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    Learning near-infrared spectroscopy detection
    Publisher：Shanghai Jinghongkepu Optoelectronics Technology Co., Ltd Release time：2025-01-13 15:45:19 Click count：22 Close
    Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and properties, quantitative detection, non-destructive testing, etc. With the advancement of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged. These systems include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, drug analysis, and other fields. This article systematically reviews domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. The research significance of near-infrared spectroscopy detection based on deep learning was discussed. This article provides an overview of the latest developments in deep learning based near-infrared spectroscopy related technologies, elaborating on the advantages, disadvantages, and applicable fields of these methods, and offering prospects and predictions for the future development trends in this field.
    Deep learning is based on deep neural networks, which continuously process input data to acquire new knowledge and achieve higher-level learning. Compared with shallow learning, which lacks depth and training is slow, deep learning methods can achieve fast, efficient, and accurate target object recognition, thereby improving recognition and accuracy. Figures 1 and 2 are the most representative network structure diagrams of two different algorithm models.

    Compared with traditional chemometrics, the deep learning based near-infrared spectral image classification algorithm has three main advantages: (1) it has powerful feature extraction capabilities, including super-resolution reconstruction, convolutional neural networks, etc; (2) Capable of achieving sparse representation of near-infrared spectral data; (3) Improved the adaptability and robustness of the algorithm in complex spectral environments. However, it has a large amount of data, high data dimensions, and multiple model parameters, which consume a lot of time and resources during the training process. Moreover, existing small models only focus on a specific field and do not have a universal large model, which puts higher demands on the model algorithm.
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