Adulterant estimation in paprika powder using deep learning and chemometrics through near-infrared spectroscopy

Spices and other food products have been permanently susceptible to adulteration, affecting safety and acceptability when commercialized. A relevant alternative to detect contaminants in food products is to couple near-infrared spectroscopy (NIR) with chemometrics. Among the most accurate chemometric techniques employed to analyze food products, partial least squares regression (PLSR) combines features from and generalizes principal component analysis (PCA) to create compact and accurate models. Other techniques inspired in the human brain, such as multilayer perceptron, the long short-term memory (LSTM) models, and other approaches based on deep learning, take advantage of the high complexity of weights and neurons to train models based on large amounts of data. In this paper, a methodology is proposed to evaluate chemometric tools to estimate the percentage of adulterants in paprika powder using NIR spectroscopy, and three approaches are proposed and compared showing different performances. According to the methodology, the paprika samples were dried and separated into pericarp, peduncle, and seed cake. The resulting elements were finely milled, sieved, and mixed into 21 different combinations with a different percentage of each. Spectral profiles were used to train PLSR, multilayer perceptron, and regression models based on LSTM networks. The models were compared following a k-fold cross-validation strategy. Results showed that PLSR presented the highest \(R^2=0.978\) $R^2=0.978$ for peduncle adulterant estimation, and the lowest \(RMSE=6.24\) $RMSE=6.24$ . In particular, when seed cake powder was used as an adulterant, the PLSR approach showed the highest \(R^2=0.981\) $R^2=0.981$ , and the lowest \(RMSE=5.806\) $RMSE=5.806$ . The RPD values were higher than 2.000 for all models that use the peduncle as an adulterant and only for models bound to the PLSR in the adulterated samples with pressed seed cake. In summary, the best predictions were obtained using PLSR models, providing evidence of the feasibility of using NIR spectra to estimate the percentage of adulterants in paprika powder.