.

Determining the concentrations of components of mixtures is quantitative analysis. However, optical spectroscopy, while rapid and convenient, is a secondary or indirect technique. Models have to be built based on samples that have been analyzed by a primary or reference technique [476]. These established methods, can be time-consuming and expensive. Those results, and their spectra are combined in a chemometric method. Validation samples, similarly analyzed, need to confirm that the model works appropriately, and is not using ‘accidental’ correlations.
<figure><img src="image_40.png" /><figcaption>**Figure 36.** The trend for ever-smaller spectrometers, and their possible application areas.</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>**Figure 37.** Miniature spectroscopic devices may be used for qualitative, detection and quantitative applications. The complexity of the analysis, and its required precision and accuracy, increase from qualitative to quantitative applications, and so does the risk to the operator and consumer if an erroneous result is given.</figcaption></figure>

Many of the new miniature spectroscopic technologies come from groups who are not analytical spectroscopists, and are not aware of these challenges, which has led to some exaggerated claims being made. One concern is that some of the new vendors propose to use ‘crowd-sourced’ data. This raises two questions: the quality of the spectroscopic measurement, and the accuracy of the identification, and/or quantitative information, usually referred to as ‘metadata. In addition, improper presentation of the specimen to the spectrometer can lead to stray light errors, invalidating quantitative methods. Finally, small instruments typically interrogate small specimen areas, which is problematic for heterogeneous samples [468]. There is real danger of ‘garbage-in, garbage-out’, with these approaches. A second possible concern is with some vendors where there also seems to be a lack of understanding about detection limits of optical spectroscopy in the condensed phase. This leads to claims of detecting trace amounts of allergens, pathogens, pesticide or
herbicide residues (figure 37). It is generally accepted that with condensed-phase optical spectroscopy, detection limits are in the low percent range, absent any separation or enhancement techniques.

### Advances in science and technology to meet challenges

The latest development in portable spectroscopy is the availability of very low-cost multispectral sensors, the size of computer chips [477]. The width of absorption bands in the VNIR regions for condensed phase samples imply that an instrument with a small number of resolution elements will be able to perform routine analyses, and that spectral range is a more important parameter than spectral resolution. Multispectral devices can be produced in volume via semiconductor and optical coating techniques, at very low cost—less than $10 each. Here we are defining a multispectral sensor as a device observing a small number of spectral bands (around 8–16), at broad resolution (around 20–50 nm). An example of a low cost (disposable) device in the agricultural area is a multispectral sensor designed to be inserted into an immature bunch of grapes, growing on the vine, to monitor their maturity [478].

The ideal next step would be for all the photonic components, including the electrical contacts required to drive them, to be integrated onto a wafer, and produced *en masse* using semiconductor manufacturing techniques. This is the promise of silicon photonics and PICs, and research in this area has been ongoing for more than twenty years, with roadmaps laid out for the future. The vision is similar to integrated circuits replacing discrete transistors and other electronic components, enabling mass production of highly integrated chips. A key point here is that conventional spectrometer designs (dispersive, Hadamard, FT) result in approximately cubic form factors; much of the research and development coming from the electrical engineering and semiconductor communities is focused on silicon photonic planar structures, which could enable facile integration with small consumer products like smartphones and smart watches.

The economic driver for much of this activity is the field of consumer 'wearables': health monitors, rings and patches. A typical ring contains multiple sensors: red and green LEDs, thermal coefficient body temperature, a 3D accelerometer, and a gyroscope. The annual unit volumes for these consumer devices are potentially in the hundreds of millions, with revenues around $30 billion, which has driven down the cost of photonic devices (such as vertical-cavity surface-emitting lasers—VCSELs—for facial recognition) dramatically. The same could happen soon for visible-NIR multispectral or Raman-based sensors.

### Concluding remarks

With further miniaturization of optical spectrometers, and their concomitant cost reduction, a number of natural products and food applications become commercially feasible. A large demand for cannabis analysis has emerged following the legalization of cannabis in Canada (among other places), and of hemp in the USA. As noted above, pesticide and herbicide residue analysis require a laboratory technique, but with appropriate calibrations in place, routine analysis of bulk components can be done with a portable optical technique.

New instruments and applications in food, feed and agriculture are appearing, given the increasing emphasis on PA. For instance, a portable NIR spectrometer, with an integrating sphere sample interface, can analyze protein, moisture, carbohydrates and oil content from crops [479], and a soil nutrient scanner [480] is also commercially available. In both the USA and Europe there have been initiatives for food assurance, using broadband visible-NIR spectroscopy, combined with the necessary reference analyses, to categorize produce for quality. One approach is to provide customers with information about the quality of produce in the store, and working with grocery retailers to improve that quality. Detection of food hazards, spoilage and food fraud is a second.
## 25. Ultra-miniaturized spectrometers and their agro-food applications

Andreas C Liapis, Faisal Ahmed and Zhipei Sun

QTF Centre of Excellence, Department of Electronics and Nanoengineering, Aalto University, Espoo, Finland

E-mail: andreas.liapis@aalto.fi, faisal.ahmed@aalto.fi and zhipei.sun@aalto.fi

### Status

In pursuit of PA, there is a great need for the development of optical technologies to monitor crop health and soil conditions. Optical spectroscopy is one of the most effective approaches for these purposes (e.g. to detect signs of fertilizer deficiency or excess well before visual symptoms appear on the plant). This can enable farmers to optimize the distribution of water and fertilizers, grow plants with resistance to diseases and pests, and increase the quantity and quality of their crops. Currently, such plant diagnosis is carried out by taking leaves off plants and measuring their spectral signatures in laboratory settings. For non-invasive on-site inspection, crucial requirements for the widespread adoption of these technologies, it is necessary to drastically decrease the size, weight, and cost of optical spectrometers. Recently, a new class of ultra-miniaturized reconstructive spectrometers has arisen that promises to address these issues.

Conventional spectrometers rely on bulk optical components to separate colors from one another for detection, and therefore cannot be miniaturized without sacrificing performance. The spectral resolution of grating spectrometers (figure 38(a)), which to date remain the gold standard for bench-top systems, generally speaking scales with linear dimensions. Spectrometers using a series of narrow-band spectral filters to isolate individual colors for detection are relatively smaller in size (figure 38(b)); their spectral resolution is determined by the linewidth of each filter, which can be deeply sub-nm even for miniaturized filters, but their operating bandwidth—that is, the number of filters—scales with the device footprint, and it is not practical to have more than a few tens of filters [481]. To further reduce the device footprint, a single tuneable filter can be used, but at the expense of measurement speed [482].

Reconstructive spectrometers overcome these trade-offs by not requiring that colors be completely separated from one another for detection. Instead, they are designed such that each input wavelength produces a complex but unique pattern in the output signal. For example, spectral information can be extracted from the complex way in which light scatters in a random medium [483], or from the patterns of light leaking from a multimode fiber taper [484–486]. These devices harness multimode interference to create wavelength-dependent spatial distributions, with interference regions of just a few hundred microns in length being sufficient to obtain pm-level spectral resolution. However, external optics and detectors are required to read the spatial patterns, hampering the portability of these spectrometers.

‘Ultra-miniaturized’ spectrometers combine the spectral sensitivity and the photodetection in a single component element [487], for example by modulating the spectral response of an electrical junction (figure 38(c)) [466]. If the responsivity of the device is known, the spectral content of unknown input light can be reconstructed from the variations in photocurrent as the applied electrical voltage is swept. Nanometer-level spectral resolutions are possible even though the total footprint of the device is on the order of a few tens of micrometers. Bringing such ultra-miniaturized spectrometers to the field would be of enormous benefit for agriculture and the food chain.

Figure 38 shows a comparison of the working principles of conventional spectrometers (i.e. a grating spectrometer and a filter-based spectrometer) with that of an ultra-miniaturized reconstructive spectrometer.

### Current and future challenges

The working principle of ultra-miniaturized reconstructive spectrometers relies on the ability to tune the wavelength dependence of the responsivity of a photodetector. Broadly speaking, we can separate these devices into two categories (i.e. bandgap and interface modulation, table 2), based on the tuning method used. Some implementations tune the bandgap of a single active material (e.g. via the Stark effect [488], or by varying the composition of compound semiconductors [489, 490], or through the application of strain [491]) while others electrically modulate the energy barrier height in a junction between two materials (such as between $MoS_2$ and $WSe_2$ [492], $MoS_2$ and Black Phosphorous [493], or $ReS_2$ and $WSe_2$ [494]). After characterizing the response of the device to a series of known spectra (typically, monochromatic illumination is used), it is possible to infer the frequency content of unknown spectra by analysing the device’s output using a computational algorithm [495]. With a sufficiently dense training dataset, and if the modulation is strong enough (meaning if there is enough change in the responsivity of the device as a function of wavelength as the applied voltage is varied) performance comparable to table-top spectrometers is possible,
<figure><img src="image_42.png" /><figcaption>**Table 2.** Performance comparison of ultra-miniaturized reconstructive spectrometers reported in literature.</figcaption></figure>

<table><thead><tr><th>Working principle</th><th>Material</th><th>Spectral range (nm)</th><th>Spectral resolution</th><th>Size</th><th>References</th></tr></thead><tbody><tr><td rowspan="2">Conventional</td><td>Table-top grating spectrometer</td><td>500–1000 VIS</td><td>&lt;0.1 nm</td><td>600 × 350 mm<sup>2</sup></td><td rowspan="2">Typical values from references of [466]</td></tr><tr><td>Portable grating spectrometer</td><td>200–1050 VIS</td><td>0.5–12 nm</td><td>90 x 60 mm<sup>2</sup></td></tr><tr><td rowspan="6">Bandgap modulation</td><td>Nanowire (Silicon)</td><td>450–800 VIS</td><td>6 nm</td><td>2 × 2 mm<sup>2</sup></td><td>[487]</td></tr><tr><td>Nanowire (CdS<sub>x</sub>Se<sub>1-x</sub>)</td><td>500–630 VIS</td><td>10 nm</td><td>75 × 0.5 µm<sup>2</sup></td><td>[489]</td></tr><tr><td>Black phosphorus</td><td>2000–9000 MIR</td><td>40 nm</td><td>16 × 9 µm<sup>2</sup></td><td>[488]</td></tr><tr><td>MoS<sub>2</sub> (Strain)</td><td>400–860 VIS</td><td>1.2 nm</td><td>20 × 25 µm<sup>2</sup></td><td>[491]</td></tr><tr><td>III-V semiconductor</td><td>480–820 VIS</td><td>10 nm</td><td>20–500 µm Ø</td><td>[496]</td></tr><tr><td>Perovskites (KMAPbCl<sub>x</sub>Be<sub>3-x</sub>)</td><td>450–780 VIS</td><td>80 nm</td><td>5.6 × 5.6 µm<sup>2</sup></td><td>[490]</td></tr><tr><td rowspan="3">Interface modulation</td><td>MoS<sub>2</sub>/WSe<sub>2</sub></td><td>405–845 VIS</td><td>3 nm</td><td>22 × 8 µm<sup>2</sup></td><td>[492]</td></tr><tr><td>MoS<sub>2</sub>/Black Phosphorous</td><td>500–1600 VIS/NIR</td><td>2 nm</td><td>30 × 20 µm<sup>2</sup></td><td>[493]</td></tr><tr><td>ReS<sub>2</sub>/Au/WSe<sub>2</sub></td><td>1150–1470 NIR</td><td>20 nm</td><td>6 × 4 µm<sup>2</sup></td><td>[494]</td></tr></tbody></table>

even though the reported active footprint of these nanomaterials-based reconstructive spectrometers is of the order of mm² or less.

