基于拉曼成像技术的面粉中抗坏血酸添加剂定量检测研究

Quantitative Detection of Ascorbic Acid Additive in Flour Based on Raman Imaging Technology

抗坏血酸是一种常见的面粉品质改良剂,用于改善面团的流变学特性及面包的烘焙品质。本研究以面粉中含不同浓度抗坏血酸的混合样品为研究对象,通过拉曼成像技术实现面粉中抗坏血酸的检测、识别和定量分析。分别采集面粉、抗坏血酸和面粉-抗坏血酸混合样品的拉曼图像,确定感兴趣区域及光谱范围,以抗坏血酸拉曼光谱中强度较高且区别于面粉的3处拉曼峰(631,1128和1658 cm^(-1))为依据对混合样品的平均拉曼光谱进行分析,结果显示其不能有效评估面粉中抗坏血酸含量,研究探索对图像中各像素点对应的拉曼光谱进行分析以实现面粉中抗坏血酸的有效检测。以混合样品图像中各像素点拉曼光谱作为校正集、面粉平均拉曼光谱和抗坏血酸平均拉曼光谱的线性组合光谱作为验证集建立偏最小二乘模型,模型的回归系数用于将混合样品的三维拉曼图像重建为二维灰度图像,通过阈值分割实现面粉中抗坏血酸的检测和识别,根据识别结果建立定量分析模型。结果显示,偏最小二乘模型的最高和最低回归系数分别对应于抗坏血酸和面粉的最强拉曼峰,所有回归系数应用于混合样品拉曼图像将其转换为灰度图像后面粉和抗坏血酸的像素点仍难以识别,阈值分割方法将灰度图像转换为用于分类面粉像素和抗坏血酸像素的二值图像实现了面粉中抗坏血酸的有效检测。通过分析各浓度混合样品对应子样品中识别到的抗坏血酸像素点数确定本研究对面粉中抗坏血酸的最低检测浓度为0.01%(100 mg·kg^(-1)),混合样品中抗坏血酸浓度同图像中识别到的抗坏血酸像素点在0.01%~0.20%范围内具有良好的线性关系,决定系数为0.9960。研究结果可为面粉中抗坏血酸添加剂的定量检测提供方法支持,为大规模快速筛查提供了技术参考。

Ascorbic acid is a common flour quality improver,which is used to improve the rheological properties of dough and the baking quality of bread.In this study,the mixed samples containing different concentrations of ascorbic acid in flour were used as the research object,and the detection,identification and quantitative analysis of ascorbic acid in flour were explored by Raman imaging technology.Raman images of flour,ascorbic acid and flour-ascorbic acid mixed samples were collected respectively,and the region of interest and spectral range were determined.The average Raman spectra of the mixed samples were analyzed based on the three Raman peaks(631,1128 and 1658 cm^(-1))of the ascorbic acid Raman spectrum with higher intensity and different from the flour.The results showed that it could not effectively evaluate the content of ascorbic acid in flour.The Raman spectrum corresponding to each pixel in the image was analyzed to detect ascorbic acid effectively in flour.The partial least squares(PLS)model was established by using the Raman spectra of each pixel in the mixed sample image as the correction set and the linear combination spectra of flour average Raman spectra and ascorbic acid average Raman spectra as the verification set.The regression coefficients of the model were used to reconstruct the three-dimensional Raman image of the sample into a two-dimensional grayscale image.The threshold segmentation method was used to classify flour pixels and ascorbic acid pixels in the image,and a quantitative analysis model was established based on the classification results.The results showed that the PLS model’s highest and lowest regression coefficients correspond to the highest Raman peaks of ascorbic acid and flour respectively.After all regression coefficients were applied to Raman images and converted into grayscale images,the flour and ascorbic acid pixels were still difficult to recognise.The threshold segmentation method transforms the gray image into a binary image to classify flour pixels and ascorbic acid pixels,which realizes the effective detection of ascorbic acid in flour.The minimum detection concentration of ascorbic acid in flour in this study was determined to be 0.01%(100 mg·kg^(-1))by analyzing the number of ascorbic acid pixels identified in the corresponding sub-samples of mixed samples with different concentrations of ascorbic acid.There was a good linear relationship between ascorbic acid concentration in the mixed sample,and the identified ascorbic acid pixels in the image in the range of 0.01%~0.20%,and the coefficient of determination was 0.9960.The research results provide method support for the quantitative detection of ascorbic acid additives in flour and provide technical reference for large-scale rapid screening.