Guan Xiao, Gu Fang-Qing, Liu Jing, Yang Yong-Jian
State Key Laboratory of Dairy Biotechnology, Shanghai 01103, China.
School of Medical Instruments and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Oct;33(10):2621-4.
Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.07%. Milk powder principal component regression model based on SIMCA was established, and used to classify the milk powder samples in prediction sets. The results showed that the recognition rate of Guangming milk powder, Netherlands milk powder and Nestle milk powder was 78%, 75% and 100%, the rejection rate was 100%, 87%, and 88%, respectively. Therefore, the near infrared spectroscopy combined with SIMCA model can classify milk powder with high accuracy, and is a promising identification method of milk powder variety.
本文将近红外光谱漫反射模式与类相关软独立建模(SIMCA)相结合,研究了几种不同品牌奶粉的溯源问题。采集了138个样品的近红外光谱,其中包括54个光明奶粉样品、43个荷兰奶粉样品、33个雀巢奶粉样品和8个伊利奶粉样品。对训练集中的全光谱数据变量进行预处理后,进行主成分分析,前三个主成分的累积方差贡献率约为99.07%。建立了基于SIMCA的奶粉主成分回归模型,并用于对预测集中的奶粉样品进行分类。结果表明,光明奶粉、荷兰奶粉和雀巢奶粉的识别率分别为78%、75%和100%,拒识率分别为100%、87%和88%。因此,近红外光谱结合SIMCA模型能够对奶粉进行高精度分类,是一种很有前景的奶粉品种鉴别方法。