State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2012 Oct;96:252-8. doi: 10.1016/j.saa.2012.05.031. Epub 2012 May 22.
A novel method for the discrimination of the three kinds of Indigowoad Root sample, Radix Isatidis (RI), Rhizoma et Radix Baphicacanthis Cusia (RRBC) and simulated adulterated samples (AD) was researched and developed with the use of near infrared spectroscopy (NIR) and chemometrics. Principal component analysis (PCA) was applied to process the NIR data of 75 collected Indigowoad Root samples, and the three kinds of such sample were discriminated along the first principal component (PC1) axis. In addition, the data pretreatment methods - genetic algorithm-partial least squares (GA-PLS), successive projections algorithm (SPA), and wavelet transform (WT), were employed to select the key analytical wavelengths, and then, these were used as input variables for the three kinds of the pattern recognition method, such as K-nearest neighbor (KNN), radial basis function-artificial neural network (RBF-ANN), least squares-support vector machine (LS-SVM) and back propagation-artificial neural network (BP-ANN). The WT was the method of choice for data pretreatment, and three pretreatment-prediction method combinations performed well (basis: %recognition rate) - WT-KNN (98.2%) and BP-ANN (97.3%) as well as GA-PLS - LS-SVM (97.2). A BP-ANN calibration model was built for the quantitative discrimination of the three types of the complex Indigowoad Root samples, and it was successfully validated.
一种鉴别三种菘蓝根样品（板蓝根 RI、蝙蝠葛 RRBC 和模拟掺假样品 AD）的新方法，利用近红外光谱（NIR）和化学计量学进行了研究和开发。主成分分析（PCA）用于处理 75 个采集的菘蓝根样品的 NIR 数据，三种样品沿第一主成分（PC1）轴进行区分。此外，数据预处理方法——遗传算法-偏最小二乘法（GA-PLS）、连续投影算法（SPA）和小波变换（WT），被用来选择关键分析波长，然后将这些作为三种模式识别方法（如 K-最近邻（KNN）、径向基函数-人工神经网络（RBF-ANN）、最小二乘支持向量机（LS-SVM）和反向传播人工神经网络（BP-ANN）的输入变量。WT 是数据预处理的首选方法，三种预处理-预测方法组合表现良好（基于：%识别率）——WT-KNN（98.2%）和 BP-ANN（97.3%）以及 GA-PLS-LS-SVM（97.2%）。为了对三种复杂的菘蓝根样品进行定量鉴别，建立了一个 BP-ANN 校准模型，并成功进行了验证。