Although ultra-miniaturized reconstructive spectrometers have been demonstrated to work well in a laboratory setting, there are fundamental challenges that need to be addressed before they can be deployed to the field. First, since their performance is primarily based on bandgap or interfacial barrier modulation, they normally function over a limited spectral bandwidth (table 2). Second, the nanoscale materials used in their construction are typically grown at laboratory scale, and most of them are not yet developed industrially. Thus, the industrial adoption and mass-manufacturability of these ultra-miniaturized spectrometers is a serious challenge. Third, some of the nanoscale materials are unstable in ambient environments, which leads to significant device-to-device variation and calibration issues. For the reconstruction algorithm to work, it is important that the spectral responsivity remain constant between the training and measurement steps. Therefore, the stability and robustness in on-field operating conditions with changing temperature and humidity are a critical issue.

### Advances in science and technology to meet challenges

There has already been some progress to mitigate these challenges at the laboratory and industrial level. While improvements in materials processing technology have enabled some nanoscale materials like graphene to be grown at large scale by chemical vapor deposition—in fact, wafer-scale growth of graphene is nowadays offered as a foundry service—the fabrication of 2D material heterostructures remains a laborious
manual task. Recently, there has been some progress in developing semi-automatic systems to assemble van der Waals heterostructures [497] but not at industrial levels. Therefore, there is great interest in realizing ultra-miniaturized spectrometers based on scalable materials such as silicon, compound semiconductors, and organic materials, which can reduce the lab-to-fab duration for such technologies. A III-V semiconductor-based reconstructive spectrometer was recently reported [496], proving a promising approach for massively parallelized fabrication. A fully-CMOS-compatible design would enable us to leverage the mature silicon electronics manufacturing and design ecosystem and manufacture such spectrometers at industrial scale.

The stability of these devices could be improved with adequate surface passivation. For example, alumina encapsulation by atomic layer deposition has been shown to improve the stability of perovskites in aqueous environments [498]. Alternatively, ultra-transparent glass encapsulation can be used to shield the device from the environment without loss of performance [499]. Additionally, routine on-site calibration against a reference sample can be employed to realize stable device performance.

Significant research effort is also being put towards the improvement of the reconstruction algorithms, with an emphasis on robustness to noise. As the amount of noise that can be tolerated by the algorithm increases, the required signal-to-noise ratio decreases, thereby allowing for faster data acquisition with shorter integration times. Currently, most implementations of reconstructive spectrometers use regularization/minimization algorithms. Generally, for reconstruction algorithms, a large dataset is needed for learning purpose to establish relationships between the measurements and the reconstructed algorithm (sampling). This makes the calibration process slow. However, adapting deep learning and neural network models can mitigate this issue to some extent. There is some indication that ML models can outperform regularization in the presence of noise, but more research on this field is necessary. This issue requires interdisciplinary and collaborative approach among researchers of different domains.

## Concluding remarks

Ultra-miniaturized reconstructive spectrometers are an active field of research that pushes the miniaturization of spectrometers to its limit. The initial laboratory results have shown great prospects in terms of performance, paving way for possible applications in PA. As can be seen in table 2, there is a great variety in material platforms among the reported devices, allowing researchers and farmers to choose the working bandwidth according to the requirements of their specific application, e.g. signatures of nitrogen deficiency can be detected in the fingerprint spectral region (7–14 µm). These spectrometers tend to be lightweight, and therefore are the ideal choice for integration with transport media (hand-held devices, drones, satellites etc), where they are expected to offer significantly reduced payload compared to conventional spectrometers for practical on-site or remote inspection. For many of the designs listed in table 2, voltages <5 V are required for operation, and with modest power requirements, it should be possible to drive these devices with on-board electronics. If CMOS compatible designs are developed, the readout circuitry can be directly built in the sensor. Furthermore, arrays of such spectrometers could be used to implement HSI in a compact form factor.

## Acknowledgments

This work was supported by Business Finland (R2B A-gate), Academy of Finland (314810, 333982, 336144, 352780, 352930, 353364, 359009, 365686 and 360411), the Academy of Finland Flagship Programme (320167, PREIN), the EU H2020-MSCA-RISE-872049 (IPN-Bio), the Jane and Aatos Erkko foundation and the Technology Industries of Finland centennial foundation (Future Makers 2022), and ERC (834742).
## 26. Portable biophotonic systems for agro-food analysis

Chrysoula Chandrinou, Marianneza Chatzipetrou and Ioanna Zergioti

School of Mathematical and Physical Sciences, Physics Department, National Technical University of Athens, Heraon Polytehneiou 9, 157 72, Athens, Greece

E-mail: cchandr@mail.ntua.gr, mchatzip@mail.ntua.gr and zergioti@central.ntua.gr

### Status

Currently, the majority of tools for food quality control during the collection and processing of raw materials, as well as during the production of food products are still based on heavy analytical methods which provide a strict quality control but are complex, expensive, time-consuming with time-to-result up to several days, and applicable in laboratory environments.

Chemical analysis techniques such as chromatography (e.g. gas chromatography, LC) [500] are widely used for detecting contaminants, pesticide residues, and food additives. Those techniques require expensive equipment and are reliant on timely procedures which involve highly skilled personnel [501]. For detecting foodborne pathogens, culturing and plating assays are considered the gold standard [502]. However, these methods are also considered demanding and slow procedures, often taking several hours to days to yield results. ELISA method is able to address this challenge as offers a much faster detection [503], however it is less specific compared to other methods and involves multiple processing steps. Similarly, PCR method, also required multiple steps, including nucleic acid extraction, amplification, and detection [503].

Conversely, biosensor devices, both for traditional as well as PA [504], are analytical devices that can provide quantitative analysis, thus covering a large part of the distance between the functionality of the analytical methods and that of low-cost screening toolkits. Additionally, biosensors can also be made portable, designed to be more user-friendly, disposable, and manufactured at a reduced expense. Up to date, different biosensor platforms have been the focus of interest including fluorescence-based [505], electrochemical [506] and surface acoustic waves-based [507]. However, and despite their individual strengths, these platforms still have individual limitations such as need for labeling [508], need for utilization of bulky setups, in many cases involve time-consuming multistep detection protocols while also present low signal-to-noise ratio. A novel category of biosensors is made possible by controlling the interaction between light and matter. Photonic biosensors can be collectively fabricated, making the manufacturing process more efficient and cost-effective, enable the simultaneous detection of multiple analytes, present high sensitivity while also are less susceptible to electromagnetic interference or noise, making them suitable for use in various environments, including those with electromagnetic interference. Furthermore, the optical biosensors enable the real-time and label-free detection, thus simplifying experimental procedures and reducing potential artifacts. Photonic biosensors base their operating principle either in the interaction of the escaping evanescent wave, as the light propagates on the waveguide, with the desired analyte, or by directly exploiting the optical properties of the analyte (spectroscopic biosensors). For the evanescent-based biosensors, figure 39 illustrates a representative example of a fully integrated Lab on Chip (LoC) device utilizing such sensors. Based on the operating principle, a bioreceptor molecule is immobilized on the surface of a waveguide and upon interaction with a bioanalyte, a variation of the signal is obtained at the output of the device. The evanescent wave has an interaction field with the analyte in the nm scale, thus it is protected from occurrences within the main body of the sample [509] and as a result, photonic biosensors exhibit high sensitivity [510]. In the field of food quality monitoring photonic biosensors have already been used for the detection of pathogens and pesticides drugs and chemicals in food samples [511] or water [512].

### Current and future challenges

Several challenges must be addressed so as to exploit the full potential of such sensors. The **sensitivity of the sensors mostly** depends on the choice of biorecognition elements. Antibodies, are the most common elements, having demonstrated their ability for high sensitivity (low detection limits) in various types of biosensors [514]. **Specificity** issues that all types of label-free biosensors still face today are related to their biochemical part. It is common to have a system capable of detecting an analyte with high sensitivity, but at the same time not being able to distinguish between this analyte and closely related biomolecules, mainly due to the fouling effects from these other molecules that are present in real-life complex matrices [515].

**Compactness:** despite the impressive progress on photonic integration on PICs devices [516], the compactness of the photonic part of the sensors is still limited, as in most cases the optical sources and the detection elements are placed off-chip and are connected with the photonic chip through external optical couplers. **Sample handling:** the PIC biosensor devices are integrated onto a final LoC device with the use of
<figure><img src="image_43.png" /><figcaption>**Figure 39.** Top: agrofood chain from farm to fork. Bottom: an example of a LoC device with integrated photonic sensor (A): BIOCDx photonic biosensor device with inserted cartridge, (A) 3D model, and (B) final instrument. Figure reproduced from [513]. CC BY 4.0.</figcaption></figure>

microfluidic cartridges that are able to perform the sampling, sample treatment, sample concentration and sample delivery onto the sensor surface [513]. There are several challenges associated with the use of such microfluidic chips that limit the advantages of the PIC biosensors. Although photonic biosensors are known for their fast response time, the use of such fluidic cartridges often lead to longer detection times [517]. Additionally, even though the use of microfluidic cartridges enables the development of a disposable sensor devices, often come with the need for bulky and expensive external microfluidic peripherals [509]. **Low cost:** the development of ultra low-cost, disposable devices has been the dominant model for the development and commercialization of all types of LoC platforms so far. This approach is used to avoid cross-contamination issues and complicated cleaning protocols associated with biological samples. However, the single-cartridge use approach often involves the use of biomaterials and chemical reagents related to the functionalization of the photonic sensors which, often increase the cost of the cartridges drastically [518]. There are also other challenges such as field testing and validation, regulatory compliance, standardization, market awareness and adoption that should be mentioned but are not discussed here.

### Advances in science and technology to meet challenges

Regarding the scientific and technological challenges described above, improvements have been and are being made, to address those issues. Concerning the **sensitivity** of the biorecognition elements, there has been an increased use of aptamers since their smaller size can achieve high sensitivity due to the higher density surface coverage with more available binding sites per surface area. As far as the **sensitivity** of the chip-integrated waveguide sensors in recent years, the waveguide sensitivity has been improved by combining the MRRs with slot-waveguides or subwavelength grating (SWG) waveguides. Relating to the **selectivity/specificity** of the sensing mechanism, through ongoing efforts in recent years, certain label-free optical transducers have made significant strides, allowing them to accomplish the detection in complex matrices [519] of individual molecules such as by the formation of a hydration layer with hydrophilic compounds [520] or the formation of an effective charge-balanced layer with zwitterionic compounds [521]. Also, polymeric flow-through microfluidics can be used for improved response time and selectivity [522]. Concerning **compactness**, significant effort was made to alter the integration method by incorporating both active and passive optoelectronic components, along with the electronics layer, onto a single sensor chip. Moreover, the evolution in optical biosensor innovation is characterized by the incorporation of nanophotonic biosensors [510], which can achieve significantly enhanced miniaturization, portability, and throughput. Another significant challenge, is the chip packaging with microfluidics and electronic readout connections. A novel integration approach to overcome this issue was introduced in 2020 [523] whereby a local backside-release process to separate the electronics and optics from the microfluidics has been developed. In addition, an approach utilizing integrated digital microfluidics [524] can further increase the portability of the system. Regarding **low cost and reusability**, nowadays, other biorecognition elements are
being used such as aptamers especially DNA-based ones, which offer the possibility to fabricate biosensors that are reusable over many cycles. Furthermore, **low-cost** methods manufacturing microfluidic chips via digital light processing (DLP) or grayscale DLP can further reduce overall system cost while also there is a tendency to introduce smartphones in the novel biosensor devices, thus exploiting the light sources, cameras, image processing and communication capabilities [525].

