近红外光谱通用模型在农产品及食品检测中的研究进展

doi:10.3964/j.issn.1000-0593(2022)11-3355-06

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摘要：我国人口基数大，农产品和食品的需求量多。农产品和食品的质量与安全与人们的日常生活息息相关。如何实现使用无损、快速、环境友好型、高通量的检测方法对农产品和食品的品质进行检测，是当代社会的发展需要。传统的检测分析方法存在着耗时耗力、检测的样品不能再次出售、产生次品漏检的现象等缺点。近红外光谱分析技术作为一种快速无损的检测手段，逐渐被一些学者以及相关行业人员所重视。然而，近红外光谱分析方法大多数只针对于单一物料建立数学模型。对于数量庞大且种类众多的农产品和食品而言，如不同地域、不同年份、不同温度、不同加工方法、不同成分组成甚至是不同品种，这种相对传统的近红外分析方法无疑会增加建模的工作量。随着计算机技术、光谱仪硬件、化学计量学以及互联网技术的发展，相关学者已经开始着手于近红外光谱通用模型的研究与开发来解决这一问题，即建立一个近红外通用模型，能够对多种物料的同一指标或多种指标进行检测。相比于传统的近红外光谱模型，通用模型具有建模成本低、工作量小等优点，特别使近红外光谱技术在农产品和食品领域中应用以及推广方面具有重要的意义。针对近红外光谱通用模型在农产品和食品检测中的研究进行综述，通过比较传统模型建模方法与通用模型建模方法，分别就建立通用模型过程中样品信息的获取、模型的建立以及样品信息的预测三大建模步骤中使用的方法进行总结，并归纳了近红外光谱通用模型在建模步骤中的要点。近红外光谱通用型模型在农产品以及食品品质检测方面的研究尚处于发展阶段。也提出对于通用模型开发与研究的一些建议，并就近红外光谱通用模型预测方法在检测领域的发展趋势进行了进一步展望。

关键词：近红外光谱；通用模型；农产品及食品；无损检测

Abstract：China has a large population, and the demand for agricultural products and food is great and diverse. Moreover, the quality and safety of agricultural products and foods are closely related to people’s daily life. Therefore, it is the development needs of contemporary society to use nondestructive, rapid, environmentally friendly and high-through put testing methods to detect the quality of agricultural products and foods. The traditional detection and analysis methods have some disadvantages, such as time and labor consumption, the tested samples cannot be sold again after testing and the defective products missing detection. As a rapid and nondestructive detection method, near-infrared spectroscopy (NIR) has been paid more and more attention by some scholars and related industry personnel. However, most NIR analysis methods only build mathematical models for single material. For a large number and variety of agricultural products and foods, such as different regions, different years, different temperatures, different processing methods, different components and even different varieties, this relatively traditional NIR analysis method will undoubtedly increase the workload of preliminary modeling. With the development of computer technology, spectrometer hardware, stoichiometry and internet technology, relevant scholars have begun researching and developing a universal NIR model to solve this problem. That is to establish a near-infrared universal model, which can detect the same index or multiple indexes of various materials. Compared with the traditional NIR model, the universal model has the advantages of low modeling cost and small workload, which makes the application and promotion of NIR spectroscopy technology in agricultural products and foods field of great significance. This paper reviews the research on the universal model of NIR in detecting agricultural products and foods. By comparing the traditional model modeling method with the universal model modeling method, the methods used in the three modeling steps of sample information acquisition, model establishment and sample information prediction in building the universal model are summarized. At the same time, the main points of modeling of NIR universal model in agricultural products and foods detection are summarized. Currently, the research of the NIR universal model in agricultural products and food quality detection is still in the development stage. In this paper, some suggestions on the development and research of the universal model are proposed, and the development trend of the NIR universal model in agricultural products and foods detection has further prospected.

Key words：Near-infrared spectroscopy; Universal model; Agricultural products and foods; Non-destructive detection

收稿日期: 2022-03-27 修订日期: 2022-06-15

中图分类号:

O657.33

基金资助: 国家自然科学基金青年科学基金项目（31901782），天津市农业科技成果转化与推广项目（201901090），湖南省自然科学基金项目（2020JJ5535），天津市大学生创新创业训练计划项目（202010069082）资助

通讯作者: 李明，韩东海 E-mail: liming@tjcu.edu.cn；handh@cau.edu.cn

作者简介: 李明，1988年生，天津商业大学生物技术与食品科学学院讲师 e-mail: liming@tjcu.edu.cn

引用本文:

李明，韩东海，鲁丁强，鲁晓翔，柴春祥，刘文，孙柯璇. 近红外光谱通用模型在农产品及食品检测中的研究进展[J]. 光谱学与光谱分析, 2022, 42(11): 3355-3360.
LI Ming, HAN Dong-hai, LU Ding-qiang, LU Xiao-xiang, CHAI Chun-xiang, LIU Wen, SUN Ke-xuan. Research Progress of Universal Model of Near-Infrared Spectroscopy in Agricultural Products and Foods Detection. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3355-3360.

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