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首页> 外文期刊>The European physical journal. Applied physics >Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS
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Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

机译：利用NIRS研究真假山参的无损鉴别

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摘要

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.
机译：提出了一种利用近红外光谱技术对真野生人参与假冒伪劣人进行无损鉴别的新方法。判别分析和反向传播人工神经网络（BP-ANN）均被用于判别模型的建立。根据伪造样品的异常光谱信息确定最佳建模波长。通过对各种真人参和假冒野山人参样品的主成分分析（PCA），获得了主成分方差的累积百分比，可作为确定主成分（PC）因子的参考。判别分析的识别率为88.46％。以样本的真实值作为输出，构建了一个三层的BP-ANN模型，该模型产生了100％的更高识别精度。总体结果充分证明，NIRS结合BP-ANN分类算法在人参识别方面比判别分析更好，可作为食品和制药业中伪造野生人参的快速，无损检测方法。

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