Quantitative Analysis of Multiple Food Additives in Wheat Flour by Raman Hyperspectral Imaging

Abstract

Raman hyperspectral imaging can obtain both the internal Raman signals and the external image information of the sample simultaneously. This study investigated the quantitatively analysis of multiple food additives in wheat flour by using this technology. Raman hyperspectral images of wheat flour containing the three additives, L-ascorbate acid (LAA), azodicarbonamide (ADC) and benzoyl peroxide (BPO), were collected. Raman signals in Raman hyperspectral images were preprocessed by smoothing and baseline correction methods to obtain the corrected image. Chemical images were created to visually identify additive pixels by selecting single-band image corresponding to Raman characteristic peaks of each additive from the corrected image and combining with the threshold segmentation method. The results showed that the chemical image can identify the above three additives in wheat flour. The identified additive pixels have a significant linear relationship with their concentration, and the coefficients of determination of LAA, ADC and BPO in the quantitative model were 0.9858, 0.9868 and 0.9830, respectively. This study indicated that the Raman characteristic peaks and threshold segmentation provide a non-destructive method for quantitative analysis of multiple wheat flour additives in Raman hyperspectral images.