The objective of this work is to demonstrate the potential of near infrared (NIR) spectroscopy for common screening of falsified medicines in the field by means of a device-independent universal discrimination approach. In order to provide a useful discrimination tool to protect people from low-quality medical products, not only is a low-cost and portable screening device necessary, but a reference library is also essential. The authors believe that a device-dependent reference library inhibits NIR spectroscopy from becoming a popular screening tool. In this study, to develop a device-independent method, discrimination performance is evaluated using different devices for training and testing. The training data sets for the reference library were prepared using a bench-top FT-NIR spectrophotometer, and predictive discrimination was performed using the spectral data by a low-cost and portable wavelength dispersive NIR spectrophotometer. A NIR spectrum-based support vector machine (SVM) was used for these purposes, but the screening resulted in low accuracy thought to be caused by the intrinsically device-dependent features of the spectra data. Thus, principal component analysis was performed to collect the proper components to discriminate low-quality products from standard products. The principal-component-score-based SVM was able to produce highly accurate results, identifying falsified products with no false positive cases.

这项工作的目的是展示近红外 (NIR) 光谱通过独立于设备的通用鉴别方法在现场常见的伪造药物筛查方面的潜力。为了提供有用的鉴别工具来保护人们免受劣质医疗产品的侵害，不仅需要低成本和便携式的筛查设备，而且参考库也必不可少。作者认为，依赖于设备的参考库阻碍了 NIR 光谱成为流行的筛选工具。在这项研究中，为了开发一种独立于设备的方法，使用不同的设备进行训练和测试来评估辨别性能。参考库的训练数据集是使用台式 FT-NIR 分光光度计准备的，并且通过低成本和便携式波长色散 NIR 分光光度计使用光谱数据进行预测判别。基于 NIR 光谱的支持向量机 (SVM) 用于这些目的，但筛选导致精度低下，这被认为是由光谱数据的固有设备相关特征引起的。因此，进行主成分分析以收集适当的成分以区分低质量产品和标准产品。基于主成分分数的 SVM 能够产生高度准确的结果，在没有假阳性案例的情况下识别伪造产品。但是筛选导致了低准确度，这被认为是由光谱数据的固有设备相关特征造成的。因此，进行主成分分析以收集适当的成分以区分低质量产品和标准产品。基于主成分分数的 SVM 能够产生高度准确的结果，在没有假阳性案例的情况下识别伪造产品。但筛选导致了低准确度，这被认为是由光谱数据的固有设备相关特征造成的。因此，进行主成分分析以收集适当的成分以区分低质量产品和标准产品。基于主成分分数的 SVM 能够产生高度准确的结果，在没有假阳性案例的情况下识别伪造产品。