# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 5 Emerging Technologies for Portable Spectroscopy
It is not the purpose of this article to review all works in the field, but the following short list below offers the reader a brief overview of the work that has already been performed with these emerging devices. These are mainly comparative studies with classic dispersive spectrometers, including benchtop instruments. In general, the conclusion reached by these investigations is that the new spectrometers have a performance only slightly inferior to that of bench instruments. There are, however, some cases where the performance of the new spectrometers is considerably lower than that of the reference instruments and also other cases where the performances are perfectly equivalent. It is also necessary to bear in mind that these studies are essentially proof of concept, not demonstrations of model robustness, as there are, in general, no studies with data from several years and different sources. This type of prolonged study is yet to be performed. Finally, the articles presented do not yet illustrate the integration of the new spectrometers into a true IoT philosophy, which will probably be observed in the coming years. Thus, a setup of AS7265x multispectral sensors (AMS, Premstaetten, Austria; b-type in Figure 2 and Table 1) has been compared with an Ocean Optics USB2000+ (Ocean Insight, Orlando, USA) dispersive spectrometer (classical a-type in Figure 2 and Table 1) for the assessment of SSC in apples [126]. The two pieces of equipment delivered similar performance. In another study [127], a NIRONE (Spectral Engines, Finland), which is an e-type spectrometer (MEMS-based Fabry-Perot; Figure 2, Table 1), was compared with a hyperspectral imaging system (DV Optics, Padova, Italy) to predict the QA of treated ground peppercors. Again, the new spectrometer performed as well or better than the classical counterpart. In the same way, a SCiO spectrometer (consumer physics, Tel Aviv, Israel; b-type; Figure 2, Table 1) was compared with a reference bench spectrometer, a NIRFlex (Buchi, Flawil, Switzerland), in the context of cheese analysis. Although the NIRFlex delivered slightly better results, the SCiO produced equally good models [128]. The investigation by [129] represents one of the most comprehensive comparisons made so far, involving two dispersive instruments, the LabSpec4 (ASD, Boulder, CO, USA) and the F750 (Felix Instrument Inc., Camas, WA, USA), two digital micromirror devices of d-type, the NIRScan Nano (Texas instruments Inc., Sherman, TX, USA), and the TellSpec (Tellspec Inc., Toronto, Ontario, Canada), a two linear variable filter device of c-type, the MicroNIR1700 and MicroNIR2200 (Viavi Solution, Milpitas, CA, USA), a MEMS-triggered Fabry-Perot of e-type (NIRONE 2.2) and a filter array device of b-type (SCiO) (Figure 2, Table 1). The comparison was performed regarding the determination of sugarcane properties (total sugar and crude protein content). The results were given through cross-validation, which is of limited interest in evaluating the true performance of the devices under real conditions but which nevertheless provides a relative measure of their performance. The LabSpec delivered the best results for both parameters, which demonstrates that high-grade classical dispersive instruments are still the best choice. The F750 did not deliver such good results probably because of the limited wavelength range compared to the LabSpec 4. Globally, the MicroNIR devices were the ones matching more closely the performance of LabSpec 4. For total sugars, all the other devices yielded a prediction error 2 to 3 times higher, while for crude protein, all the tested devices delivered comparable errors, except for the digital micromirror devices (NIRscan Nano and TellSpec), which produced prediction errors clearly above the others.