Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China.
Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Jun 5;274:121133. doi: 10.1016/j.saa.2022.121133. Epub 2022 Mar 10.
In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.
在这项研究中，拉曼光谱结合卷积神经网络（CNN）和化学计量学用于分别实现对掺有高果糖玉米糖浆、米糖浆、麦芽糖糖浆和混合糖浆的蜂蜜样品的识别和定量。用于分析与单品种糖浆混合的蜂蜜的浅层 CNN 通过掺假浓度将样品分类为四个类别，准确率超过 97%，而同时检测任何类型糖浆掺假的通用 CNN 模型的准确率为 94.79%。与几种化学计量分类算法相比，所建立的 CNN 具有最佳的性能。此外，偏最小二乘回归（PLS）成功预测了与单一糖浆混合的蜂蜜的纯度，其决定系数和预测均方根误差均大于 0.98 且小于 3.50。因此，基于拉曼光谱的方法对蜂蜜产品的食品安全和质量控制具有重要的实际意义。
