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To thrive as a global civilization, food production must meet the demands of our ever-growing population. There are more than a billion people on the planet suffering from malnutrition through poor quality or lack of food. Nutrient content of food can be determined by a variety of methods, which have issues such as slow analysis or sample destruction. Near-infrared (NIR) spectroscopy is a long-standing alternative to these methods. In this work, we demonstrated that Raman spectroscopy (RS), another spectroscopic method, can also be used to assess the nutrient content of maize ( Zea mays ), one of the most widely cultivated grains in the world. Using a handheld Raman spectrometer, we predicted the content of carbohydrates, fibers, carotenoids, and proteins in six different varieties of maize. This analysis requires only a single maize kernel and is fast (1s), portable, noninvasive, and nondestructive. Moreover, we showed that RS in combination with chemometric methods can be used for highly accurate (approximately 90%) spectroscopic typing of maize, which is important for plant breeders and farmers. Finally, we demonstrate that Raman-based approach is as accurate as NIR analysis. These findings suggest that portable Raman systems can be used on combines and grain elevators for autonomous control of grain quality.

Rapid and Noninvasive Typing and Assessment of Nutrient Content of Maize Kernels Using a Handheld Raman Spectrometer