基于近红外光谱特征的三文鱼品质多指标快速检测

doi:10.3964/j.issn.1000-0593(2019)07-2244-06

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Abstract Salmon is expensivebut popular among consumers because of its good-taste, sweet flavor and high nutritional values. The import volume of salmon in 2017 reaches 350 million dollars. The problems of selling shoddy salmon for quality salmon by unscrupulous businessmen, who are pursuing high profit only, become more and more serious. The problems can be mainly manifested by the following steps: (1) Using fresh water rainbow trout with low price and similar appearance like Amur salmon, Pacific salmon to masquerade Norwegian salmon that of high price and high consumer acceptance; (2) Replacing high cost and high quality fresh salmon (stored in 0～4 ℃, with short shelf life, on ice crystal produced and longest maintaining flavor and taste) with low cost and low quality frozen-thawed substitute (stored in -18 ℃, with long shelf life, destroyed organizational structure by ice crystal and destroyed flavor); (3) Selling stale salmon as the fresh ones. Therefore, considering the disadvantages of big error in sensory detection of salmon quality as well as the time consumption in physical and chemical testing, the article intends to research a fast identification method for genuine and counterfeit salmon, fresh and frozen-thawed salmon as well as fresh and sub-fresh salmon based on near infrared spectral characteristics. Firstly, genuine and counterfeit salmon samples were taken from Norwegian salmon and fresh water rainbow trout, Amur salmon, Pacific salmon; fresh and frozen-thawed salmon samples were taken from fresh salmon with chilling for 1, 3 and 5 d and frozen-thawed salmon with frozen for 15, 30 and 45 d; fresh and sub-fresh salmon samples were taken from fresh salmon with 0, 2, 4, 6 and 8 d storage. Secondly, NIRs information was collected, meanwhile, the salmon with different storage days were analyzed by national standard method for determination of the TVB-N. Thirdly, the different pre-processing methods (Standard normal variate transformation，Vector normalization，Multiplicative scatter correction，Savitzky-Golay，First derivative，Second derivative) were employed, then Principal component analysis (PCA) and Genetic algorithms (GA) were used to reduce the spectral and the excess spectral bands. Finally, K-nearest neighbors (KNN) and Least-squares support vector machine (LS-SVM) models were used for the construction of identification model of genuine and counterfeit salmon as well as fresh and frozen-thawed salmon; the prediction spectra were constructed associated with their corresponding TVB-N using Synergy Interval Partial Least Square Method (Si-PLS). Modeling results show that for genuine and counterfeit salmon, the spectral information were treated with SNV and PCA, the LS-SVM model recognition rate of the testing set is 97.50%; for fresh and frozen-thawed salmon, the spectral information were treated with SNV and PCA, the LS-SVM model recognition rate of the testing set is 98.89%; for fresh and sub-fresh salmon, the spectral information were treated with SNV, the feature spectra were associated with their corresponding TVB-N using Si-PLS, the Si-PLS model correlation coefficient of the validation set is 0.864 1, the Si-PLS model recognition rate of the testing set is 90.00%. According to research results, using combination of NIR spectroscopy and chemometrics, genuine and counterfeit salmon, fresh and frozen-thawed salmon, as well as fresh and sub-fresh salmon, can be detected quickly and non-destructively, thus realizing the rapid and multi-index detection of salmon quality.

Key words：Salmon quality; NIRs; Chemometrics; Multi-index; Rapid detection

Received: 2018-06-05 Accepted: 2018-10-16

ZTFLH:

TS254.7

Corresponding Authors: ZOU Xiao-bo E-mail: zou_xiaobo@ujs.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)07-2244-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I07/2244

[1] ZHAO Hai-jun, LI Hong-quan, ZHONG Yan, et al(赵海军，李红权，种炎，等). Journal of Food Safety & Quality(食品安全质量检测学报), 2015, 6(10): 3947.
[2] WANG Shou, XIE Jing, YANG Kai, et al(王硕，谢晶，杨凯，等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2018, 18(5): 173.
[3] WANG Zun, XIE Jing(王尊，谢晶). Packaging Engineering(包装工程), 2016, 37(9): 80.
[4] Shi Jiyong, Chen Wu, Zou Xiaobo, et al. Spectrochimica Acta Part A: Molecular and Biomoleadar Spectroscopy, 2018, 188: 436.
[5] Shi Jiyong, Zou Xiaobo, Huang Xiaowei, et al. Food Chemistry, 2013, 138(1): 192.
[6] MENG Zhi-juan, ZHOU Yu-fang, YANG Hui-cheng, et al(孟志娟，周宇芳，杨会成，等). Food Science and Technology(食品科技), 2013, 38(12): 294.
[7] Komivi Dossa, Xin Wei, Marème Niang, et al. The Crop Journal, 2018, 6(2): 202.
[8] YANG Zhi-min, DING Wu(杨志敏，丁武). Meat Research(肉类研究), 2011, 25(2): 25.
[9] ZHU Feng-le, HE Yong, SHAO Yong-ni, et al(朱逢乐，何勇，邵咏妮，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(1): 113.
[10] Shi Jiyong, Zhang Fang, Li Zhihua, et al. Analytical Methods, 2017, 9(8): 1297.
[11] SHI Ji-yong, HU Xue-tao, ZHU Yao-di, et al(石吉勇，胡雪桃，朱瑶迪，等). Transactions of the Chinese Society for Agriculture Machinery(农业机械学报), 2016, 47(2): 245.
[12] Huang M, Wang Q, Zhang M, et al. Journal of Food Engineering, 2014, 128(1): 25.
[13] Mohd Said M, Gibbons S, Moffat A C, et al. Food Chemistry, 2014, 158(1): 296.
[14] YAN Yan-lu, CHEN Bin, ZHU Da-zhou(严衍禄，陈斌，朱大洲). Near Infrared Spectroscopy Analytical-Principles, Technology and Application(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社), 2013.
[15] Shi Jiyong, Zhang Fang, Wu Shengbin, et al. Food Chemistry, 2019, 274: 925.