**Concluding remarks**

The need for effective screening tools that will be fast, low-cost, sensitive, truly reliable and portable for use at food production sites and processing units, is here and is urgent. However, as with any new approach there are several challenges that need to be addressed. Overcoming these challenges requires a multidisciplinary approach involving scientists, engineers, policymakers, and industry stakeholders. Continuous innovation and investment in biosensor technology will play a significant role in addressing the unique challenges of the agri-food industry and ensuring a safer and more sustainable food supply.

**Acknowledgment**

This project has received funding from the European Union's Horizon Europe Framework Programme research and innovation action under Grant Agreement No. '101093150'.
## 27. Lab-on-a-chip (LoC) systems for agriculture

Ulrich Trog, Anja Haase and Martin Smolka

JOANNEUM RESEARCH Forschungsgesellschaft mbH, Leonhardstrasse 59, 8010 Graz, Austria

E-mail: ulrich.trog@joanneum.at, anja.haase@joanneum.at and martin.smolka@joanneum.at

### Status

Growing awareness for environmental protection and sustainability, coupled with limited land and energy resources and the impact of climate change, are placing increasing demands on agriculture and food production. To meet these challenges successfully and to address the trade-offs between sustainability, productivity and food/feed quality, optimized processes and new technologies are essential [526, 527].

The use of analytical technologies in agricultural processes as an enabler for precision farming offers many opportunities [527]. Analytical technologies are particularly relevant for

* Monitoring the agricultural and aquacultural environment to ensure optimal conditions for growing plants or animals (e.g. soil moisture, pH level, nutrient content) [528].

* Monitoring the quality or safety of food and feed (e.g. detection of pests like pathogens and micro-organisms).

**Current analytical technologies** use electrochemical, optical, colorimetric, and biological methods. As these techniques are typically applied in specialized laboratories, they have the following **disadvantages** [529, 530]:

* initial costs are high

* the equipment requires space and lab infrastructure

* trained professionals are needed for operation

* tests cannot be performed on site and samples need to be transported to the laboratory. Analysis is therefore time-consuming and action cannot be taken immediately.

For time urgent and precise decision making new sensing technologies are needed:

**Microfluidic LoC systems** typically consist of a readout device operated with exchangeable analytic LoCs (figure 40). They integrate multiple functions for characterization of liquid samples onto a single platform, enabling faster analytical testing with precision comparable to specialized labs. These systems offer several **advantages**:

* portability

* cost effectiveness

* the ability to provide real-time data

* enable farmers to make informed decisions at the point of need.

One prominent application is soil health monitoring. Additionally to traditional methods of measuring soil pH and moisture levels, LoC systems can e.g. analyze nutrient content accurately, allowing farmers to tailor their fertilization and irrigation practices, thereby optimizing crop yields while reducing resource wastage [531].

Water quality assessment is another vital area where LoC systems can make a difference. They enable the analysis of water sources for contaminants such as heavy metals, pesticides, and pathogens. This is crucial for ensuring crop safety and preventing environmental pollution [527].

Moreover, LoC systems can be employed for rapid pest and disease detection. By analysing plant samples, these systems can identify pathogens or pests quickly, enabling farmers to take timely action, such as targeted pesticide application or crop removal, to prevent further spread [532].

### Current and Future challenges

However, there are also challenges in using LoC technology in agriculture (table 3):

**Fabrication and cost of LoCs:** LoCs require precise and complex fabrication techniques, such as microfluidics, nanotechnology, and biotechnology, which can be expensive and time-consuming. Moreover, the materials used for LoC systems should be biocompatible, durable, and scalable, which can limit the choice and availability of suitable materials. Therefore, there is a need to develop low-cost, simple, and robust fabrication methods and materials for LoCs that can be mass-produced and widely distributed [533].
<figure><img src="image_44.png" /><figcaption>**Figure 40.** Lab-on-a-Foil (®JOANNEUM RESEARCH).</figcaption></figure>

<table><caption>**Table 3.** Summary challenges and technology advances.</caption><thead><tr><td></td><td>Current and future challenges</td><td>Advances in Science & Technology</td></tr></thead><tbody><tr><td>Fabrication and cost</td><td>Mass production is expensive, time consuming and difficult to scale</td><td>Continuous roll-to-roll manufacturing</td></tr><tr><td>Biobased and biodegradable</td><td>Non-renewable resources used for single use components</td><td>Adoption of recycled or bio-derived materials</td></tr><tr><td>Integration and Automation</td><td>Complex sample preparation and analysis in a centralized lab</td><td>Microfluidic Lab-on-a-Chip (LoC) systems integrating all required functions at the Point of Need</td></tr><tr><td>Sensitivity and specificity</td><td>Detect and quantify low levels of target analytes, in complex and heterogeneous samples</td><td>Translating innovative biosensor approaches initially developed for human disease diagnostics</td></tr><tr><td>Standardization and regulation</td><td>Lab-on-a-Chip (LoC) systems should comply with relevant standards and regulations</td><td>Adoption and support from governmental regulations can drive implementation of LoC</td></tr><tr><td>Throughput</td><td>Increase throughput for more efficient and sustainable agricultural practices</td><td>Miniaturization of analytical processes, enabling (parallel) on-site testing without trained personnel.</td></tr></tbody></table>

**Biobased and biodegradable:** most LoC systems are currently single use, made from plastic materials derived from non-renewable sources and ending as medical/diagnostic waste in landfill. To move towards a circular economy, reduce the overall carbon footprint and commit to net zero, it is necessary to look at the entire product life cycle. This includes the development of new biodegradable materials, also from natural sources, taking into account their own environmental issues. More generally, the LoC field needs to address key challenges related to global sustainability goals, reducing single-use plastics in analysis and using low power, recyclable microsystems technologies [534].

**Integration and automation:** there is a continued need to move analytical tests away from specialized and centralized laboratories and into community settings to enable operators to make informed decisions at the point of need. Required are smart and adaptive LoC systems that can integrate and automate various functions in a chip/device [535].

**Sensitivity and specificity:** LoC systems should be able to detect and quantify low levels of target analytes, in complex and heterogeneous samples, such as food, water, soil, and biological fluids. This can be challenging due to the presence of interfering substances and the variability of environmental conditions. This results in the need for sensitive and specific LoC systems that can enhance the signal-to-noise ratio, reduce the
<figure><img src="image_45.png" /><figcaption>**Figure 41.** Roll-to-roll (R2R) pilotplant for UV nanoimprint lithography of microfluidic structures, microarray spotting of biomolecules and UV-lamination (©JOANNEUM RESEARCH).</figcaption></figure>

false-positive and false-negative rates, and increase the accuracy and precision of the detection and analysis [535].

**Standardization and regulation:** LoC systems should comply with the standards and regulations of the food, agriculture, and biosystems industries, which can vary depending on the country, region and application.

**Throughput:** improving the throughput of LoC systems is essential for enhancing their application in agriculture. Overcoming current constraints will enable faster diagnostics, large-scale monitoring, and better integration with automated systems, ultimately supporting more efficient, sustainable agricultural practices and timely decision-making in precision farming [536].

**Advances in science and technology to meet challenges**

Looking forward, the future of LoC technology in agriculture seems promising. Continued research and development efforts, coupled with advancements in nanotechnology and microfluidics, are expected to enhance the performance and reliability of these LoC systems.

**Fabrication and cost of LoCs:** Roll-to-roll (R2R) imprinting technology facilitates efficient, high-throughput fabrication of micro- and nano-structures over large areas. The process uses flexible polymer foils, allowing for subsequent modifications such as bio-functionalization, hole cutting, or chip lamination, all within the R2R manufacturing framework. In an R2R UV-nanoimprint lithography (UV-NIL) process, structures are replicated from a polymer, nickel, or steel master (referred to as a 'shim') onto an uncured photoresin layer applied to a polymer substrate (figure 41). The photoresin is cured under UV light while in contact with the shim, forming solid microfluidic structures on the foil. This process takes place at room temperature, under low pressure, and in ambient air conditions, enabling the creation of precise microfluidic features such as V-grooves or rectangular channels. The method holds considerable potential for scaling up production and reducing costs for LoC systems. Overall, R2R imprinting offers an industrially efficient solution by reducing manufacturing costs, resource usage, and energy consumption, while enhancing production throughput for micro- and nano-structured devices. [533, 537, 538].

**Biobased and biodegradable:** adopting recycled or bio-derived materials for single-use LoC components can reduce CO₂ emissions and plastic pollution. Material choice must consider manufacturing and disposal processes [539]. Despite slow progress, the potential for circularity is emerging, with over 130 European bioplastics projects conducted between 2007 and 2020 [540].

**Integration and automation:** LoC systems often involve multiple steps and components, such as pumps, valves, sensors, actuators, and microcontrollers, which are integrated on a single chip. This involves new designs for different assays and consideration of compatibility, reliability, and functionality of the LoC systems. Moreover, development of LoC systems includes automated and autonomous sample preparation [541, 542].

**Sensitivity and specificity:** there is great scope for translating innovative biosensor approaches, initially developed for human disease diagnostics, to the early detection of plant pathogens. Especially
electrochemical and optical biosensors may provide innovative solutions for plant pathogen detection and management in the future [532].

**Standardization and regulation:** increased adoption and support from agricultural institutions as well as governmental regulations can drive the implementation of LoC sensors for agriculture across a wider range of farming practices.

**Throughput:** LoC systems offer key opportunities in agriculture through miniaturization of analytical processes, enabling on-site testing without trained personnel. They also allow for parallelizing operations, increasing testing efficiency and throughput, making rapid diagnostics and monitoring more accessible for better decision-making and sustainability in agricultural practices.

**Concluding remarks**

Despite these advancements, challenges persist. LoC technology development needs to focus on improving sensitivity, specificity, and robustness, especially in complex agricultural matrices. Calibration and maintenance of these sensors also remain areas of concern, as they can affect data accuracy over time. Integration into existing farm management systems and the affordability of these technologies for small-scale farmers are other hurdles to overcome.

In conclusion, LoC technology has made significant strides in agriculture by offering efficient, cost-effective, and real-time analysis capabilities. While challenges remain, ongoing research and wider adoption have great potential to further improve the utility of this technology in transforming agriculture towards more sustainable and efficient practices.

**Acknowledgments**

PhotonHub Europe—One-Stop-Shop Open Access to Photonics Innovation Support for a Digital Europe (Grant Agreement ID: 101016665)

NextGenMicrofluidics—Next generation test bed for upscaling of microfluidic devices based on nano-enabled surfaces and membranes (Grant Agreement ID: 862092)
## 28. Opportunities for photonic integrated circuits in agriculture and food processing

Lex Oosterveld and Chris Van Hoof

OnePlanet Research Center and imec, Bronland 10, 6708 WH, Wageningen, The Netherlands

E-mail: Lex.Oosterveld@imec.nl and Chris.VanHoof@imec.be

### Status

Agriculture is one of the biggest industries worldwide with a market size of approximately 12 trillion dollars, accounting for ca 25% of global employment in 2018 [543]. As a result, the demand for resources for agriculture is enormous and is not sustainable in the long term. For instance, agriculture irrigation accounts for 70% of water use worldwide [544] and nitrogen deposition in agricultural areas resulted in a decrease in biodiversity. There is need for more sustainable agricultural production.

'Smart Agriculture' refers to managing farms using modern ITCs to increase the quantity and quality of products while making optimum use of resources and minimizing the environmental impact. It can be regarded as a toolbox of sensing and data technologies including use of sensors, robotics and drones, AI, big data, IoT, and satellites [526]. Photonics based sensing is already widespread in agrifood applications [9]. As technology improves there is a trend towards proximal plant sensing, more autonomous processes, measurement of specific nutrients inside the plant, and further miniaturization of sensing devices.

