School of Automation, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing 100086, China.
School of Computer Science and Information Security, Guilin University of Electronic Technology, No.1 Jinji Road, Qixing District, Guilin 541004, China.
Sensors (Basel). 2021 Feb 5;21(4):1088. doi: 10.3390/s21041088.
Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions.
药物检测和鉴定技术在药物监管和管理中具有重要意义。为了确定药物的确切来源，通常需要直接识别多个制造商生产的多种药物。在这些情况下，通常使用近红外光谱（NIR）结合化学计量学。然而，现有的 NIR 分类建模方法在处理大量类别和光谱方面存在很大的局限性，特别是在样本不足、样本不平衡和敏感识别错误成本的前提下。因此，本文提出了一种基于改进的双向生成对抗网络（Bi-GAN）的 NIR 多分类建模方法。它充分利用了 Bi-GAN 的强大特征提取能力和良好的样本生成质量，并使用生成的具有明显特征、类别之间数量相等、类别内数量充足的样本来替代光谱分类过程中不平衡和不足的真实样本。本文以 29 家制造商生产的四种药物的 1721 个样本作为实验材料，结果表明，该方法在药物 NIR 分类场景中优于其他对比方法，在理想条件下，最优准确率甚至超过 99%。
