Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam

Lissovoy D. Zakeryanova A. Orazbayev R. Rakhimzhanova T. Lewis M. Varol H.A. Chan M.-Y.
November 2025 Multidisciplinary Digital Publishing Institute (MDPI)

Foods
2025 #14 Issue 21

Apple jam is a widely used all-season product. The quality of the jam is closely related to its sugar concentration, which affects its taste, texture, shelf life, and legal compliance with production requirements. Although traditional methods for measuring sugar, such as titration, enzymatic methods, and chromatography, are accurate, they are also invasive, destructive, and unsuitable for rapid screening. This study investigates a non-destructive and non-invasive alternative method that uses hyperspectral imaging (HSI) in combination with machine learning to estimate the sugar content in processed apple products. Eight cultivars were selected from the Central Asian region, recognized as the origin of apples and known for its rich diversity of apple cultivars. A total of 88 jam samples were prepared with sugar concentrations ranging from 25% to 75%. For each sample, several hyperspectral images were obtained using a visible-to-near-infrared (VNIR) camera. The acquired spectral data were then processed and analyzed using regression models, including the support vector machine (SVM), eXtreme gradient boosting (XGBoost), and a one-dimensional residual network (1D ResNet). Among them, ResNet achieved the highest prediction accuracy of R² = 0.948. The results highlight the potential of HSI and machine learning for a fast, accurate, and non-invasive assessment of the sugar content in processed foods.

apple jam , food adulteration , food authenticity , hyperspectral imaging , machine learning , non-destructive analysis , ResNet , sugar concentration , Support Vector Machine , XGBoost

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School of Engineering and Digital Sciences, Nazarbayev University, Astana, 010000, Kazakhstan
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Biomedical Sciences, School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan

School of Engineering and Digital Sciences
Institute of Smart Systems and Artificial Intelligence
Department of Biomedical Sciences

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