The Discrimination of Organic and Conventional Eggs Based on Hyperspectral Technology

被引:1
作者
Ma Ling-kai [1 ]
Zhu Shi-ping [1 ]
Miao Yu-jie [1 ]
Wei Xiao [1 ]
Li Song [1 ]
Jiang You-lie [1 ]
Zhuo Jia-xin [1 ]
机构
[1] Southwest Univ, Coll Engn & Technol, Chongqing 400716, Peoples R China
关键词
Eggs; Organic food; Hyperspectral technology; Spectral dimension reduction; Identification; SPECTROSCOPY; QUALITY;
D O I
10.3964/j.issn.1000-0593(2022)04-1222-07
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
Today, the organic food industry is developing rapidly around the world. It reflects consumers' attention to the quality and safety of food. Organic eggs are produced under strict conditions, and it have nutrition, so the more price it is compared with conventional eggs. Although there are some strict certification processes to the eggs sold in the market, they still cannot prevent illegal elements from making profits by replacing organic eggs with conventional eggs. This phenomenon harms the interests of organic producers, and consumers will have less faith in organic food. Therefore, an effective non-destructive method is needed to identify the organic eggs from conventional eggs. One material' s inner information can be obtained by hyperspectral transmission image technology. In this paper, the organic eggs and conventional eggs were used as the experimental objects, and hyperspectral image data of egg samples were collected in the wavelength range from 364 to 1 025 nm, and the average spectral of the ROI in the area of albumen and yolk were abstracted from the collected data respectively. According to the transmission spectrum curves, bands with obvious differences in spectral response between organic eggs and conventional eggs were selected out. The Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were used to establish the discrimination model. The results show that the accuracy of the four models based on the yolk and albumen area respectively are closed, further analysis was carry on the datas of yolk area. Due to a large amount of hyperspectral data and redundant information, it is inconvenient for data storage, transmission and modeling. Therefore, Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to reduce the dimensions of data. After removing a lot of redundant information, the SPA-SVM discrimination model based on 23 wavelengths selected by using SPA on the hyperspectral data of yolk area has the highest accuracy, reaching 94. 2%. The results show that the hyperspectral technique has some effect on the non-destructive identification of organic eggs and conventional eggs by hyperspectral technique has some effect.
引用
收藏
页码:1222 / 1228
页数:7
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