Utilization of spectral signatures of food for daily use
The analysis of spectral signatures of materials is an established technology in biochemistry and analytical chemistry. This includes the identification of different materials and some of their ingredients. A common method used is optical spectroscopy. Optical spectroscopy refers to the visible effects caused by the interaction of matter with electromagnetic radiation. Because each element has its own specific energy reflection within different wavelengths, the identification of materials or material families is generally possible. With respect to the availability of sensors, the greatest opportunity for the broad use of this technology is expected in the wavelength of NIR. Given the complexity of reliable identification and verification of spectral signatures of a product, the three aspects that must be minimally considered are as follows: the product itself, the necessary sensors, and the evaluation of the obtained data. Special attention must be paid to the measurement itself, the reflection of the material, and the calibration of the measurement arrangement. There is information available about organic and inorganic products and their spectral reflection within near infrared (NIR). Within our focus of research, existing information related to food is mostly about products and their quality, especially, microbial spoilage, freshness, and ripening. Meat, fruits, and dairy are the most analyzed products in this wavelength region. The quantity of sugar, carbohydrates, and fat is essential for the investigations related to nutrients in a product. The major trend in the area of sensors (for optical spectroscopic measurements) is the miniaturization and integration of functions, separating out expensive assembly needs. On the other hand, there is a need for increasing performance. Resolution and wavelength have to match the applied chemometric models with an acceptable signal-to-noise ratio. The availability of new, better, and cheaper spectral sensors will directly influence the market of automated sorting technology. The current focus is on simple and standardized solutions that use sensor technology within the wavelength of visible light. Chinese manufacturers, especially, play an increasingly important role in this development. To gain all this new scientific knowledge, a broad, sophisticated community of scientists with their institutes is necessary. All of them are connected via a global virtual science network. A key question is an understanding of the primary drivers and the outlook for a future infrastructure. Besides spectral information, one way to gain additional information about products is to combine them, e.g., with volume knowledge.