Quantitative analysis of BPO additive in flour via Raman hyperspectral imaging technology

Abstract

Raman hyperspectral imaging technology not only can acquire the image information of the sample; it also contains the Raman spectra information about each pixel. Due to the abundant information that the method provides, it has been applied to detect food safety. This study adopted line-scan Raman hyperspectral technology to quantify benzoyl peroxide (BPO) additive in flour. By analyzing the Raman spectra of BPO and flour, the 999 cm−1 Raman peak was selected for the detection and identification of BPO in flour. Savitzky–Golay filter and adaptive iteratively reweighted penalized least squares (airPLS) methods were used to de-noise and fluorescence correction of the original Raman signals. Binary image was established by 999 cm−1 single-band correction image and threshold segmentation, and this method was used to detect 11 mixture samples with different BPO additive concentrations. The results show that the BPO additives in the mixture samples can be detected, and the detected BPO pixels had a good linear relationship with the concentration of BPO in the mixture samples, correlation coefficient was 0.9902. The above results indicated that the method established in this paper can be applied to non-destructive quantitative detection of BPO additive in flour.