Abstract Spectroscopy techniques are powerful tools for the rapid identification of traditional Chinese medicine because they provide chemical information with no sample preparation. In this study, a rapid and reliable approach was proposed to differentiate Pinellia ternata from adulterated P. ternata, processed P. ternata, and adulterated processed P. ternata by mid-infrared (MIR) and near-infrared (NIR) spectroscopy coupled with a partial least squares-discriminant analysis (PLS-DA) algorithm. One-hundred sixty-five batches of P. ternata, adulterated P. ternata, processed P. ternata, and adulterated processed P. ternata samples were collected and prepared. All of the samples were characterized by MIR and NIR spectra. The PLS-DA was first applied to build the discriminant model on the individual data matrices. Next, the data matrices coming from MIR and NIR spectra were fused at the low-level and mid-level, and PLS-DA models were built on the fused data. The classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA models. The results showed the use of mid-level fusion strategy, in particular, integrating latent variables from different spectral data matrices, allowed the correct discrimination of all samples in the training and testing sets. In the case of mid-level fusion with latent variables, the accuracy of the PLS-DA model was 100%, and the sensitivity and specificity of the PLS-DA model were all 1. The present discriminant model can be successful to differentiate P. ternata from adulterated P. ternata, processed P. ternata, and adulterated processed P. ternata. This study first provides a new path for the quality control of P. ternata.

中文翻译：

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通过偏最小二乘法的中红外 (MIR) 和近红外 (NIR) 光谱鉴定真正和掺假半夏 - 判别分析 (PLS-DA)

摘要 光谱技术是快速鉴定中药的有力工具，因为它们无需样品制备即可提供化学信息。在这项研究中，提出了一种快速、可靠的方法，通过中红外 (MIR) 和近红外 (NIR) 光谱结合半夏半夏与掺假半夏、加工半夏和掺假加工半夏偏最小二乘判别分析 (PLS-DA) 算法。收集并制备了 165 批 P. ternata、掺假 P. ternata、加工 P. ternata 和掺假加工 P. ternata 样品。所有样品均通过 MIR 和 NIR 光谱表征。PLS-DA 首先应用于在单个数据矩阵上构建判别模型。下一个，来自MIR和NIR光谱的数据矩阵在低层和中层融合，并在融合数据上建立PLS-DA模型。计算分类准确性、敏感性和特异性以评估 PLS-DA 模型。结果表明，使用中级融合策略，特别是整合来自不同光谱数据矩阵的潜在变量，可以正确区分训练和测试集中的所有样本。在有潜变量的中层融合的情况下，PLS-DA模型的准确率为100%，PLS-DA模型的敏感性和特异性均为1。目前的判别模型可以成功区分P。来自掺假 P. ternata、加工 P. ternata 和掺假加工 P. ternata 的 ternata。