**3. Conclusions**

Based on the reported studies, it was possible to develop a robust classification, authentication or fraud detection model for rice samples considering their specific physicochemical properties and using machine learning tools such as PLS-DA, KNN, ANN, and SVM among other methodologies applied to NIR spectroscopy data, revealing the pattern and relationship of each variety and chemical similarities, according to their specific properties. The classification models developed using several models allow to classify with high confidence rice varieties using the spectral data. The results show that the use of these chemometric tools, combined with spectroscopy capabilities, can facilitate the process of classification and identification of different rice types. The rice discrimination by their origin, harvest season, state of conservation as well as the presence of contaminants and adulteration issues based on robust classification methods can facilitate the creation of a data base, a useful tool for rice authenticity that can increase the confidence and producer-consumer engagement in rice-based foods.