Process control in the food processing industry is often based on temperature, pH, and pressure as a function of time. Computer vision is commonly used for real-time inspection and grading of fruit, vegetables, and grain ingredients. However, information on the product composition during the process is mainly obtained by time consuming laboratory analyses, and as a result real-time process control based on product composition is not possible. Fast analysis of the ingredient composition is required to replace laboratory analysis. There is a potential for the application of photonic-based spectroscopic sensors that can provide information on product composition to reduce costs related to ingredient storage and real-time process control.

Food producers are legally required to ensure the food safety of their products [545]. In addition, food safety issues can have large impact on a brand's reputations. Food safety includes microbiological, physical, and chemical safety. Physical food safety is related to the presence of foreign matter which represent a hazard upon consumption (e.g. glass, metal, hairs). Microbial food safety relates to presence of the foodborne pathogens that can lead to illness and mortality. Chemical food safety includes e.g. contaminants from the environment and residues of veterinary medicinal products, antibiotics, pesticides, and herbicides.

Agricultural production and food processing require reliable and cost-effective sensors to measure product quality and composition in real-time to replace time-consuming laboratory analysis and to make more optimal use of resources.

### Current and future challenges

PICs have great potential in the field of agriculture, as they have the potential to provide small, low-cost, and reliable sensing systems with low power consumption, which fits well with the trends in agriculture (figure 42). Relevant parameters for growth control of crops are growth monitoring, water uptake, transport and transpiration monitoring, and metabolite monitoring, in leaves, stems, and fruit.

In the Netherlands, the government aims to halve nitrogen emissions by 2030 because of the implementation of EU rules to reduce loss of biodiversity. To limit the impact of agrifood production on the environment, it is important to understand the factors that contribute to the decrease of the environmental quality. Fine grained measurement of nitrogen emissions in the environment and in stables requires low-cost, robust, and sensitive gas sensors.

For food processing, promising applications for PIC-based sensors are assessment of ingredient quality, real-time process control, measurement of product properties of the final food product, as well as controlling of food safety along the production process.

However, to develop PIC-based sensors for these different applications, it is necessary to understand the requirements of each application. This includes the specific wavelength ranges, the required frequencies and sensitivities, and application specific requirements, including sample handling and resistance to external factors (temperature, vibration, humidity, etc.). And it is important that specific application requirements are met, but also that the sensors are widely applicable, to maximize market volumes.

For production of PICs-based sensors various building blocks are required. For low-cost spectrophotometric sensors on chip tuneable lasers are needed, with a wavelength tuning range that matches with the application, so that detection can be performed with a photodetector instead of a more costly
<figure><img src="image_46.png" /><figcaption>**Figure 42.** On-chip Raman spectroscope developed by imec [545, 546] (top). Silicon photonics wafer technology providing miniaturization and scalability [545, 546] (bottom).</figcaption></figure>

spectrometer [547]. Possible applications are measurement of moisture, lactose, protein, and fat by NIR or MIR spectroscopy [548, 549]. Also, a tuneable single mode laser is suitable to probe the absorption lines of gases in the case of gas sensing. An example is tuneable diode laser absorption spectroscopy (TDLAS) [550]. A wider tuning range makes the sensor more versatile, so that it may be used in other applications. Accurate and highly sensitive detection requires development of photodetectors, detectors for THz, and microphones for photo acoustics applications. For accurate measurements, for instance for gas sensing in the ppb range, longer wavelengths are needed, compare to the traditional wavelengths used for telecom (e.g. C-band = 1530–1565 nm).

Application requirements have a high impact on the optical system design requirements. Analysis of samples that require direct interaction between sample and optical sensor surface, requires physical, mechanical, or chemical cleaning of the optical sensor surface. In case the optical designs become smaller, the possibility to clean these optical windows becomes more limited. Low cost integrated disposable optical sensor heads that can be separated from the optical analysis and electronics may be solution for some cases. For other cases proximal optical sensing may be a better alternative. Other applications require highly integrated and small sensors, e.g. a leaf clip to monitor moisture. However, integrated lasers and light sources are the common denominator for most of the sensing applications.

### Advances in science and technology to meet challenges

A limited number of sensing platforms based on PICs, including Vis-NIR spectroscopy, RS, photoacoustic spectroscopy, laser speckle imaging, LiDAR, THz spectroscopy, and biosensing, are suitable to support many of the agrifood applications (table 4). Sensing platforms based on integrated photonics have already been demonstrated, for e.g. RS [546], NIRS [10*], open path gas sensing [547], and photoacoustic spectroscopy [551]. For sensing of emission gasses, specific on-chip lasers are required for each of the relevant gasses, e.g. ammonia and methane, or alternatively tuneable lasers to measure multiple gases using a single sensor. Various materials are available for laser production, each with their own wavelength range, e.g. GaAs, InP, GaN, Si, InAs, and GaSb [552]. Of specific interest is extending the wavelength range of tuneable lasers from the near infrared to the mid infrared region to enable fingerprinting based on vibrations of molecular bonds [553]. Production of tuneable lasers in a cost-effective way requires new production processes, including micro transfer printing [554] and flip chip technology, which are currently being developed.

Accurate and highly sensitive detection requires further development of e.g. resonant cavity enhanced photodetectors [560], waveguide integrated antenna-coupled diodes for THz [561], and low-cost sensitive microphones for photo acoustics applications.

For sensing mostly waveguides in a straight or spiral configuration (longer interaction path with analytes) are used. In the context of RI sensing more complex structures are available (e.g. ring resonators, Mach-Zehnder interferometers, photonic crystals) [562]. For biosensors multiplex capability is relevant
<table><caption>**Table 4.** Overview of agrifood applications and suitable photonic modules.</caption><thead><tr><th>Property</th><th>Photonic modules</th></tr></thead><tbody><tr><td colspan="2"><strong>Plants &amp; fruits</strong></td></tr><tr><td>Plant morphology</td><td>Lidar [175], 3D Imaging</td></tr><tr><td>Plant moisture</td><td>THz [555] (100 GHz–2 THz)</td></tr><tr><td>Sap flow</td><td>Laser Speckle [556]</td></tr><tr><td>Plant nutrients</td><td>NIRS [548] (800–2400 nm)</td></tr><tr><td>Fruit ripeness</td><td>NIRS [548] (800–2400 nm)</td></tr><tr><td>Pesticides</td><td>Raman [557] (SERS; 350–1700 nm)</td></tr><tr><td>Storage conditions</td><td>Gas sensing [558], laser absorption spectroscopy, photoacoustic spectroscopy</td></tr><tr><td>Controlled ripening</td><td>Gas sensing [559], laser absorption spectroscopy, photoacoustic spectroscopy</td></tr><tr><td colspan="2"><strong>Livestock</strong></td></tr><tr><td>Feed &amp; water quality</td><td>NIRS [548] (800–2400 nm)</td></tr><tr><td>Emission gases</td><td>Gas sensing, laser absorption spectroscopy, photoacoustic spectroscopy [551]</td></tr><tr><td colspan="2"><strong>Food processing</strong></td></tr><tr><td>Composition</td><td>NIRS [548] (800–2400 nm), MIRS (2400–11 000 nm), Raman (875 nm laser, 300–1800 cm<sup>-1</sup>)</td></tr><tr><td>Flavor</td><td>Gas sensing [558], laser absorption spectroscopy, photoacoustic spectroscopy</td></tr><tr><td>Microbiological safety</td><td>Fluorescence, biosensing</td></tr><tr><td>Physical food safety</td><td>THz imaging [555] (0.1–4.0 THz)</td></tr><tr><td>Chemical food safety</td><td>NIRS (800–2400 nm), MIRS (2400–11 000 nm), Raman [557] (875 nm laser, 300–1800 cm<sup>-1</sup>)</td></tr></tbody></table>

[563]. To connect various photonic structures in a PIC edge couplers and gratings are required to enable light input/output [564].

To increase specificity, waveguide coatings are essential for the sensor fabrication process. For spectroscopic sensing, metal-organic frameworks or sorbent polymers are available to concentrate target analytes close to the waveguide. For biological and refractive index-based sensing, e.g. antibodies may be attached to waveguides to capture target biomolecules with high specificity [175].

Most examples from scientific literature demonstrate the initial feasibility of PIC based sensors. However, the challenge is to increase the TRL level of the sensors and to develop the manufacturing processes. The photonic sensors market is at a relatively early stage and current manufacturing is focused on telecom wavelengths. Extension of photonic structures to other waveguiding materials to enable sensing at wavelengths beyond the telecom domain is a key goal. Photonic structures of PICs require very high design fidelity, and it is essential to limit manufacturing variability.

## Concluding remarks

In conclusion, the markets for sensors are rapidly growing, and Photonics Integrated Circuits (PICs) have great potential in the fields of agriculture and food production, as they have the potential to provide small, low-cost, and reliable sensing systems with low power consumption. Together with availability of low-cost PIC based sensors, the wide availability of cloud computing, and the use of AI to automatically generate models from sensor data, will pave the way to broad application of PICs in the agrifood sector. However, the photonic sensors market is at a relatively early stage, and investments in manufacturing capability are needed to achieve the advances detailed in this chapter. Also, the diversity of agrifood production processes and the big range of sensor devices require a clear prioritization based on economic and societal benefits.

## Acknowledgment

This research received funding from the Netherlands National Growth Fund “PhotonDelta”.
# Chapter 4: Optimized lighting

## 29. Wavelength-stabilized and tunable diode laser-based light sources for sensing applications in the agro-food sector

Bernd Sumpf and André Müller

Ferdinand-Braun-Institut (FBH), Leibniz-Institut für Höchstfrequenztechnik, Gustav-Kirchhoff-Str. 4, 12489 Berlin, Germany

E-mail: bernd.sumpf@fbh-berlin.de and andre.mueller@fbh-berlin.de

### Status

In addition to customized detection systems, light sources with target-specific requirements are key components in recent optical sensing techniques in agro-food applications. The covered spectral range extends from the UV (100–380 nm) through the visible (VIS: 380–780 nm) and infrared (IR: 780 nm–100 µm), up to the terahertz region (THz: 100 µm–1 mm).

Target spectral regions can vary for different techniques. For example, fluorescence probing electronic transitions requires excitation wavelengths in the UV-VIS. Techniques like lidar and RS based on scattering use light sources from the UV to the near IR range. Absorption spectroscopy where characteristic lines occur in the whole optical spectral range can apply light sources with various excitation wavelengths.

Key specifications of the light sources include emission wavelength, spectral width, spectral tuneability or spectral switching, optical output power, and beam quality. With respect to portable instruments, compact size and low power consumption are additional important factors. Several techniques require continuous wave (cw) operation whereas other applications utilize pulsed excitation.

These requirements can be addressed by compact, reliable, and robust diode laser-based light sources. In various sensor applications, moderate optical output powers up to a few hundreds of milliwatts together with target-specific emission linewidths and spectral tuneability are needed. Here, devices with implemented internal gratings, either realized as distributed feedback (DFB) [559] lasers or as distributed Bragg reflector (DBR) lasers provide the necessary spectral capabilities. The combination of several devices emitting at different wavelengths within one laser chip either as laser bar with several output apertures or combining the light into one common output spot using Y-branch or multi-mode interference (MMI) couplers allows fast wavelength switching. Adjusting the current through DFB lasers or implemented heater elements close to the gratings of DBR lasers enables fast spectral tuning without changing the heat sink temperature. Higher output powers can be achieved by using e.g. flared sections integrated monolithically into tapered lasers (TPL) or as separate tapered amplifiers (TPA) in hybrid master oscillator power amplifiers (MOPAs).

