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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