基于高光谱和极限学习机的冷鲜羊肉颜色无损检测

梁晓燕; 郭中华; 线文瑶; 魏菁

doi:10.13386/j.issn1002-0306.2016.24.005

基于高光谱和极限学习机的冷鲜羊肉颜色无损检测

1. 宁夏大学物理电气信息学院
2. 宁夏沙漠信息智能感知重点实验室

基金项目:

国家自然科学基金(61565014)；

详细信息

作者简介:
梁晓燕(1990-),女,硕士研究生,研究方向:计算机网络与多媒体通信,E-mail:lxy19900212@163.com。;

郭中华(1973-),男,博士,教授,研究方向:电路与系统,信号处理与检测,E-mail:guozhh@nxu.edu.cn。;

中图分类号: TS251.53
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出版历程
- 收稿日期: 2016-04-28

Non-destructive detection of color in chilled mutton based on hyperspectral technique and extreme learning machine

School of Physics and Electronic Information Engineering,Ningxia University
Ningxia Key Laboratory of Intelligent Sensing for Desert Information

摘要

摘要: 利用400¹000 nm近红外高光谱成像系统对冷鲜羊肉颜色进行快速无损检测研究。采集140个冷鲜羊肉样品（贮藏1⁷ d）光谱图像,并测量其亮度（L*）、红度（a*）、黄度（b*）和饱和度（C*）等颜色参数。选取感兴趣区域获取样品代表性光谱,利用联合区间偏最小二乘法（si PLS）对一阶微分、多元散射校正、标准正态变量变换（SNV）等方法预处理后的光谱数据筛选特征波段,建立冷鲜羊肉颜色各参数的si PLS-ELM神经网络校正模型。对于L*、a*、b*和C*,模型的预测集相关系数（RP）分别为0.9219、0.9391、0.9603和0.8839,预测集均方根误差（RMSEP）分别为1.1935、0.2333、0.6009和0.3586。结果表明:采用可见-近红外高光谱成像技术结合si PLS-ELM神经网络对冷鲜羊肉颜色的快速无损检测是可行的。
- 高光谱成像技术 /
- 冷鲜羊肉 /
- 颜色参数 /
- 联合区间偏最小二乘法 /
- 极限学习机 /
- 无损检测
Abstract: Near- infrared hyperspectral imaging system that was ranging from 400 nm to 1000 nm was used to finish the research of non- destructive and rapid testing of the chilled mutton colors. Hyperspectral images were taken from the 140 chilled mutton samples( storaging 1 ~7 d),and it measured the colors parameters: brightness( L*),redness( a*),yellowness( b*) and saturation( C*). Then it selected an interested area to get a representative sample spectra,and utilized the spectral data which was gotten from the preprocess synergy interval partial least squares regression( si PLS),the first derivative( FD),multiplicative scatter correction( MSC) and SNV and it was used to select the characteristic bands.It established si PLS- ELM neural network calibration model that was based on the colors parameters of the chilled mutton.The correlation coefficient( RP) of the model prediction was 0.9219,0.9391,0.9603,0.8839 which was corresponded to the L*,a*,b*and C*. The root mean square error prediction( RMSEP) was 1.1935,0.2333,0.6009,0.3586. The results showed that: the near- infrared hyperspectral imaging technologies combing with si PLS- ELM neural network was feasible for the non- destructive and rapid testing of the the chilled mutton colors.
- hyperspectral imaging technology /
- chilled mutton /
- color parameters /
- si PLS /
- ELM /
- non-destructive detection

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参考文献(16)

[1]	刘珂,刘骞.冷却肉品质控制技术[J].肉类工业,2010(6):1-3.
[2]	王婉娇,王松磊,贺晓光,等.冷鲜羊肉冷藏时间和水分含量的高光谱无损检测[J].食品科学,2015,36(16):112-116.
[3]	朱彤,王宇,杨君娜,等.肉色研究的概况及最新进展[J].肉类研究,2008(2):11-18.
[4]	郭中华,王磊,金灵,等.基于近红外透射光谱的乳制品蛋白质、脂肪含量检测[J].光电子·激光,2013,24(6):1163-116.
[5]	吴龙国,何建国,刘贵珊,等.基于近红外高光谱成像技术的长枣含水量无损检测[J].光电子·激光,2014,25(1):135-140.
[6]	THYHOLT K,SAKSSON T.Differentiation of frozen and unfrozen beef using near-infrared spectroscopy[J].Journal of the Science of Food and Agriculture,1997,73(4):525-532.
[7]	王文秀,彭彦昆.基于近红外光谱的冷鲜肉-解冻肉的判别研究[J].食品安全质量检测学报,2014,5(3):754-760.
[8]	Qiao J,Ngadimo,Wang N,et al.Pork quality and marbling level assessment using a hyperspectral imaging system[J].Journal of Food Engineering,2007,83(1):10-16.
[9]	Qiao J,Wang N,Ngadimo,et al.Prediction of driploss,p H,and color for pork using a hyperspectral imaging technique[J].Meat Science,2007,76(l):1-8.
[10]	Naganathan G K,Grimes L M.Partial least square analysis of near-infrared hyperspectral imagesfor beef tenderness prediction[J].Sensing and Instrumentation for Food Quality and Safety,2008,2(3):178-188.
[11]	Naganathan G K,Grimes L M.Visible/near-infrared hyperspectral imaging for beef tenderness prediction[J].Computers and Electronics in Agriculture,2008,64(2):225-233.
[12]	邹小波,李志华,石吉勇,等.高光谱成像技术检测肴肉新鲜度[J].食品科学,2014,35(8):89-93.
[13]	吴建虎,彭彦昆,陈菁菁,等.基于高光谱散射特征的牛肉品质参数的预测研究[J].光谱学与光谱分析,2010,30(7):1815-1819.
[14]	赵杰文,毕夏坤,林颢,等.鸡蛋新鲜度的可见-近红外透射光谱快速识别[J].激光与光电子学进展,2013,50(5):1-8.
[15]	Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:a new learning scheme of feed forward neural networks[C].Budapest,Hungary:In Proceedings of the International Joint Conference on Neural Networks,2004:985-990.
[16]	Guangbin Huang,Hongming Zhou,Xiaojian Ding,et al.Extreme learning machine for regression and multiclass classification[J].IEEE Transactions on Systems Man and Cybernetics,Part B:Cy-bernetics,2012,42(2):513-529.

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