Multi-manufacturer drug identification based on near infrared spectroscopy and deep transfer learning

• Main Authors: Lingqiao Li, Xipeng Pan, Wenli Chen, Manman Wei, Yanchun Feng, Lihui Yin, Changqin Hu, Huihua Yang
• Format: Article
• Language: English
• Published: World Scientific Publishing 2020-07-01
• Series: Journal of Innovative Optical Health Sciences
• Subjects: near-infrared spectroscopy; transfer learning; drug identification; multi-manufacturer
• Online Access: http://www.worldscientific.com/doi/pdf/10.1142/S1793545820500169

Near infrared (NIR) spectrum analysis technology has outstanding advantages such as rapid, nondestructive, pollution-free, and is widely used in food, pharmaceutical, petrochemical, agricultural products production and testing industries. Convolutional neural network (CNN) is one of the most successful methods in big data analysis because of its powerful feature extraction and abstraction ability, and it is especially suitable for solving multi-classification problems. CNN-based transfer learning is a machine learning technique, which migrates parameters of trained model to the new one to improve the performance. The transfer learning strategy can speed up the learning efficiency of the model instead of learning from scratch. In view of the difficulty in acquisition of drug NIR spectral data and high labeling cost, this paper proposes three simple but very effective transfer learning methods for multi-manufacturer identification of drugs based on one-dimensional CNN. Compared with the original CNN, the transfer learning method can achieve better classification performance with fewer NIR spectral data, which greatly reduces the dependence on labeled NIR spectral data. At the same time, this paper also compares and discusses three different transfer learning methods, and selects the most suitable transfer learning model for drug NIR spectral data analysis. Compared with the current popular methods, such as SVM, BP, AE and ELM, the proposed method achieves higher classification accuracy and scalability in multi-variety and multi-manufacturer NIR spectrum classification experiments.