# Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture
## 4 Current Applications and Future Perspectives
### Elastic Spectroscopy
For the time being, however, the majority of both experimental studies and practical applications, at least on Vis-NIRS, involve spectral \"point\" measuring systems instead of MHS imaging. They have been used to assess several plant and fruit quality attributes, disorders and pathologies [10; 11; 54; 78; 79; 80]. They have also been incorporated in many commercial applications to be used on inline, benchtop and handheld devices, which similarly to the MHS imaging systems, may benefit from the non-linear techniques of machine and deep learning to obtain the best calibration models. However, there are several issues regarding the full potential and limitations of this technology that require further attention and research to provide the robustness and accuracy warranted by researchers when acquiring information on the composition and structure of plant tissues and by the daily basis assessment routines in various crops supply chains. First of all, and despite the many recommendations [31; 81], the calibration models' robustness still needs to be accounted for and solved through stringent multi-year, multi-cultivar and multi-orchard validations, such as previously reported for mango and orange [56; 57]. This is of the utmost importance when considering the assessment of fruit ripening on-tree across different plots of an orchard or in different orchards and essential to depart from the \"proof of concept\" approach and embrace the real-life conditions. Secondly, there is the effect of the mind in the assessment of the pulp quality attributes in thick rind fruit, since the NIR radiation hardly reaches the fruit pulp and both biochemical and optical properties have a major role to play in the spectral data acquired [1; 59; 60; 70; 72]. Thirdly, in spite of the different spectral ranges, levels of accuracy and cost-effective portable devices, only a few have been effectively used on-tree to assess fruit quality and ripening, for there are many conditions such as light and temperature that diminish the devices performance and deteriorate the calibration models predictions, as observed for citrus [10; 11; 66; 82; 83; 84; 85]. Finally, it is very important to add that these devices are of medium and high cost and that they are not the kind of technology to \"set and forget\", as reiterated by [11]. This warrants not only for a budget to acquire the systems but also to maintain them and to keep the continuous update and improvement of the calibration models, which in most cases need the selling company's assistance. Overall, producers and packinghouses must be able to add commercial value to crops supply chains through these systems and direct the consumer willingness to pay for the fresh commodities graded in terms of IQA such as sugar content, acidity and nutraceutical properties. Both SRS and TRS have been shown to be valuable tools in the sorting of both fruit and vegetables based on the assessment of their quality attributes, external and internal defects caused by several disorders and pathologies, and the identification of contaminants [26; 36; 40]. However, compared to SRS, TRS is considered to be more accurate in measuring optical properties and able to reach larger depths in tissues. This turned TRS into a smart sorting tool regarding the non-destructive assessment of IQA and disorders of various fruit [26; 40; 41]. However, TRS is also expensive and complex, even for a portable TRS device, starting on the requirement of good contact between the detection probe and the sample during the measurement, which may introduce some difficulties when working with intact fruit and plants with curved or irregular shape. Similarly, to all other spectroscopic techniques, computation of the optical parameters from the SRS and TRS measurements usually requires sophisticated numerical methods to access all the physiological information from the spectral data acquired, which presents a major drawback, based on the substantial computational time needed. [PERSON] et al.