基于近红外光谱与支持向量机的甘薯粉丝掺假快速检测
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摘要
探索了近红外光谱(near infrared spectra,NIRS)结合支持向量机(support vector machine,SVM)检测甘薯粉丝掺假(掺杂木薯淀粉和玉米淀粉)的可行性。以掺假甘薯粉丝为研究对象,建立了基于NIRS及SVM的甘薯粉丝掺假定性判别及定量分析模型,并通过光谱预处理及光谱变量筛选对模型进行了优化。结果显示,采用标准正态变量变换和一阶导数对全光谱预处理后,甘薯粉丝掺假SVM定性判别模型的识别准确率可达100%,优于马氏距离判别模型;用标准正态变量变换和一阶导数对光谱预处理,并通过前向区间支持向量机(forward interval support vector machine,fi-SVM)筛选光谱变量后,木薯淀粉含量SVM预测模型的相关系数(r)和预测均方差(RMSEP)可达到0. 92和11. 20,玉米淀粉含量SVM预测模型的r和RMSEP可达到0. 96和7. 49。结果表明,基于NIRS和SVM的甘薯粉丝掺假定性判别及定量分析检测模型具有较高的识别率和预测精度,用于检测甘薯粉丝的掺假是可行的。
The aim of this study was to explore the feasibility of identifying and quantifying adulterated sweet potato starch noodles( adulterated with cassava starch and corn starch) using near-infrared spectroscopy( NIRS) and support vector machine( SVM). Qualitative discrimination and quantitative analysis models based on NIRS and SVM were built,which were optimized by spectra pretreatment and spectral variables selection. The results showed that after using standard normal variate transformation and first derivative pretreatment,the accuracy of SVM model for qualitative discrimination based on whole NIRS spectra for identifying adulterated sweet potato starch noodles achieved100%,which surpassed the Mahalanobis distance discriminant model. Moreover,by using a forward interval support vector machine( fi-SVM) algorithm to screen out spectral variables,the correlation coefficients( r) of the SVM models for cassava starch content and corn starch content reached 0. 92 and 0. 96,respectively. Besides,the root mean square error of prediction( RMSEP) of these two models reached 11. 2 and 7. 49,respectively. The results indicated that models based on NIRS and SVM for qualitative discrimination and quantified analysis for adulterated sweet potato starch noodles had high recognition rates and prediction accuracy. Therefore,it is feasible to detect adulteration of sweet potato starch noodles by using NIRS and SVM.
The aim of this study was to explore the feasibility of identifying and quantifying adulterated sweet potato starch noodles( adulterated with cassava starch and corn starch) using near-infrared spectroscopy( NIRS) and support vector machine( SVM). Qualitative discrimination and quantitative analysis models based on NIRS and SVM were built,which were optimized by spectra pretreatment and spectral variables selection. The results showed that after using standard normal variate transformation and first derivative pretreatment,the accuracy of SVM model for qualitative discrimination based on whole NIRS spectra for identifying adulterated sweet potato starch noodles achieved100%,which surpassed the Mahalanobis distance discriminant model. Moreover,by using a forward interval support vector machine( fi-SVM) algorithm to screen out spectral variables,the correlation coefficients( r) of the SVM models for cassava starch content and corn starch content reached 0. 92 and 0. 96,respectively. Besides,the root mean square error of prediction( RMSEP) of these two models reached 11. 2 and 7. 49,respectively. The results indicated that models based on NIRS and SVM for qualitative discrimination and quantified analysis for adulterated sweet potato starch noodles had high recognition rates and prediction accuracy. Therefore,it is feasible to detect adulteration of sweet potato starch noodles by using NIRS and SVM.
引文
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[18] LU G,HUANG H,ZHANG D. Application of near-infrared spectroscopy to predict sweetpotato starch thermal properties and noodle quality[J]. Journal of Zhejiang University Science B,2006,7(6):475-481.
