The Near-IR spectra that are used for quality control of food are quite complex to analyse. They contain overtones and combination spectra which are broad-band and a few orders of magnitude lower in intensity as compared to the Mid-IR intensity of the fundamental band.
Many suppliers of food and nutritional products are interested in a handheld near-IR instrument that can measure the ingredients of their raw supplies and also sort them. This is not only a hardware challenge but measuring the reflectance spectrum by the hardware and converting it to the absorption spectrum by the app is only the first step of this process. The most difficult part is to use a chemo-metrics model such as a Principal Component Analysis (PCA) for sorting/identification or a Partial Least Squares (PLS) model for quantitative predictions. Each model should have its own set of calibration spectra which should preferably be accessible through a cloud platform.
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