基于近红外光谱的淡水鱼新鲜度在线检测方法研究

doi:10.3964/j.issn.1000-0593(2014)10-2732-05

Abstract
References
Related Citation (15)

Download: PDF (1800 KB)
Export: BibTeX | EndNote (RIS)

Abstract In the present study, the near infrared spectrum of freshwater fish was used to detect the freshness on line, and the near infrared spectra on-line acquisition device was built to get the fish spectrum. In the process of spectrum acquisition, experiment samples move at a speed of 0.5 m·s^-1, the near-infrared diffuse reflection spectrum (900~2 500 nm) could be got for the next analyzing, and SVM was used to build on-line detection model. Sample set partitioning based on joint X-Y distances algorithm (SPXY) was used to divide sample set, there were 111 samples in calibration set (57 fresh samples and 54 bad samples), and 37 samples in test set (19 fresh samples and 18 bad samples). Seven spectral preprocessing methods were utilized to preprocess the spectrum, and the influences of different methods were compared. Model results indicated that first derivative (FD) with autoscale was the best preprocessing method, the model recognition rate of calibration set was 97.96%, and the recognition rate of test set was 95.92%. In order to improve the modeling speed, it is necessary to optimize the spectra variables. Therefore genetic algorithm (GA), successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted to select characteristic variables respectively. Finally CARS was proved to be the optimal variable selection method, 10 characteristic wavelengths were selected to develop SVM model, recognition rate of calibration set reached 100%, and recognition rate of test set was 93.88%. The research provided technical reference for freshwater fish freshness online detection.

Key words： Near infrared spectra Variable selection On-line Freshness Freshwater fish

Received: 2014-05-20 Accepted: 2014-07-25

ZTFLH:

O657.3

Corresponding Authors: LI Xiao-yu E-mail: lixiaoyu@mail.hzau.edu.cn

Cite this article:

HUANG Tao,LI Xiao-yu,PENG Yi, et al. Freshwater Fish Freshness On-Line Detection Method Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(10): 2732-2736.

URL:

http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2014)10-2732-05 OR http://www.gpxygpfx.com/EN/Y2014/V34/I10/2732

[1] ZHANG Jun, LI Xiao-yu, WANG Wei, et al(张军, 李小昱, 王为, 等). Transactions of the Chinese Society for Agricultural Machinery (农业机械学报), 2009, 40(4): 129.
[2] ZHOU Zhu, LI Xiao-yu, GAO Hai-long, et al(周竹, 李小昱, 高海龙, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43, (2): 128.
[3] JIE Deng-fei, XIE Li-juan, RAO Xiu-qin, et al(介邓飞, 谢丽娟, 饶秀勤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(12): 264.
[4] MA Guang, SUN Tong(马广, 孙通). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(7): 170.
[5] XIA Jun-fang, LI Pei-wu, LI Xiao-yu, et al(夏俊芳, 李培武, 李小昱, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2007, 38(6): 107.
[6] CAI Jian-rong, WAN Xin-min, CHEN Quan-sheng(蔡健荣, 万新民, 陈全胜). Acta Optica Sinica(光学学报), 2009, 29(10): 2808.
[7] XU Fu-bin, HUANG Xing-yi, DING Ran, et al(徐富斌, 黄星奕, 丁然, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2012, 6(3): 644.
[8] Nilsen H, Esaiassen M, Hela K, et al. Journal of Food Science, 2002, 67(5): 1821.
[9] Huang X, Xin J, Zhao J. Journal of Food Engineering, 2011, 105(4): 632.
[10] Sivertsen A H, Kimiya T, Heia K. Journal of Food Engineering, 2011, 103(3): 317.
[11] CHENG Ni, LI Xiao-yu, ZHAO Si-ming, et al(程旎, 李小昱, 赵思明, 等). Journal of Food Safety and Quality(食品安全质量检测学报), 2013, 4(2): 427.
[12] National Standard of the People’s Republic of China(中华人民共和国国家标准). GB/T 5009.45—2003, Method for Analysis of Hygienic Standard of Fish and Other Aquatic Products(水产品卫生标准的分析方法), 2003.
[13] National Standard of the People’s Republic of China(中华人民共和国国家标准). GB 2733—2005, Hygienic Standard for Fresh and Frozen Marine Products of Animal Origin(鲜、冻动物性水产品卫生标准), 2005.