Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks.

Anbing Zheng,Xipeng Pan,Yanchun Feng,Lihui Yin,Huihua Yang

doi:10.3390/s21041088

Abstract

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.

Highlights

In the drug market, different drugs and different brands will have different pricing.Sellers can use fake packaging on low-cost pharmaceutical products and sell them as high-priced drugs
Near-infrared spectroscopy (NIR) has the advantages of low instrument cost, direct measurement, non-destructive detection, and on-site detection, which is suitable for rapid qualitative and quantitative analysis of drugs [1,2,3]. It is usually combined with chemometrics methods such as partial least squares discriminant analysis (PLS-DA) [3,4,5], linear support vector machine (Linear SVM), and other linear classifiers [6,7,8,9,10] and BP-ANN
The key to its realization lies in the modification of the original Bidirectional Generative Adversarial Networks (Bi-generative adversarial networks [17] (GAN))

Summary

Introduction

Different drugs and different brands will have different pricing.Sellers can use fake packaging on low-cost pharmaceutical products and sell them as high-priced drugs. Near-infrared spectroscopy (NIR) has the advantages of low instrument cost, direct measurement, non-destructive detection, and on-site detection, which is suitable for rapid qualitative and quantitative analysis of drugs [1,2,3]. It is usually combined with chemometrics methods such as partial least squares discriminant analysis (PLS-DA) [3,4,5], linear support vector machine (Linear SVM), and other linear classifiers [6,7,8,9,10] and BP-ANN classifier [10,11] in a classification scenario. Some deep learning methods, such as stack sparse auto-coding (SAE) [12], deep belief network (DBN) [13], deep convolution neural network (CNN) [14], have been reported in drug identification and classification modeling

Methods

Results

Discussion

Conclusion