Review Of Near Infrared Spectrum Detection Based on Deep Learning

Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and characteristics, quantitative testing, non-destructive testing, and so on. With the progress of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged, which include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, and drug analysis. This article provides a systematic review of domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. It discusses the research significance of near-infrared spectroscopy detection based on deep learning. It also reviews the latest progress in deep learning based near-infrared spectroscopy related technologies, elaborates on the advantages, disadvantages, and applicable fields of the methods, and makes prospects and predictions for the future development trends in this field.