University of Tehran Press Iranian Journal of Biosystem Engineering 2008-4803 56 4 2025 12 22 Classification of Final Maillard Reaction Products in Protein-Polysaccharide Conjugates Using Hyperspectral Imaging and Machine Learning Models Classification of Final Maillard Reaction Products in Protein-Polysaccharide Conjugates Using Hyperspectral Imaging and Machine Learning Models 107 123 105375 10.22059/ijbse.2025.403611.665622 FA Mohammad Hossein Nargesi . Biosystems Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran Somayeh Aziznia Department of Food Science and Hygiene, Faculty of Veterinary Science, Ilam University, Ilam, Iran. Kamran Khairalipour Biosystems Mechanical Engineering Department, Faculty of Agriculture, Ilam University, Ilam, Iran Mohammad Kord Department of Food Science and Hygiene, Faculty of Veterinary Science, Ilam University, Ilam, Iran Zahra Kavyani . Department of Food Science and Hygiene, Faculty of Veterinary Science, Ilam University, Ilam, Iran Journal Article 2025 10 04    The Maillard reaction is a chemical reaction between free amino groups in proteins and carbonyl groups of reducing sugars. The formation of a covalent bond between protein and carbohydrate, known as a protein-saccharide conjugate or conjugate compounds, improves the functional properties of proteins and is effective in developing and enhancing the flavor and color of foods. However, without precise control, there are concerns about the formation of compounds harmful to human health. Therefore, optimizing the reaction conditions to leverage its benefits and minimize harmful compounds is essential. In this research, whey protein concentrate and beta-glucan were conjugated at different temperatures, and the final Maillard reaction products were assessed using UV-visible spectrophotometry. The data were processed using Principal Component Analysis and machine learning algorithms, including Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The KNN algorithm demonstrated superior performance, achieving a classification accuracy of 91.04%. The SVM model, employing "one-vs-one" and "one-vs-rest" strategies, attained accuracies of 87.88% and 84.85%, respectively, while the RF model yielded the lowest accuracy (77.20%). Spectral analysis confirmed that increased temperature led to a significant formation of final Maillard products. The machine learning models based on spectral data successfully enabled the precise discrimination of samples based on process temperature. In summary, this study demonstrated that the proposed approach of integrating UV-visible spectroscopy with machine learning possesses significant potential as a fast, non-destructive, and efficient method for monitoring the Maillard reaction and optimizing thermal processes in the food industry    The Maillard reaction is a chemical reaction between free amino groups in proteins and carbonyl groups of reducing sugars. The formation of a covalent bond between protein and carbohydrate, known as a protein-saccharide conjugate or conjugate compounds, improves the functional properties of proteins and is effective in developing and enhancing the flavor and color of foods. However, without precise control, there are concerns about the formation of compounds harmful to human health. Therefore, optimizing the reaction conditions to leverage its benefits and minimize harmful compounds is essential. In this research, whey protein concentrate and beta-glucan were conjugated at different temperatures, and the final Maillard reaction products were assessed using UV-visible spectrophotometry. The data were processed using Principal Component Analysis and machine learning algorithms, including Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The KNN algorithm demonstrated superior performance, achieving a classification accuracy of 91.04%. The SVM model, employing "one-vs-one" and "one-vs-rest" strategies, attained accuracies of 87.88% and 84.85%, respectively, while the RF model yielded the lowest accuracy (77.20%). Spectral analysis confirmed that increased temperature led to a significant formation of final Maillard products. The machine learning models based on spectral data successfully enabled the precise discrimination of samples based on process temperature. In summary, this study demonstrated that the proposed approach of integrating UV-visible spectroscopy with machine learning possesses significant potential as a fast, non-destructive, and efficient method for monitoring the Maillard reaction and optimizing thermal processes in the food industry https://ijbse.ut.ac.ir/article_105375_b1b25865a712f107798438b89a844d23.pdf