基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析

武小红; 孙俊; 武斌; 唐凯

doi:doi:10.3788/lop52.043003

激光与光电子学进展, 2015, 52 (4): 043003, 网络出版: 2015-04-03

基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析下载： 809次

Qualitative Analysis Model of Near Infrared Spectra of Pork Based on Synergy Interval Partial Least Squares Discriminant Analysis

论文大纲

武小红 ^1,2,*孙俊 ^1,2武斌 ³唐凯 ¹

作者单位

¹ 江苏大学电气信息工程学院, 江苏镇江 212013

² 江苏大学机械工业设施农业测控技术与装备重点实验室, 江苏镇江 212013

³ 滁州职业技术学院信息工程系, 安徽滁州 239000

AI 词云图 AI一句话精读 AI短摘要

注：本部分内容由 AI 自动生成，请您知悉。

摘要

为解决偏最小二乘判别分析(PLSDA)建模时光谱区域中的噪声及冗余信息干扰问题，提出一种基于联合区间偏最小二乘判别分析(SiPLSDA)算法，并将该算法应用于猪肉近红外光谱的定性建模分析。SiPLSDA 利用联合区间偏最小二乘回归（SiPLS）进行光谱特征区域筛选，在筛选出来的光谱区域内建立数据的定性预测模型。采用Antaris II 快速傅里叶变换近红外光谱分析仪获取波数范围为10000~4000 cm-1的猪肉样本近红外光谱，采用标准正态变量变换(SNV)进行近红外光谱的预处理，用SiPLSDA 建立猪肉近红外光谱的定性模型。实验结果表明，SiPLSDA 建立的预测模型对猪肉储藏时间的识别率达到93.94%，高于基于全光谱区域建立的PLSDA 预测模型的识别率。

Abstract

To solve the problems of noise and redundancy in spectra regions which partial least squares discriminant analysis (PLSDA) encounters, synergy interval partial least squares discriminant analysis (SiPLSDA) is proposed and is used in the qualitative analysis model of near infrared spectra of pork. With the help of synergy interval partial least squares (SiPLS), SiPLSDA can select the spectral regions where the prediction model is constructed. Antaris II Fourier transform-near infrared (FT-NIR) spectrophotometer is used to obtain near infrared reflectance (NIR) spectra of pork samples in the range of 10000~4000 cm- 1. NIR spectra are preprocessed by standard normal variate (SNV) transformation. A qualitative analysis model of NIR spectra is built by SiPLSDA. The experimental results show that according to the SiPLSDA prediction model, the recognition ratio of pork storage time is up to 93.94 % and it is higher than that of PLSDA.

参考文献

[1] 武小红, 唐凯, 孙俊. 基于Adaboost+OLDA 和近红外光谱的猪肉贮藏时间辨别[J]. 光谱学与光谱分析, 2012, 32(12): 3238-3241.

Wu Xiaohong, Tang Kai, Sun Jun. Discrimination of pork storage time using near infrared spectroscopy and Adaboost+OLDA [J]. Spectroscopy and Spectral Analysis, 2012, 32(12): 3238-3241.

[2] 张晓东, 李贵荣, 李若诚, 等. 近红外光谱结合极限学习机和GA-PLS 算法检测普洱茶茶多酚含量[J]. 激光与光电子学进展, 2013, 50(4): 043001.

Zhang Xiaodong, Li Guirong, Li Ruocheng, et al.. Determination of tea polyphenols content in puerh tea using nearinfrared spectroscopy combined with extreme learning machine and GA- PLS algorithm [J]. Laser & Optoelectronics Progress, 2013, 50(4): 043001.

[3] 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): 053003.

Zhao Jiewen, Bi Xiakun, Lin Hao, et al.. Visible-near-infrared transmission spectra for rapid analysis of the freshness of eggs [J]. Laser & Optoelectronics Progress, 2013, 50(5): 053003.

[4] 陈晓峰, 龙长江, 牛智有, 等. 基于潜在语义分析与NIR 的中药材分类研究[J]. 光学学报, 2014, 34(9): 0930001.

Chen Xiaofeng, Long Changjiang, Niu Zhiyou, et al.. Classification research of Chinese medicine based on latent semantic analysis and NIR [J]. Acta Optica Sinica, 2014, 34(9): 0930001.

[5] 赵杰文, 呼怀平, 邹小波. 支持向量机在苹果分类的近红外光谱模型中的应用[J]. 农业工程学报, 2007, 23(4): 149-152.