Selected III-V compound semiconductor materials form the basis to manufacture such devices addressing the UV-VIS spectral range (GaN, 3xx-5xx nm), the VIS-NIR spectral range (GaAs, 6xx-11xx nm), or the NIR spectral range (InP, 13xx-20xx nm). Additionally, the application of second harmonic generation (SHG) or sum frequency generation (SFG) can fill gaps in the spectral coverage.

This contribution will focus on internally wavelength-stabilized, spectrally tunable, or spectrally switchable diode lasers for cw or pulsed sensing applications in the VIS and NIR.

### Current and future challenges

RS is a well-established analytical tool but suffers from the weakness of the Raman signals in comparison to strong disturbing backgrounds especially in real-world applications (e.g. fluorescence, daylight). A physical approach to address this issue is changing the excitation wavelength by an amount comparable to the spectral width of the Raman signals of the target under study. While the Raman signals follow the change in the excitation wavelength, background signals remain spectrally unchanged, thus enabling an effective separation. In a concept with two wavelengths (SERDS) [565], the Raman signals are obtained simply by subtraction of both measured spectra followed by integration and baseline corrections. Techniques with up to eight different emission wavelengths (e.g. sequentially shifted excitation) [566, 567] require more complex data treatment.

Here, light sources with typical emission widths of about 10 cm⁻¹, output powers in the 100 mW range, and a spectral distance between excitation wavelengths of about 10 cm⁻¹ (e.g. 0.6 nm at 785 nm) are requested. One potential but rather slow way is to change the overall device temperature. Shorter switching times can be realized by implementing heater elements close to the wavelength-stabilizing gratings, applying a direct current through the grating section, or the implementation of multi-resonator devices.
In absorption spectroscopy under atmospheric conditions, e.g. using individual rotational-vibrational lines for the determination of gas concentrations, the emission linewidth should be smaller compared to RS. Assuming a full scan of an absorption line, e.g. of water vapor at 940 nm with a linewidth of 0.2 cm⁻¹ (20 pm), the emission width should be at least one order of magnitude smaller. Less demanding and fast while still providing sufficient target information would be a dual-wavelength approach and wavelength switching between a wavelength in resonance with the absorption line $|_{on}$ and a second off-resonance wavelength $|_{off}$. Here, an emission width close to the linewidth would be acceptable.

To illuminate larger areas in RS on targets with heterogeneous material distributions, light sources with Watt-class optical output powers and the discussed spectral properties are required. Even higher output powers at tens of watts in continuous wave or pulsed operation are required to perform gas concentration measurements based on the DIAL. To enhance the optical output power available for applications without sacrificing the spectral characteristics, hybrid MOPA approaches are preferred. The light from an above described wavelength-stabilized diode laser serving as master oscillator master oscillator is focused into a power amplifier. This concept enables to increase the optical output power from the 100 mW range provided by the master oscillator to several 10 W. Moreover, short pulses in the nanosecond or picosecond range can be obtained, e.g. for DIAL.

### Advances in science and technology to meet challenges

Externally wavelength-stabilized tunable devices for the above discussed spectral sensor applications are well-established. Also, the concept of MOPAs for the generation of short laser pulses with high peak power, e.g. for lidar applications, is well-known. The realization of such systems in a compact manner requires time-consuming high-precision active alignments and mountings of optical elements during manufacturing. An implementation of gratings as intrinsic wavelength-stabilizing elements during the manufacturing process of semiconductor device reduces the efforts significantly and allows a large volume production.

Several approaches to implement internal grating structures into the semiconductor laser were realized, i.e. writing gratings using holographic exposure, using evanescent metal grating structures, and implementing higher order gratings by using electron beam lithography. Especially the latter technology allows for low-order surface gratings and full wafer processes without any additional overgrowth process.

DBR lasers (figure 43(a)) and DFB lasers (figure 43(b)) were manufactured on a full wafer scale for the spectral range between 635 nm and 1180 nm. At 785 nm, a typical excitation wavelength in RS, a DFB ridge waveguide (RW) laser reached a spectral tuning range of 1 nm suited for the application SERDS [568]. Larger tuning ranges up to 2 nm were realized using a DBR laser with implemented heater elements close to the grating [569]. A further increase of the tuning range could be expected by introducing Y-branch coupled DBR lasers (figure 43(c)) [570–573] or MMI coupled DBR-MOPA devices (figure 43(d)) [574, 575]. The concepts were extended to four (figure 43(e)) [576] or six branches [577] to increase the spectral coverage up to 23 nm at an emission wavelength of 975 nm.

For absorption spectroscopy, DFB lasers are preferred to scan over full absorption lines. In the VIS and NIR, experiments for the determination of oxygen and water vapor concentrations are known. Dual-wavelength diode lasers can be used for the above-mentioned $|_{on}-|_{off}$-approach.

An increase of the optical output power was achieved by coupling the laser emission from the devices from figures 43(a)–(e) into an amplifier figure 43(f) thus realizing cw output powers of 1 W at 785 nm [359]. Pulsing the injection current though the RW gate and the tapered section, pulses suitable for miniaturized DIAL systems with 4 ns length and 15 W output power were reported at 960 nm [578].

Figure 44 presents an overview of achievable cw optical output powers from selected devices. Lasers based on narrow RWs (light blue circles) or dual-wavelength Y-RW or MOPA devices (dark blue circles) reach optical powers up to 1 W. TPAs and lasers (red triangles) enable an extent increase of the output power by at least one order of magnitude.

### Concluding remarks

Selected optical sensing techniques in the agro-food sector require target-specific excitation light sources. This contribution presented selected concepts to address these requirements. The above-mentioned monolithic devices are nowadays commercially available or close to the introduction into the market, helping to advance current applications and giving access to entirely new fields of applications. In the future, the biggest issue would be the monolithic integration of MOPA systems. Present physical challenges include the thermal coupling between the different sections and the occurrence of different sub-resonators formed by the facets and the internal gratings. Technologically, the devices will have a larger length leading to a more challenging mounting technology, which should be as much as possible expansion matched to prevent internal strain over the entire resonator length. Also, the reliability of these complex devices could be a potential issue.
<figure><img src="image_47.png" /><figcaption>**Figure 43.** Schemes of selected diode laser concepts with internal wavelength stabilization (a)—DBR-ridge waveguide laser, (b)—DFB laser, (c)—Y-branch DBR RW laser, (d)—MMI DBR RW MOPA, (e)—four-wavelength Y-branch DBR RW laser) and a tapered amplifier (f)—red arrows mark the laser emission in a-e and the input laser beam into the amplifier f.</figcaption></figure>

<figure><img src="image_48.png" /><figcaption>**Figure 44.** CW output powers from RW DBR and DFB devices (light blue circles: single emitters, dark blue circles: dual-wavelength devices) and RW amplifiers at 405 nm [579] as well as tapered amplifier and lasers (red triangles) [580, 581].</figcaption></figure>
Nevertheless, state of the art manufacturing technology is available, and it should be possible within the next years to implement further features directly into the semiconductor laser and maybe combining the devices into photonics integrated circuits. Here, also the monolithic realization of extremely narrow linewidth devices suitable for quantum technology applications could be on the semiconductor road map.

Acknowledgment

This study received funding from the Federal Ministry of Education and Research (BMBF) within the projects RaMBo (Grant Nos. 031A564C, 031B0513C and 031B1069C) within the funding measure BonaRes (Soil as a Sustainable Resource for the Bioeconomy), DiLaRa (Grant No. 03V0207) and Research Fab Microelectronics Germany—FMD (Grant No. 16FMD02).
## 30. Advanced mid-infrared light sources for food quality/safety applications

Polina Fomina<fnref n="1" />, Michael Hlavatsch<fnref n="1" />, Antoni Femenias<fnref n="1" />, Diellza Bajrami<fnref n="1" /> and Boris Mizaikoff<fnref n="1" />,<fnref n="2" />,*

E-mail: polina.fomina@uni-ulm.de, michael.hlavatsch@uni-ulm.de, antoni-1.femenias-llaneras@uni-ulm.de, diellza.bajrami@uni-ulm.de and boris.mizaikoff@uni-ulm.de

### Status

In recent decades, MIR spectroscopy has evolved into an increasingly relevant analytical technique in food industry for quality control and safety [582]. Conventional MIR spectroscopy applied in this field is based on FTIR spectrometers relying on thermal radiation sources that emit broadband black body radiation usually via resistive heating across a wide spectral window (2.5–25 µm). Typical black body emitters include Nernst glowers and globars [583]. FTIR spectroscopy has gained importance for the analysis of food-borne pathogens like fungal contaminations and intrinsic mycotoxins (e.g. deoxynivalenol (DON), aflatoxin B1 (AFB1), ochratoxin A, etc) in a variety of commodities such as wheat, maize, peanuts, dried fruits [386, 584]. Besides such biopathogens, contaminations resulting from pesticides, herbicides and insecticides have also been investigated [585, 586]. In addition to its role in ensuring food safety, IR spectroscopy is useful for quality control and quality monitoring. This exemplarily includes but is not limited to the analysis of the fatty acid (FA) composition in lipids for characterizing fish and milk quality [587, 588].

Within recent years, advanced narrowband light sources including QCLs—in part combined with external cavities facilitating broad tunability (EC-QCLs)—and interband cascade lasers (ICLs) have become a competitive alternative to traditional broadband light sources also revolutionizing food safety/quality analysis. Lasers provide several advantages compared to broadband emitters including higher output power, operational stability and in part broad tunability (>400 cm⁻<fnref n="1" />). Nowadays, tunable MIR lasers are commercially available within the 3–13 µm spectral window, as well as QCLs/ICLs with single emission wavelengths between 2.5 and 14 µm [589]. MIR lasers are particularly suited for chem/bio sensing scenarios when addressing strongly absorbing samples or if high sensitivity is required.

For brevity an exemplary relevant food safety scenarios should be highlighted, i.e. screening of mycotoxins. For that purpose, the detection of DON in maize and wheat below 1250 µg kg⁻<fnref n="1" /> and the detection of AFB1 in peanuts below 2 µg kg⁻<fnref n="1" /> has been shown using QCLs operating in the particular spectral range of interest (5.5–6.4 µm) [394]. Moldy peanuts were identified by determining intensity differences of the FA signature around 1710 cm⁻<fnref n="1" /> [590]. The analysis of proteins (i.e. α-lactalbumin, β-lactoglobulin, bovine serum albumin) serves as a crucial example for quality control in dairy industries, and has been performed via EC-QCLs at concentration levels <0.1 g l⁻<fnref n="1" /> even in highly absorbing matrices such as water and milk [549, 591]. Hence, utilizing IR laser spectroscopy for screening contaminants provides distinct advantages vs. conventional food analysis methods usually relying on separation techniques—which remain the gold standard for detailed contamination studies—given minimal sample preparation, the potential of direct analysis, the non-destructive nature and reduced cost per analysis. While the current-state-of-the-art in food safety analysis using MIR spectroscopy relies on the application of blackbody emitters, the emerging usage of MIR lasers for target analysis pushes the envelope in terms of sensitivity and addressable matrices.

### Current and future challenges

While some challenges associated with the currently applied light source including black body emitters and QCLs remain, alternative technologies such as MEMS and MIR-LEDs are prospective solutions improving the capabilities of MIR chem/bio sensing in food analysis. Figure 45 provides an overview of the currently employed light sources in the context of food analysis and includes emerging technologies expected to be integrated within the coming years along with sample interface strategies and infrared detector technologies.

