Detection of Adulteration in Ziziphus Jujuba Mill. Fruit Powder by Fourier-Transform Infrared Spectroscopy Combined with Chemometrics Analysis

Tai, Crystal Wern Chze (2024) Detection of Adulteration in Ziziphus Jujuba Mill. Fruit Powder by Fourier-Transform Infrared Spectroscopy Combined with Chemometrics Analysis. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

Food fraud is a pervasive issue that encompasses deceptive practices throughout the food supply chain. This study investigates adulteration in Ziziphus jujuba Mill. fruit powder using Fourier-transform infrared spectroscopy and chemometrics. Adulteration with cornstarch and powdered sugar jeopardizes food quality and safety. The research aims to develop a reliable method for comparing and detecting adulteration with commercial brands using characteristics measurement, principal component analysis, and partial least squares regression. This approach can safeguard consumer well-being and strengthen trust in the authenticity of jujube products. It also contributes to the development of novel food analysis techniques. This study investigated the adulteration of jujube powder using adulteration levels (0, 2, 4, 6, 8, 10, 15, 20, 25, 30, 100%) of cornstarch, powdered sugar, and the combination of cornstarch and powdered sugar at the ratio of 1:1. Statistical analysis revealed brands A and B exhibited significantly higher total sugar content (p<0.05) and both Brands C and E have a lighter color with more red and yellow tones compared to other brands. FTIR spectral analysis showed similarities among jujube samples (pure and adulterated), O–H peak at 3250 to 3350 cm-1, C–H peak at 2900 to 2950 cm-1, and 975 to 1050 cm-1. PCA models explained the accumulative variance proportion of 85-92%. Brands A, B, and E cluster with cornstarch while Brand C and Brand D cluster with pure jujube. PLSR analysis validates the predicted and references samples of adulteration series (cornstarch, powdered sugar, mix) with R2 values (0.93-0.95) and low prediction errors (RMSE: 0.017-0.088). Future work suggests model refinement using PLS-DA by incorporating specific FT-IR spectral ranges with the eigenvector correlate most to the PC1 for enhanced adulteration classification accuracy for better differentiation