Zhao Jiewen, Hu Huaiping, Zou Xiaobo. Application of support vector machine to apple classification with near-infrared spectroscopy [J]. Transactions of the Chinese Society of Agricultural engineering, 2007, 23(4): 149-152.

[6] 郭培源, 林岩, 付妍, 等. 基于近红外光谱技术的猪肉新鲜度等级研究[J]. 激光与光电子学进展, 2013, 50(3): 033002.

Guo Peiyuan, Lin Yan, Fu Yan, et al.. Research on freshness level of meat based on near- infrared spectroscopic technique [J]. Laser & Optoelectronics Progress, 2013, 50(3): 033002.

[7] T Pholpho, S Pathaveerat, P Sirisomboon. Classification of longan fruit bruising using visible spectroscopy [J]. Journal of Food Engineering, 2011, 104(1): 169-172.

[8] H Cen, Y He, M Huang. Combination and comparison of multivariate analysis for the identification of orange varieties using visible and near infrared reflectance spectroscopy [J]. European Food Research and Technology, 2007, 225(5-6): 699-705.

[9] W Luo, S Huan, H Fu, et al.. Preliminary study on the application of near infrared spectroscopy and pattern recognition methods to classify different types of apple samples [J]. Food Chemistry, 2011, 128(2): 555-561.

[10] X Luo, T Takahashi, K Kyo, et al.. Wavelength selection in vis/NIR spectra for detection of bruises on apples by ROC analysis [J]. Journal of Food Engineering, 2012, 109(3): 457-466.

[11] N Attaviroj, S Kasemsumran, A Noomhorm. Rapid variety identification of pure rough rice by Fourier-transform nearinfrared spectroscopy [J]. Cereal Chemistry, 2011, 88(5): 490-496.

[12] F Shen, J Wu, Y B Ying, et al.. Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting [J]. Food Chemistry, 2013, 141(4): 4026-4030.

[13] I Herrmann, U Shapira, S Kinast, et al.. Ground- level hyperspectral imagery for detecting weeds in wheat fields [J]. Precision Agriculture, 2013, 14(6): 637-659.

[14] X J Tian, J Wang, S Q Cui. Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors [J]. Journal of Food Engineering, 2013, 119(4): 744-749.

[15] 蔡健荣, 万新民, 陈全胜. 近红外光谱法快速检测猪肉中挥发性盐基氮的含量[J]. 光学学报, 2009, 29(10): 2808-2812.

Cai Jianrong, Wan Xinmin, Chen Quansheng. Feasibility study for the use of near-infrared spectroscopy in the quantitative analysis of TVB-N content in pork [J]. Acta Optica Sinica, 2009, 29(10): 2808-2812.

[16] Q Ouyang, Q S Chen, J W Zhao, et al.. Determination of amino acid nitrogen in soy sauce using near infrared spectroscopy combined with characteristic variables selection and extreme learning machine [J]. Food and Bioprocess Technology, 2013, 6(9): 2486-2493.

[17] J W Zhao, Q Ouyang, Q S Chen, et al.. Simultaneous determination of amino acid nitrogen and total acid in soy sauce using near infrared spectroscopy combined with characteristic variables selection [J]. Food Science and Technology International, 2013, 19(4): 305-314.

[18] 吕强, 汤明杰, 赵杰文, 等. 近红外光谱预测猕猴桃硬度模型的简化研究[J]. 光谱学与光谱分析, 2009, 29(7): 1768-1771.

Lü Qiang, Tang Mingjie, Zhao Jiewen, et al.. Study of simplification of prediction model for kiwifruit firmness using near infrared spectroscopy [J]. Spectroscopy and Spectral Analysis, 2009, 29(7): 1768-1771.

[19] 赵杰文, 蒋培, 陈全胜. 雪莲花产地鉴别的近红外光谱分析方法[J]. 农业机械学报, 2010, 41(8): 111-114.

Zhao Jiewen, Jiang Pei, Chen Quansheng. Discrimination of snow lotus from different geographical origins by near infrared spectroscopy [J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(8): 111-114.

武小红, 孙俊, 武斌, 唐凯. 基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析[J]. 激光与光电子学进展, 2015, 52(4): 043003. Wu Xiaohong, Sun Jun, Wu Bin, Tang Kai. Qualitative Analysis Model of Near Infrared Spectra of Pork Based on Synergy Interval Partial Least Squares Discriminant Analysis[J]. Laser & Optoelectronics Progress, 2015, 52(4): 043003.

基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析下载： 809次

关于本站 Cookie 的使用提示

全站搜索

基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析 下载： 809次

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索

基于联合区间偏最小二乘判别分析的猪肉近红外光谱定性建模分析下载： 809次