Nowadays applied MIR light sources in the food safety/quality analysis such as thermal emitters and cascade lasers are still subject to certain limitations and challenges. The light source temperature determines the wavelength range and the intensity of the radiation emanating from thermal emitters. This dependence on temperature limits the utility of thermal emitters in the so-called ‘fingerprint region’ (6–15 µm) containing relevant molecular information. Furthermore, thermal emitters radiate across a broad wavelength window, which consequently results in reduced energy density per wavelength or narrow wavelength band [583, 589]. Low light intensities together with background noise (e.g. electronic, vibrational or thermal

<footnote n="1">¹Institute of Analytical and Bioanalytical Chemistry, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany</footnote>

<footnote n="2">²Hahn-Schickard, Sedanstraße 14, 89077 Ulm, Germany</footnote>
<figure><img src="image_49.png" /></figure>

noise) affect the overall sensitivity of the device and may restrict applications in food analysis to target analytes present at high concentrations given that the achievable LODs hover around 0.05 g l⁻¹ [592].

Despite the evident advantages of using laser light sources, certain limitations remain affecting applications in real-world scenarios in the food industries. ICLs and QCLs are high-intensity light sources that may introduce inherent noise to the system. Hence, real-time referencing is recommended including but not limited to balanced detection schemes [593]. In addition, lasers emit radiation within a limited spectral bandwidth, i.e. even broadly tuneable QCLs do not cover the entire MIR spectral window of interest (2.5–25 µm). Last but not least, lasers typically require complex operation electronics for temperature, current and tuning control rendering ICL-/QCL-based sensing systems expensive, which in turn elevates the entry barrier especially into the food quality/safety screening market demanding for low-cost devices.

When designing portable and compact devices for on-site analysis, both types of MIR light sources usually require light-guiding optics, suitable electronics and appropriate detectors adding to the complexity and dimensions of the sensing system. Last but not least, it should be noted that any MIR sensing system is affected by environmental parameters including humidity and temperature, which may convolute the IR signatures of interest.

### Advances in science and technology to meet challenges

Next-generation MIR light sources already in part address some of the previously mentioned challenges and will be pivotal in establishing MIR sensing technologies in food quality/safety application scenarios.

MEMS-based light sources are also thermal emitters, yet, comprising a heating element integrated into a dielectric membrane usually supported by a silicon substrate. In contrast to conventional black body sources, the emitted wavelength range and the intensity of the radiation can be tailored by varying the structure of the MEMS pattern [583]. Exemplarily, applying graphene oxide (GO) coatings at the SiO₂ wafer surface allows increasing the emissivity by up to 150% via coupling of photons with plasmons induced at the GO [594].
<table><caption>**Table 5.** MIR light sources for food safety/quality analysis.</caption><thead><tr><td>Light source</td><td>Wavelength range</td><td>Advantages</td><td>Limitations</td></tr></thead><tbody><tr><td>Black body emitters</td><td>2.5–25 μm</td><td>Cover the entire MIR range</td><td>Limited emissivity in the fingerprint regime</td></tr><tr><td>QCLs/ICLs</td><td>Tunable up to 300 cm<sup>-1</sup> in the 3–13 μm regime; Single emission wavelengths available in the entire MIR band</td><td>High output power; operational stability; broad tunability</td><td>Limited spectral range; introduce noise</td></tr><tr><td>MEMS emitters</td><td>Depends on the surface structure and can be tailored in the 2.5–25 μm regime</td><td>High intensity in the fingerprint range (>5.5 μm); operation at low temperature</td><td>When tailored for a specific goal, may be limited for widespread use</td></tr><tr><td>MIR-LEDs</td><td>Depends on the quantum heterostructure and can be tailored in the 3–10 μm regime</td><td>High intensity in the fingerprint range (5.5–10 μm); precise emission wavelength; operation at low temperature</td><td>Currently only available in a limited spectral window (<10 μm)</td></tr></tbody></table>

Thus, the intensity limitation of black body emitters at room temperature may be circumvented rendering the fingerprint regime readily accessible.

By combining a MEMS light source with a pyroelectric line array detector (PLAD) paired with a LVF, compact and portable ATR—MIR devices without any moving parts may be established. These devices were successfully tested for the analysis of relevant analytes in food matrices (e.g. mycotoxins, fatty acids, organic solvents) demonstrating the utility of compact MIR diagnostics for the on-site analysis of food constituents. Integrating MEMS sources with ATR-MIR spectrometers offers sensitivities comparable to advanced laboratory FTIR spectrometers while enabling compact handheld devices. For example, FA analysis at 0.0864 g l⁻¹ has been achieved using such a device, which is close to the FTIR LOD of 0.0772 g l⁻¹ [592].

Another type of light source that shows potential in food safety and readily integrates with the PLAD-LVF spectrometer are MIR-LEDs. MIR-LEDs are based on a quantum heterostructure where electrons from the conduction band quantum wells (QWs) recombine with holes in the valence band QWs. Consequently, appropriate alignment of this ‘broken’ bandgap structure supports emission energies below the bulk bandgap of the two semiconductors by controlling the QW thickness [595, 596]. By appropriately engineering the QW thickness, the central emission wavelength of the MIR-LED can be precisely adjusted. This tunability of the central wavelength allows to target specific spectral regions, therefore increasing the selectivity of the analysis in complex matrices. MIR-LEDs show high emissivity when operated at room temperature leading to enhanced energy efficiency (EUE) and decreased temperature control requirements [597]. In addition, MIR-LEDs readily integrate onto miniature printed circuit boards, thereby facilitating compact portable devices. MIR-LEDs operating in the 3–10 μm spectral window have already been demonstrated [598], which covers characteristic bands of a wide variety of relevant food contaminants and additives.

In summary, the highlighted emerging MIR light sources demonstrate great substantial potential for miniaturized on-site food safety/quality analysis and sensing beyond lasers and conventional thermal emitters. Table 5 summarizes current and emerging MIR light sources in the context of food safety and quality analysis along with the associated benefits and limitations.

## Concluding remarks

In this roadmap, recent trends in MIR light source technology for food quality/safety analysis are discussed. Conventional MIR devices that are routinely employed for contaminant analysis and food quality monitoring are predominantly based on conventional black body sources with the emitted radiation modulated via an interferometer. Overcoming the limitations of thermal radiation sources, MIR quantum heterostructure lasers (i.e. QCLs, ICLs) are gaining importance, however, their adoption in food industries remains limited considering the associated high costs.

Emerging alternative light sources are not yet widely utilized in the field of food quality/safety, yet have promising potential especially for low-cost MIR devices and on-site detection of contaminants including but not limited to mycotoxins in cereals and pesticides in crops. MEMS-based emitters provide reduced power consumption vs. conventional black body emitters and facilitate increased emission intensities in the fingerprint regime. MIR-LEDs are probably the most promising future MIR light source enabling precise
tailoring of the central emission wavelength, a high degree of miniaturization, operation at room
temperature and direct integration into electronic control circuitry. In addition, MIR-LEDs and MEMS
sources are significantly less expensive compared to ICLs and QCLs, although this remains a moving target
depending on the number of devices produced per wavelength.

In summary, while highly sensitive analysis at ultra-trace levels will probably require laser technologies, rapid on-site screening and monitoring at moderate concentration levels may indeed be realized utilizing MIR-LEDs or MEMS-based light source technology integrated into compact chem/bio sensing devices.

Acknowledgments

This work was supported by the EU Horizon 2020 Project PHOTONFOOD [#101016444] which is part of the PHOTONICS PUBLIC PRIVATE PARTNERSHIP and by the Research Council of Norway via project SFI Digital Food Quality (DIGIFOODS) [#309259].
# 31. LED light spectrum and its power on the nutrient content in closed plant production CPPS

Ernesto Olvera-Gonzalez and Nivia Escalante-Garcia

Laboratorio de Iluminación Artificial (LIA), Tecnológico Nacional de México Campus Pabellón de Arteaga, Carretera a la Estación de Rincón Km. 1, Pabellón de Arteaga, Aguascalientes 20670, Mexico

E-mail: jose.og@pabellon.tecnm.mx and nivia.eg@pabellon.tecnm.mx

## Status

Multispectral LED lighting is a vital component in closed plant production systems (CPPSs) as it enables precise regulation of physiological and metabolic processes. This regulation not only enhances nutrient uptake and the synthesis of secondary metabolites such as antioxidants, anthocyanins, and phenolic compounds, but also strengthens the plants' response to stress. The production of these compounds, which are required for stress response, is species and growing condition-dependent. The use of 'light recipes' is a key strategy that allows for the adjustment of growth, water use efficiency (WUE), EUE, CO₂ uptake, biomass accumulation, and final product quality. The qualities (blue, red, green, yellow, infrared, and UV) of light, sensed by specific photoreceptors such as cryptochromes and phytochromes, regulate stomatal opening and root activity, promoting the boost of essential nutrients. These photoreceptors trigger signals that activate the expression of key genes in roots, such as nitrate (NRT1) and phosphate (PHT1) transporters, allowing efficient transport of nutrients into plant tissues. Upon receiving these signals, roots enhance the proliferation of root hairs, thereby optimizing surface area for water and mineral uptake. Figure 46 illustrates the effects of different LED light spectra and light combinations on nutrient and metabolite synthesis in plants. In this context, sustainable lighting strategies are essential to maximize crop productivity and efficiency in controlled systems [599].

## Current and future challenges section

**Blue light** regulates stomatal opening, anthocyanin synthesis, and the expression genes, increasing antioxidants and providing protection against oxidative damage [600]. Additionally, its influence promotes plant morphology and compact growth [601]. **Red light** growths leaf thickness and density and improving leaf structure, its repellent activity on some pests reduces the use of pesticides [602]. **Green light** absorbed in the deeper layers of the plant canopy, regulates metabolic processes, and nitrite content in leaves. Also, it promotes the synthesis of amino acids and essential biomolecules [603]. **Yellow light** is slightly used. However, it reduces the rate of net photosynthesis while increasing the accumulation of carotenoids and flavonoids, which are essential for antioxidant protection and it plays a role in photoperiodic signaling [604, 605]. **UV light** is a factor in the physiological structure of plants. UV-C controlling pathogens, improving the bioactive compounds like glycosylates, and enhancing stress tolerance [606]. UV-A promotes the accumulation of flavonoids and antioxidants, supports photomorphogenesis, and phototropism by enhancing growth [607]. UV-B boosts the production of secondary metabolites as carotenoids, and enhances tolerance to water stress, supporting resistance to pathogens and improving both plant health and postharvest quality [607]. **Infrared light** expands the quality, flavor, aroma, and appearance, increases the sugar content depending on the amount of nitrogen. The addition of infrared light to a spectrum increases biomass and leaf area of plants [608]. LED light recipes in **White + Blue (84:16)** increase the biomass accumulation in different crops, in addition, WB light activate plant physiological activity and the synthesis of compounds such as anthocyanins and ascorbic acid; its application in closed-plant production systems is convenient [609]. The **Red** and **Blue** (70:30) increases the growth and synthesis of compounds (anthocyanins and phenols). It promotes biomass production, improves the concentration of chlorophylls and flavonoids in the leaves, and increases the nutrients route such as nitrogen, phosphorus, potassium, and magnesium [599]. **Red + Blue (90:10)** helps enhanced biosynthesis and chlorophyll accumulation, improving water-use efficiency and promoting biomass accumulation [609]. **Red + Green + Blue (30.6: 44.0: 25.4)** increases biomass in crops such as radish microgreens, lettuce, other green leafy vegetables improve flavonoid and anthocyanin production. The balance of red and blue radiation allows photosynthetic efficiency and antioxidant formation [609]. **Red + Green + Blue (80:10:10)** promotes biomass production, inhibition of potassium accumulation in leaves. In addition, it increases seed yield, accumulation of macro and micronutrients in plants. It contributes to CO₂ fixation and increases fresh and dry weight [609].

