Rapid detection and spatial visualization of wheat flour contamination in rice flour using near-infrared hyperspectral imaging coupled with PLS-DA

Abstract

This study aimed to develop an effective method for detecting wheat flour contamination in rice flour using near-infrared hyperspectral imaging (NIR-HSI), with the goal of supporting raw material quality control and food safety. Rice flour samples contaminated with varying levels (0%–100%) of low-gluten and high-gluten wheat flour were analyzed using NIR-HSI, and classification models were developed using partial least squares–discriminant analysis (PLS-DA). The developed models successfully discriminated between pure rice flour and wheat flour–contaminated rice flour. The selected models based on the original spectra achieved classification accuracies of 98.18% and 100.00% for low-gluten and high-gluten wheat flour, respectively, in the independent prediction set. For the low-gluten wheat flour model, sensitivity, specificity, and F1-score values were 97.78%, 100.00%, and 98.88%, respectively, while all classification metrics for the high-gluten wheat flour model were 100.00%. The original spectra provided comparable or better classification performance than the SNV- and second derivative–pretreated spectra. In addition, color maps generated from the PLS-DA models enabled visualization of the spatial distribution of wheat flour contamination across the investigated concentration range. These findings demonstrate the potential of NIR-HSI combined with PLS-DA as a rapid and non-destructive approach for detecting and visualizing wheat flour contamination in rice flour.

Keywords

rice flour / wheat flour / contamination / near-infrared hyperspectral imaging / predictive model / chemometric

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