Li Shuailing, Wang Zhian, Shao Qingsong, Fang Hailing, Zhu Jianjun, Wu Xueqian, Zheng Bingsong
1State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300 China.
Zhejiang Research Institute of Traditional Chinese Medicine Co., Ltd., Hangzhou, 310023 China.
J Food Sci Technol. 2018 Sep;55(9):3518-3525. doi: 10.1007/s13197-018-3276-x. Epub 2018 Jul 19.
To determine the authenticity of , this study presents an application of near-infrared spectroscopy and chemometric methods for evaluating adulteration of with two cheaper adulterants, i.e. C. and . Partial least squares discriminant analysis models were built for the accurate classification of authentic and adulterated at 5-100% (w/w) levels. Partial least squares regression models were used to predict the level of adulteration in the . After by compared different spectral pretreatment methods, and using interval PLS and synergy interval PLS for variable selection, optimum models were developed. These results show that the NIR spectroscopy combined with chemometric methods offers a simple, fast, and reliable method for classifying and quantifying the adulteration of .
为确定[具体物质]的真实性，本研究提出了一种应用近红外光谱和化学计量学方法来评估[具体物质]被两种较便宜的掺假物（即C. [具体掺假物1]和[具体掺假物2]）掺假的情况。建立了偏最小二乘判别分析模型，用于对真实的[具体物质]和掺假率为5 - 100%（w/w）的掺假[具体物质]进行准确分类。使用偏最小二乘回归模型预测[具体物质]中的掺假水平。通过比较不同的光谱预处理方法，并使用区间偏最小二乘法和协同区间偏最小二乘法进行变量选择后，开发出了最优模型。这些结果表明，近红外光谱结合化学计量学方法为[具体物质]掺假的分类和定量提供了一种简单、快速且可靠的方法。