## Advances in science and technology to meet challenges

A concise summary of strategies, methodologies, and technologies developed for the sustainable management of LED lighting in CPPS is presented below (see figure 47).
<figure><img src="image_50.png" /><figcaption>**Figure 46.** LED light (red, blue, green, infrared) influences the synthesis of specific metabolites and enhancing compounds.</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>**Figure 47.** Strategic and sustainable use of LED light in CPPS.</figcaption></figure>

**Light/Water/Fertilizers:** In CPPS, efficient water use (WUE) is vital, influenced by light factors like intensity, spectral content, and DLI. A balanced mix of these factors enhances WUE [610]. Optimal light intensity is linked to light quality. In addition, excessive light increase transpiration and water usage. Regarding light and fertilizers, the LEDs technology offer diverse spectra, paired with fertilization, to boost nutrient absorption in crops, ensuring human-consumable nutritional levels. Extensive LED spectral research unlocks opportunities to optimize nutrient solutions (micro and macronutrients) for crops in CPPS, yielding innovative strategies for superior growth and quality [611–613]. **CO2/Light Intensities/DLI:** Research highlights strong links between CO2, DLI, photoperiod, and light intensities, achieving ideal outcomes in biomass, photosynthesis, weight, nutrient content, and resource efficiency (RUE). Regarding the efficient use of LED light, non-stress threshold light levels and optimal CO2 levels are the key, high light intensities—generating greater energy consumption—do not necessarily yield the best results [614–616]. **LED technology evolution:** In considering LED technology for horticulture, two crucial factors emerge: LED costs, linked to reduced investment in CPPS, and photosynthetic photon efficiency (PPE), linked to energy savings. LED manufacturing costs dropped by around 75% from 2014 to 2019, while PPE in white LEDs rose from under 1 [mol/j in 2005 to over 2.5 [mol/j in 2021, with an expected 3.1 [mol/j by 2050. In CM-LED
with a red-blue ratio of 4, PPE increased from slightly over 1 [mol/j in 2005 to over 3.1 [mol/j in 2021, projecting over 4.5 [mol/j by 2050 [617]. **Pulsed LED Light**: LEDs enable pulsed light control without affecting morphological parameters (dry biomass, leaf area, leaf count, or water usage). However, pulsed light impacts phenolic compounds and antioxidant levels, modifying the nutraceutical value of lettuce. Pulsing also reduces LED energy consumption by 8% to 12%, depending on the light recipe, frequency, intensity, and duty cycle [618, 619]. **Green technologies, IoT sensors and AI**: Green technologies like solar panels, wind turbines, storage batteries significantly lower energy cost [618]. IoT and AI will form a powerful combination in vertical farming. For example, IoT sensors can monitor and automate the control of pH, water levels, temperature, and light intensity, as well as manage nutrient solutions remotely. IoT combining with AI can be used for disease detection, highly accurate yield prediction, plant health, leaf and root inspection [620], and also predict LED light energy consumption [621]. AI allows increasing the precision of affordable sensors, reducing the cost of equipment [622]. Implementing IoT sensors in CPPS keeps key growth factors (pH, temperature, dissolved solids, and humidity) within optimal ranges, resulting in significant increases: fresh weight (25%), root weight (36%), dry weight (32%), root dry weight (52%), and leaf count (6%). Nutrients levels, including Ca, Mg, Mn, and Cu, also showed improvement [623].

**Concluding remarks**

Specific light recipes offer a WUE, EUE, CO₂ uptake, biomass production, and better crop nutritional quality. Blue, red, green, and UV light, are sensed by specific photoreceptors, which initiate a cascade of gene expressions, enhancing nutrient uptake, and antioxidant production. Pulsed LED lighting has demonstrated energy saving benefits while maintaining crop quality. Additionally, integrating green technologies, IoT sensors, and AI in CPPS further enhances precision in controlling growth factors, achieving resource efficiency and promoting sustainability. We underscore the importance of the scientific information available in the literature through two fundamental points:

1. Basic research about the effects of LED technology on plant growth for human consumption remains a topic of great interest to the scientific community. This research continually generates new insights into the phenomena occurring in plant-light interaction.

2. The integration of IoT and AI will be crucial for sustainable crop management in CPPS. Although these technologies may initially increase investment costs due to the level of automation required and higher energy consumption, they offer significant advantages. IoT and AI will be essential in reducing human error, enable continuous monitoring, and supporting the cultivation of higher-value crops in a production system that operates 24/7 throughout the year.

**Acknowledgments**

We acknowledge the support of the Consejo Nacional de Humanidades Ciencia y Tecnología (CONAHCYT) in Mexico for supporting this work through funds for projects INFRA-2016-01, Project No. 270665. CB-2016-01, Project No. 287818.
## 32. UV light-based technologies for potential application in the agri-food sector

Arturo B Soro¹,², Koenraad Van Hoorde³ and Brijesh K Tiwari⁴

¹ Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Facultat de Farmàcia i Ciències de l'Alimentació, Campus de l'Alimentació de Torribera, University of Barcelona, Barcelona, Spain

² Institut de Recerca en Nutrició i Seguretat Alimentària (INSA·UB), University of Barcelona, Barcelona, Spain

³ Foodborne Pathogens Unit, Department of Infectious Diseases in Humans, Sciensano, Brussels, Belgium

⁴ Teagasc Ashtown Food Research Centre, Dublin, Ireland

E-mail: arturoblazquezsoro@ub.edu, Koenraad.VanHoorde@sciensano.be and Brijesh.Tiwari@teagasc.i.e.

### Status

UV light is electromagnetic radiation below the visible spectrum with wavelengths ranging from 200 to 400 nm that can be divided into three wavelength ranges: UVC (200–280 nm), UVB (280–315 nm), and UVA (315–400 nm). The UVC region is identified with the highest germicidal effect that corresponds to the peak of absorption of bacterial DNA [624]. The effectiveness of UVC light is derived from its molecular action on DNA, leading to the formation of pyrimidine dimers, primarily thymine dimers. These dimers disrupt the DNA structure, interfering with the processes of replication and transcription, and ultimately leading to the inactivation of microorganisms [625]. Currently, the most common sources of UV light for industrial applications include low- and medium-pressure mercury lamps, excimer lamps, broadband pulsed UV lamps, microwave UV lamps, and LEDs technologies. Each of these has a specific relevance in the agri-food sector for the decontamination of water, surfaces and food [624, 625].

Historical timeline of UV is presented in figure 48. In 1877, Downes and Blunt observed no microbial growth after using sunlight in a bacterial suspension [626]. In 1949, Kelner discovered photoreactivation, which allows bacteria to recover from UV light when exposed to visible light. Eleven years later, Beukers and Berends further elucidated UV light's impact on bacterial DNA by observing the formation of thymine dimers on exposed DNA strands [627]. While the fundamental principles of UV light have remained constant for centuries, the technological advancements and applications of this technology have evolved significantly.

In the early 20th century, Niels Finsen developed the first artificial UV source, employing an electric carbon arc light used to treat lupus vulgaris [627]. Moreover, the production of the first quartz lamp in 1904 by Kuch led to the creation of the first commercial UV mercury lamp for water disinfection in Marseille, France [628]. These early developments laid the groundwork for today's diverse applications of UV light in the agri-food sector, from disinfection to enhancement of food quality. Over time, UV mercury lamps were improved and became the standard for water treatment, waste-water treatment, air purification, and surface disinfection against viruses, bacteria, fungi, and parasites [629]. Beyond disinfection, UV light has found applications in agriculture to enhance plant growth, biomass accumulation, photosynthesis and stem diameter, and increase the resistance to insects and pathogens. This technology also showed to boost nutrient content such as vitamin C, phenols, flavonoids, chlorophyll, carotenoids, and anthocyanins [630]. Vitamin D is an essential micronutrient for the correct functioning of the human body. Apart from sunlight, dietary sources of vitamin D are also important. However, few foods (e.g. mushrooms, blue fish, dairy, etc) naturally contain this vitamin. Notably, the combined effect of UV light with drying processes showed to induce the conversion of ergosterol to vitamin D in mushrooms. The application of UV light for producing vitamin D-enriched mushrooms may be a promising approach for addressing nutritional deficiencies in humans [631].

Despite these remarkable applications, the use of UV light in food industry is still considered an emergent technology by regulatory agencies and the agri-food sector. Further advances in this field could facilitate its implementation in food industry [629]. Furthermore, the emergence of the LEDs technology has revolutionized the UV light source market. The use of UV-LEDs can offer a number of benefits of EUE, operational costs and electricity consumption to the agri-food sector. The higher EUE of UV-LEDs can contribute to a reduction in operational costs and electricity consumption. Additionally, the longer lifetime of UV-LEDs, which also means less frequent replacements, can lead to a reduction in maintenance costs and waste, enhancing the cost-effectiveness of the technology over time. Moreover, the lower heat generation of UV-LEDs is especially beneficial for temperature-sensitive food processing applications, where excess heat could degrade product quality or affect freshness. Furthermore, unlike mercury lamps, their mercury-free design enhances environmental safety, and their adjustable wavelengths and consistent light intensity allow for targeted microbial control without compromising food quality [625, 632]. Ongoing research in UV light promises safer, more efficient, and sustainable solutions for the agri-food sector.
<figure><img src="image_52.png" /><figcaption>**Figure 48.** Timeline of UV light discoveries and technological advances (Created with BioRender.com). Information extracted from [625–627].</figcaption></figure>

## Current and future challenges

The implementation of UV-based technologies in the agri-food sector requires some challenges to be overcome. Safety and regulatory challenges that consist on ensuring the safety of foods processed with UV light. The regulatory authorities carefully assess and verify the adoption of new technologies. Generally, research must navigate a complex regulatory network to demonstrate the safety and effectiveness of a new technology such as UV light [633]. For instance, the European Food Safety Authority (EFSA) was required to assess the safety of UV light technology for the commercialization of UV-treated bread and cow milk. The novel food EFSA panel evaluated the impact of UV light on the nutritional composition, safety, microbiological quality, and allergenicity of both foods. The panel concluded that the UV light treatment was safe under the intended conditions of use. Other regulatory bodies, such as the Food and Drug Administration (FDA) in the United States, require substantial evidence demonstrating both the safety and efficacy of UV technology for food applications [629]. Addressing these regulatory hurdles is essential for successful adoption in the market.

Apart from safety and regulatory challenges, the agri-food sector must also cope with technical and operational issues in implementing UV technologies. Microorganisms can exhibit high differences in susceptibility to UV treatment. Research is needed to understand the stress response mechanisms of microbial cells involved in UV adaptation and cross-protection mechanisms towards other stresses such as heat, disinfectants, pH, etc [625]. Furthermore, identifying optimal UV doses for different crops and foods is a complex task. UV optimization can be challenging in food due to its low penetrating power. Moreover, the application of UV light to opaque or cloudy liquid foods, powders, or uneven or rough foods can affect the effectiveness of the treatment. The presence of UV absorbing molecules (e.g. sugars, pigments, organic acids, etc.) in foods such as juices may also have an impact on the treatment optimization [634]. Factors such as UV wavelength, intensity, exposure time, and distance from the UV source contribute to achieving the desired results. Researchers need to develop accurate UV dose guidelines for various applications [635]. Currently, standardized guidelines or protocols for UV measurement are lacking. Some studies have proposed the adoption of the nomenclature established by the Photochemistry Commission of the International Union of Pure and Applied Chemistry and recommended the measurement of the photon fluence and photon fluence rate as a means of quantifying the energy applied to water, food or a surface [632, 636].

