小型化可见/近红外光谱仪在叶片和果实无损检测中的研究进展

doi:10.3964/j.issn.1000-0593(2025)10-2720-10

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摘要：叶片的生理指标可反映作物生长状态，果实的理化参数则能表征其品质特征，高效检测叶片和果实的关键指标是实现精细农业的重要前提。可见/近红外（Vis/NIR）光谱通过捕获物质分子振动与电子跃迁信息，同步采集可见光波段和近红外波段信息，可有效地无损检测样品的组成成分和内在结构。目前台式光谱仪成本高、体积大、功耗高，因此难以用于现场检测。商用便携式光谱仪和手持式光谱仪的价格有所降低但智能化水平不高，限制了小型化光谱仪的规模化应用。为实现更具经济性、高效性、灵活性的光谱检测，Vis/NIR光谱仪的小型化成为一个重要的研究方向。近年来，传感器技术和微机电系统（MEMS）的发展推动了光谱仪小型化的演变，数据处理优化和机器学习建模的发展推动了光谱检测精确性的提升。本文对比了小型化Vis/NIR光谱仪开发过程中的结构设计、光谱仪集成、模型迁移等关键技术，分析了预处理、异常值剔除、特征提取等光谱数据处理优化方法，讨论了定性预测模型构建、定量预测模型构建及预测模型评价指标，综述了小型化Vis/NIR光谱仪对叶片理化指标（叶绿素、含氮量、含水量等）检测和果实品质指标（糖度、可滴定酸、颜色等）检测的国内外最新研究进展，总结了在叶片和果实无损检测中存在的问题，并对小型化Vis/NIR光谱仪研究方向作了展望。这些研究成果为Vis/NIR光谱仪技术的发展提供方向性指导，对作物叶片和果实检测领域的应用推广具有重要参考价值。

关键词：可见/近红外光谱；小型化；无损检测；光谱传感器；模型迁移

Abstract：The physiological indicators of leaves reflect crop growth status, while the physicochemical parameters of fruits characterize their quality attributes. Efficient detection of key indicators in leaves and fruits is a crucial prerequisite for achieving precision agriculture. Visible/near-infrared (Vis/NIR) spectroscopy can non-destructivelydetect material composition and internal structures by capturing molecular vibrations and electron transition signals, synchronously acquiring spectral information across both visible and near-infrared bands. Currently, benchtopspectrometers are expensive, bulky, and power-consuming, making them difficult to use for on-site detection. The prices of commercial portable spectrometers and handheld spectrometers have decreased, but the level of intelligence is not high, which limits the widespread adoption of miniaturized spectrometers. To achieve more economical, efficient, and flexible spectral detection, the miniaturization of Vis/NIR spectrometers has become a critical research direction. In recent years, the development of sensor technology and microelectromechanical systems (MEMS) has driven the miniaturization of spectrometers. The advancement of data analysis optimization and machine learning modeling has further improved spectral detection accuracy. This paper compared key technologies in the development of miniaturized Vis/NIR spectrometers, including structural design, spectrometer integration, and model transfer. It analyzed spectral data processing optimizing methods, such as preprocessing, outlier removal, and feature extraction. Extraction. The construction of qualitative and quantitative prediction models, as well as evaluation indicators for these models, was discussed. Furthermore, it reviewed the latest domestic and international research progress in applying miniaturized Vis/NIR spectrometers to detect leaf physicochemical parameters (e.g., chlorophyll content, nitrogen levels, water content) and fruit quality indicators (e.g., sugar content, titratable acidity, color attributes). Current drawbacks in non-destructive detection of leaves and fruits were summarized, and future research directions for miniaturized Vis/NIR spectrometers were proposed. These research results provide directional guidance for the development of Vis/NIR spectrometer technology and have important reference value for the application and promotion of crop leaf and fruit detection.

Key words：Visible/Near infrared spectroscopy; Miniaturization; Nondestructive detection; Spectral sensor; Model transfer

收稿日期: 2025-03-13 修订日期: 2025-06-20

中图分类号:

TG115.3+39

基金资助: 国家自然科学基金项目（32201678），博士科研启动费项目（2452022150），技术开发（委托）项目（TG20250495，TG20250368），陕西省重点研发计划项目（2025NC-YBXM-219）资助

通讯作者: 熊晓林 E-mail: somso99999@163.com

作者简介: 张旭，1991年生，西北农林科技大学葡萄酒学院讲师 e-mail: zhang_xu@nwafu.edu.cn

引用本文:

张旭，谢卓君，覃子权，赵瑞杰，刘文政，白雪冰，熊晓林，刘旭. 小型化可见/近红外光谱仪在叶片和果实无损检测中的研究进展[J]. 光谱学与光谱分析, 2025, 45(10): 2720-2729.
ZHANG Xu, XIE Zhuo-jun, QIN Zi-quan, ZHAO Rui-jie, LIU Wen-zheng, BAI Xue-bing, XIONG Xiao-lin, LIU Xu. Research Progress of Miniaturized Vis/NIR Spectrometers in Leaf and Fruit Nondestructive Detection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2720-2729.

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