基于高光谱技术的有机鸡蛋与普通鸡蛋鉴别

doi:10.3964/j.issn.1000-0593(2022)04-1222-07

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Abstract 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.

Key words：Eggs; Organic food; Hyperspectral technology; Spectral dimension reduction; Identification

Received: 2021-03-04 Accepted: 2021-10-29

ZTFLH:

S123

Corresponding Authors: ZHU Shi-ping E-mail: zspswu@126.com

Cite this article:

MA Ling-kai,ZHU Shi-ping,MIAO Yu-jie, et al. The Discrimination of Organic and Conventional Eggs Based on Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1222-1228.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)04-1222-07 OR https://www.gpxygpfx.com/EN/Y2022/V42/I04/1222

[1] IFOAM-Organic International. The World of Organic Agriculture Statistics and Emerging Trends 2020(2020年世界有机农业概况与趋势预测). Translated by Beijing Organic and Beyond Corporation[正谷（北京）农业发展有限公司，译]. Beijing：China Agricultural Science and Technology Press(北京：中国农业科学技术出版社)，2020.
[2] LU Cheng-ren(卢成仁). Journal of Poyang Lake(鄱阳湖学刊)，2020，(4)：104.
[3] European Commission, COMMISSION IMPLEMENTING REGULATION (EU) No 354/2014 of 8 April 2014 Amending and Correcting Regulation (EC) No 889/2008 Laying Down Detailed Rules for the Implementation of Council Regulation (EC) No 834/2007 on Organic Production and Labelling of Organic Products With Regard to Organic Production, Labelling and Control. The European Commission，2014.
[4] GB/T 19630—2019, Organic Products-Requirements for Production, Processing, Labeling and Management System. National Standards of the People’s Republic of China(GB/T 19630—2019，有机产品生产、加工、标识与管理体系要求. 中华人民共和国国家标准).
[5] ZHANG Feng，LI Bao-pu，ZHANG Xin-tong，et al（张峰，李保普，张新同，等）. China Poultry（中国家禽），2006，28(1)：27.
[6] Giannenas I，Nisianakis P，Gavriil A，et al. Food Chemistry，2009，114(2)：706.
[7] Borges E M，Volmer D A，Gallimberti M，et al. Analytical Methods，2015，7(6)：2577.
[8] Bologa M，Pop I M，Gabriela C，et al. Journal of Biotechnology，2014，185：S72.
[9] Rogers K M，Ruth S V，Alewijn M，et al. Agricultural and Food Chemistry，2015，63：8372.
[10] Ruth S V，Alewijn M，Rogers K，et al. Food Chemistry，2011，126：1299.
[11] Ackermann S M，Lachenmeier D W，Kuballa T，et al. Magnetic Resonance in Chemistry，2019，57(9)：579.
[12] Puertas G，Vázquez M. Food Chemistry，2019，288：8.
[13] Mahesh S，Jayas D S，Paliwal J，et al. Journal of Stored Products Research，2015，61：17.
[14] Zhang Wei，Pan Leiqing，Tu Sicong，et al. Journal of Food Engineering，2015，157：41.
[15] PAN Lei-qing，ZHANG Wei，YU Min-li，et al（潘磊庆，张伟，于敏莉，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报），2016，32(1)：181.
[16] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing：Chemical Industry Press(北京：化学工业出版社)，2011.
[17] Yang Yue，Wu Yongjiang，Li Weili，et al. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy，2018，191：233.
[18] WANG Xue-song，CHENG Yu-hu，KONG Yi，et al(王雪松，程玉虎，孔毅，等). Dimensionality Reduction for Hyperspectral Remote Sensing Data(高光谱遥感数据降维). Beijing：Science Press(北京：科学出版社)，2017.
[19] Wang Weiting，Yun Yonghuan，Deng Baichuan，et al. RSC Advances，2015，5(116)：95771.
[20] Araujo M C U，Saldanha T C B，Galvao R K H，et al. Chemometrics and Intelligent Laboratory Systems，2001，57：65.
[21] Li Hongdong，Liang Yizeng，Xu Qingsong，et al. Analytica Chimica Acta，2009，648(1)：77.