Consequently, new advances in UV light based-technologies must be taken into account. Although UV-LEDs offer numerous advantages, such as EUE and longer lifespan, their development poses challenges. Researchers should improve the performance, affordability, and availability of UV-LEDs for wider adoption [632]. Thus, although UV technology has proven to be effective in laboratories and small-scale environments, the expansion of commercial agriculture and food processing is challenging. Researchers must design scalable and cost-effective UV systems capable of meeting the volume required by industry. In addition, the introduction of UV technology into existing agri-food processes may be disruptive and
expensive. Researchers must develop strategies that seamlessly integrate UV into the current workflow to reduce production delays and costs [637].

UV technology is generally considered to be an environmentally friendly strategy compared to chemical and thermal treatments, but its environmental impact needs to be carefully evaluated. Researchers should conduct life cycle assessments to determine the overall sustainability of UV-based processes [638]. Additionally, long-term research is needed to assess the potential cumulative effects of consumption of UV-treated foods on human health. A primary concern is the potential alteration of nutrients sensitive to UV light, resulting in a change in the nutritional composition of foods. Other concerns include the potential for UV light to induce lipid oxidation, the formation of new photochemical compounds or allergens, the degradation of antioxidants, the generation of off-flavors or the alteration of the food color. The effect of UV light on molecular degradation from a nutritional, quality and sensory perspective has been studied in foods (e.g. vegetables, meat, milk and milk products, etc). Studies evaluating this technology have reported mixed results. The majority of studies have proposed optimizing the UV treatment to maximize the UV effect while minimizing the impact on the nutritional, quality and sensory aspects of food [637]. Finally, consumers' perception of the safety and benefits of UV-treated food is a major challenge. Research into consumer perception and education campaigns is crucial to promote acceptance [639]. Other technologies such as high-pressure pasteurization (HPP) have been gradually adopted in the food and beverage industry. This is a consequence of an increasing consumer concern for minimally-processed, clean label, and organic food products. Strategies for promoting the adoption of technologies among consumers include clear communication and transparency, campaigns focusing on the perceived benefits of their incorporation, and the creation of certifications or labels indicating the safety of the technology [640].

## Advances in science and technology to meet challenges

Future efforts should focus on addressing the challenges mentioned above. Improvements in sensor technologies such as UV intensity sensors and pathogenic and spoilage microorganisms' detection sensors could improve the accuracy and effectiveness of the UV treatment. These sensors are designed to detect the presence of an analyte through chemical or biological interactions, providing real-time monitoring and adjustment of food processes such as UV, ensuring consistent results [641]. Moreover, advanced modeling and simulation tools can help predict UV treatment outcomes for specific crops and pathogens. These tools are descriptive and predictive strategies that could help us better understand changes in food microbiology and food quality. Modelling and simulation techniques could facilitate the optimization of UV-based technologies and their system design [642].

UV-LEDs offer many advantages over conventional UV mercury lamps, but they require further development. The advances in UV-LED technology may require to include advances in their design, EUE, and scalability to make them more accessible to the agri-food sector. Innovative design and engineering solutions for UV reactor could address these challenges [632]. Ensuring uniform exposure is crucial for effective microbial inactivation, which can be achieved through rotating systems or multiple UV-LED arrays. Heat dissipation is equally important to prevent damage to food and equipment, requiring the incorporation of cooling systems. Furthermore, UV reactors must integrate seamlessly with existing processing lines, employing modular and compact designs, while maintaining optimized LED placement to ensure EUE. Another critical aspect is scalability, which enables UV reactors to accommodate varying production volumes [629].

Research efforts should focus on establishing industry-wide standards and guidelines for UV treatment protocols, safety standards, and equipment specifications. In the absence of international, clear and standardized guidelines, manufacturers and food processors are confronted with ambiguity regarding the optimal UV treatment, equipment design, and safety assessments necessary for various food types. This lack of clarity impedes regulatory approval and slows market adoption. For example, inconsistent safety protocols across countries presents a challenge to the international trade of UV-treated products [634]. The standardization of UV technology and treatment protocols is essential to ensure consistency and quality in the agri-food sector. To illustrate, the current and widespread use of HPP technology for food application was achieved by establishing clear safety and operational guidelines, thereby ensuring consistent quality and microbial safety [643]. Collaboration between researchers, industry stakeholders, and regulatory bodies can lead to this standardization effort. At the same time, efforts should be made in cooperation between researchers, regulators, and industry players to streamline the approval process for UV-treated foods. Clear guidelines and regulations can provide a framework for implementation [634, 644].

Understanding the genetic and physiological mechanisms that influence microbial susceptibility to UV light could help optimize UV treatment protocols. Advances in microbiology such as the incorporation of next-generation sequencing technologies (e.g. genomics, transcriptomics, proteomics...) might contribute to the understanding of the microbial stress response to UV light and the ability of UV light to induce
cross-protection to other stresses [625]. As an example, genetic mutations in the bacterial genome caused by UV light exposure can be identified through the use of Whole Genome Sequencing [645]. Insights such as mapping genetic mutations, studying gene expression changes or understanding protein-level responses may assist in the optimization of UV light inactivation treatment protocols by enabling the tailoring of UV doses to exploit microbial vulnerabilities, thereby reducing the likelihood of resistance. Furthermore, an understanding of how UV exposure induces cross-protection against other stresses (e.g. heat or disinfectants) can inform the development of more effective combination treatments that prevent adaptive microbial survival [625]. Consumer acceptance is one of the most important challenges to be faced by the implementation of UV light on the agri-food sector. Confusion of consumers of UV light with ionizing radiation (or radioactivity) may affect the acceptance of this technology. Advances in communication technologies could promote the education and acceptance to new technologies. Interactive applications, augmented reality, and virtual reality experiences may help consumers understand the benefits and safety of UV-treated food products [646, 647].

**Concluding remarks**

Past and present advances in UV light technologies have contributed to their increasing interest and promising applications in the agri-food sector. Despite the potential of these technologies, several challenges must be overcome to ensure their successful integration in the agri-food processes. Safety and regulatory concerns require careful navigation, with a focus on microbial response mechanisms and precise UV dose guidelines. UV-LEDs offer several benefits but need further development to improve scalability and affordability. Environmental sustainability and long-term health effects require rigorous assessment, while consumers' perception remains a major challenge. Effective communication and education efforts are essential to promote acceptance among consumers. Moreover, future efforts should prioritize improvements in sensor technology and modeling and simulation tools for the accurate application of UV treatment. This can be also achieved through the use of novel techniques to enhance the understanding of microbial mechanisms towards UV light. Furthermore, other advances in technology such as advances in design and scalability may facilitate the adoption of this technology at industry level. Collaborative efforts from scientists, industry stakeholders, and regulatory bodies are essential to drive the development and implementation of the UV technology in agri-food applications while ensuring safety and compliance with regulations. Future research should prioritize the refinement of UV-LED effectiveness, the optimization of dose guidelines for diverse food types, and the development of robust sensor technology to monitor and control UV exposure with precision. These subsequent steps could facilitate the broader integration of UV technology in agri-food processes over the next decade, potentially leading to its routine use across various stages of food production.

**Acknowledgment**

This word was supported by the Department of Agriculture, Food, and Marine (DAFM) project CAMPYDECON: Controlling Campylobacter Contamination in Poultry Processing.
## 33. UVC-LEDs for water treatment

Thomas Westerhoff¹ and Michael Kneissl²,³

¹Smart UV Systems at Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Am Vogelherd 90, 98693 Ilmenau, Germany

²Institute of Solid State Physics, Technische Universität Berlin, Hardenbergstr. 36, 10623 Berlin, Germany

³Ferdinand-Braun-Institut (FBH), Gustav-Kirchhoff-Str. 4, 12489 Berlin, Germany

E-mail: thomas.westerhoff@iosb-ast.fraunhofer.de and kneissl@physik.tu-berlin.de

### Status

For more than 100 years, UV radiation has been used in drinking water disinfection, as well as in wastewater treatment, as the process does not require any chemicals and thus does not affect the taste of the water. For this purpose, almost exclusively low-pressure (LP) mercury lamps with a typical emission wavelength of 254 nm are employed and wall-plug efficiencies (WPE) in the range of 20%–40% [648]. In large-scale water treatment plants, medium-pressure lamps (MPLs) are also used since these systems can provide higher irradiation powers. The geometric shape of these radiation sources offers few design options for UV water disinfection reactors. Therefore, disinfection systems are usually cylindrical and the radiation source is surrounded by the water to be disinfected. However, all these lamp types contain mercury, which is harmful to the environment and to human health. Hence, the utilization of mercury is subject to growing regulations in numerous countries, with ongoing initiatives aimed at further limiting its usage, potentially leading to a comprehensive ban. For example, due to the new EU Eco-design Rules for light sources as well as RoHS regulations, a total ban of mercury containing fluorescent lamps was implemented within the European Union in 2023 [649]. However, water disinfection is a mandatory requirement in many regions worldwide because of hygiene concerns, and a ban can only be implemented once a viable alternative technology becomes accessible. Recent advancements in UV LED technology, particularly the improvements in optical power and WPE, have already led to first LED-based disinfection solutions in water treatment. Even though the focus is currently on smaller systems for point-of-use (PoU) and point-of-entry (PoE), there are also first prototype implementations of LED-based systems on an industrial scale. Even though the WPE of UVC-LEDs (UV-C LED) is currently lower compared to conventional mercury discharge lamps the development of UVC-LED-based water disinfection systems has gained significant momentum since these systems offer clear advantages due to their unique properties. For example, UVC-LED based water disinfection system enable a much higher microbiological effectiveness. In contrast to mercury lamps, the emission wavelength of LEDs can be tuned by the manufacturing process to ideally fit the peak efficiency for damaging the DNA of microorganisms (i.e. viruses, bacteria, molds) which is in the wavelength range between 265 nm and 270 nm. Figure 49 shows an example of the microbiological efficacy for some selected microorganisms as well as the emission spectra of a LP mercury lamp (blue) and a 265 nm UVC-LED (red). As can be seen, the spectrum of an UVC-LED is much broader than that of a mercury lamp, but in comparison the 265 nm UVC-LED would be the most efficient radiation source for the inactivation of microbes as it emits close to the peak wavelength of the efficacy curves.

### Current and future challenges

Another advantage of UVC-LED based water disinfection systems is clocked operation, since UVC-LEDs provide full irradiation power within fractions of a second after turn-on and they also allow many switching cycles without degradation. These are significant advantages compared to mercury lamps which require several minutes to reach full power and suffer from premature ageing if exposed to frequent switching. As a consequence Hg lamp based disinfection systems usually operate 24/7 even when no water is flowing and thus consume an unnecessarily large amount of electrical energy. In contrast, UVC-LED based systems can be turned on-and-off as needed and their output power can be controlled electronically to match the required irradiation dose levels. Therefore, despite their currently lower WPE and higher purchase price, already today UVC-LED based water treatment systems can be more cost-effective over their service life and are likely to be more efficient due to their higher microbiological effectiveness. An additional hurdle for the practical implementation of UVC-LED water disinfection systems in real-world scenarios are outdated standards and regulations. In many countries, e.g. the US and EU, UV water disinfection systems, especially for municipal use, can only be put into operation after they received successful certification in compliance with local regulations. However, these regulations are generally based on classic mercury-containing radiation sources, which only have a narrow-band emission peak at 254 nm. The benefits, that UVC-LEDs provide, like a broader emission spectrum as well as variable wavelengths and even combinations of