College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Beijing Key Laboratory of Flavor Chemistry, Beijing Technology and Business University (BTBU), Beijing 100048, China.
Molecules. 2021 Feb 1;26(3):749. doi: 10.3390/molecules26030749.
Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.
近红外光谱（NIRS）结合模式识别技术已成为一种重要的非破坏性判别方法。本综述首先介绍了基于近红外光谱的定性分析过程的基本结构。然后，研究了 NIRS 数据处理的主要预处理方法。介绍了基于 NIRS 的传统模式识别方法的原理和最新进展，包括一些浅层学习机和聚类分析方法。此外，还调查了新开发的深度学习方法及其在食品质量分析中的应用，包括卷积神经网络（CNN）、一维 CNN 和二维 CNN。最后，比较了基于 NIRS 的这些模式识别技术的几种应用。还回顾了现有模式识别方法的不足和未来的研究方向。
