近红外光谱技术用于豆浆粉品牌与假冒豆浆粉的鉴别

采用近红外光谱分析技术结合化学计量学方法研究对不同品牌的豆浆粉以及假冒的豆浆粉鉴别的可行性。采集不同品牌豆浆粉以及假冒豆浆粉在12 500～4 000 cm-1范围内光谱，并进行不同的预处理。采用偏最小二乘-判别分析(partial least squares-discriminant analysis, PLS-DA)对不同预处理的光谱进行建模比较，去趋势算法(De-trending)预处理光谱与多元散射校正(multiplicative scatter correction, MSC)结合De-trending(MSC+De-trending)预处理光谱的PLS-DA模型预测集判别正确率最高, 均为100%。采用x-loading weights方法分别基于De-trending和MSC-De-trending预处理光谱选择了6个和7个特征波数，并以特征波数分别建立了线性判别分析(linear discriminant analysis, LDA)和误差反向传播神经网络(back-propagation neural network, BPNN)的判别分析模型。结果表明，以所选出的不同的特征波数建立的BPNN判别分析模型取得了最佳的判别效果，建模集和预测集的判别正确率均为100%。采用近红外光谱分析技术可以准确的判别豆浆粉品牌以及假冒豆浆粉产品。

Application of Near-Infrared Spectroscopy to Distinguish Brands of Soy Milk Powder and Fake Soy Milk Powder

Near-infrared spectroscopy combined with chemometrics was used to investigate the feasibility of identifying different brands of soymilk powder and the counterfeit soymilk powder products. For this purpose, partial least squares-discriminant analysis (PLS-DA), linear discriminant analysis (LDA) and back-propagation neural network (BPNN) were employed as pattern recognition methods to class ify soymilk powder samples. The performances of different pretreatments of raw spectra were also compared by PLS-DA. PLS-DA models based on De-trending and multiplicative scatter correction (MSC)combined with De-trending(MSC+De-trending) spectra obtained best results with 100% prediction accuracy, respectively. Six and seven optimal wavenumbers selected by x-loading weights of the best two PLS-DA models were used to build LDA and BPNN models. Results showed that BPNN performed best and correctly classified 100% of the soymilk powder samples for both the calibration and the prediction set. The overall results indicated that NIR spectroscopy could accurately identify branded and counterfeit soymilk powder products.