# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 5 Emerging Technologies for Portable Spectroscopy
This tradeoff does not apply to an FTIR spectrometer, as the resolution does not depend on the size of the input aperture, and it is this fact that constitutes the Jacquinot's advantage. In fact, it is not just the system in Figure 2f that displays the [PERSON]'s advantage. Systems depicted in Figure 2b,c,e also have it, as they do not need an input slit. Another important concept in comparing spectrometers is the [PERSON]'s advantage [125]. This advantage comes from multiplexing the measurement, that is, the simultaneous measurement of several wavelengths by the same detector. For example, in FTIR spectroscopy, all wavelengths are measured simultaneously in the detector for each position of the moving mirror. This is the extreme example of multiplexing, encompassing all wavelengths. In system (d), with DMDs, there is also multiplexing, as it is a Hadamard spectrometer, as explained above. Multiplexed measurements allow the reduction in the signal-to-noise ratio by a factor \(\surd\)N, where N is the number of elements sampled in the spectrum [125]. On the other hand, classic dispersive spectrometers do not have this advantage because each of the detectors (pixels) receives only one wavelength at a time. However, [PERSON]' s advantage also entails a disadvantage. As all wavelengths are processed simultaneously to obtain the spectrum, noise limited to one of the spectral components ends up being spread across the entire spectrum. This disadvantage is important if, for some reason, there is a particularly noisy spectral component. The systems in Figure 2d,f benefit from [PERSON]'s advantage. A summary of the main spectrometers characteristics previously described is presented in Table 1.  
The advantages described above are, however, not decisive when choosing these new devices. The sensitivity of the detectors employed and the geometric factors associated with light coupling are also of critical importance for the overall behavior of these systems. The interest and potential of the new spectrometers are quite clear. Their more affordable price, higher portability and the possibility to operate the equipment wirelessly are the characteristics necessary to implement spectroscopic networks in the context of IoT. We are, therefore, at the dawn of a new age, in which spectroscopic sensors can be used on a large scale. This intensive use can be the result of its operation by a large number of users or its \begin{table}
\begin{tabular}{c c c c c} \hline
**Spectrometer** & **Jacquinot’s** & **Fellgett’s** & **Point or** & **Achievable Spectral** \\
**Type** & **Advantage** & **Advantage** & **Array Detector** & **Resolution** \\ \hline (a) Dispersive & N & N & Array & High \\ (b) Multi-filter & Y & N & Array & Low \\ (c) LFV & Y & N & Array & Medium \\ (d) DMD & N & Y & Point & Medium \\ (e) Fabry–Pérot & Y & N & Point & Medium \\ (f) FTIR & Y & Y & Point & High \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of basic features among the different spectrometer types used in conventional and major emerging optical spectroscopic technologies. autonomous integration into the IoT, remotely and automatically. In either case, the issue of managing and analyzing large amounts of data will be raised in the context of what is called big data. With the new spectrometers, it will be possible to infer in real-time the evolution of spectral characteristics of leaves or fruit on-tree, orchards, forests and also in indoor environments, such as greenhouses or cold storage chambers. The evolution of these spectral characteristics, by itself, may be enough to infer the physiological states of plants and fruit. However, the spectral data acquired may also feed calibration models based on classical chemometric methods or on the recent deep learning methods to predict physiological parameters. The management of information from the end user's point of view may involve the use of a second layer of analysis to make averages, interpolations and spatio-temporal forecasts.