[19] DING X,NI Y,KOKOT S. NIR spectroscopy and chemometrics for the discrimination of pure,powdered,purple sweet potatoes and their samples adulterated with the white sweet potato flour[J]. Chemometrics and Intelligent Laboratory Systems,2015,144:17-23.
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[21]张丽华,郝莉花,李顺峰,等.基于支持向量机的近红外光谱技术快速鉴别掺假羊肉[J].食品工业科技,2015,36(23):289-293.
[22] PIERNA J,VOLERY P,BESSON R,et al. Classification of modified starches by Fourier transform infrared spectroscopy using support vector machines[J]. Journal of Agricultural and Food Chemistry,2005,53(17):6 581-6 585.
[23] CHEN J,ZHU S,ZHAO G. Rapid determination of total protein and wet gluten in commercial wheat flour using si SVR-NIR[J]. Food Chemistry,2017,221:1 939-1 946.
[24]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4):528-542.
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[26] FILGUEIRAS P R,ALVES J C L,POPPI R J. Quantification of animal fat biodiesel in soybean biodiesel and B20diesel blends using near infrared spectroscopy and synergy interval support vector regression[J]. Talanta,2014,119:582-589.
[27]邹婷婷,窦英,王莹,等.近红外光谱法结合C-SVM及v-SVM方法快速无损鉴别淀粉种类[J].食品工业科技,2013,34(17):317-319.
[28] CHEN J,YE F,ZHAO G. Rapid determination of farinograph parameters of wheat flour using data fusion and a forward interval variable selection algorithm[J]. Analytical Methods,2017,9(45):6 341-6 348.
[29] RANZAN C,TRIERWEILER L F,HITZMANN B,et al.NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization[J]. Chemometrics and Intelligent Laboratory Systems,2015,142:78-86.
[30] CHUANG C C,JENG J T,TAO C W. Two-Stages support vector regression for fuzzy neural networks with outliers[J]. International Journal of Fuzzy Systems,2009,11(1):20-28.
[31] SHAO Y,CEN Y,HE Y,et al. Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J]. Food Chemistry,2011,126(4):1 856-1 861.
[32] LIN C,CHEN X,JIAN L,et al. Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley[J]. Food Chemistry,2014,162:10-15.
[33] AL-MBAIDEEN A,BENAISSA M. Frequency self deconvolution in the quantitative analysis of near infrared spectra[J]. Analytica Chimica Acta,2011,705(1-2):135-147.
[34] WU F,MENG Y,YANG N,et al. Effects of mung bean starch on quality of rice noodles made by direct dry flour extrusion[J]. LWT-Food Science and Technology,2015,63(2):1 199-1 205.
[35] PHOTINAM R,MOONGNGARM A,PASEEPHOL T.Process optimization to increase resistant starch in vermicelli prepared from mung bean and cowpea starch[J]. Emirates Journal of Food and Agriculture,2016,28(7):449-458.
[2]侯汉学,董海洲,刘传富.甘薯淀粉中掺有玉米淀粉的检测方法[J].食品与发酵工业,2010,36(1):134-137.
[3]杜连起.甘薯粉条掺杂异种淀粉检验方法的研究[J].河北职业技术师范学院学报,2000,26(2):24-26.
[4]陈嘉,刘嘉,马雅钦,等.葛粉掺假的近红外漫反射光谱快速检测[J].食品科学,2014,35(8):133-136.
[5] SANS S,FERRE J,BOQUE R,et al. Determination of chemical properties in'calcot'(Allium cepa L.)by near infrared spectroscopy and multivariate calibration[J]. Food Chemistry,2018,262:178-183.
[6]贾柳君,张海红,王健,等.采用近红外光谱定量分析葡萄酒发酵液中总酸含量和p H值[J].食品与发酵工业,2017,43(2):191-195.
[7]何佳艳,李亭,郭长凯,等.近红外光谱法快速无损测定奶粉的脂肪含量[J].食品与发酵工业,2017,43(10):228-233.
[8] TAHIR H E,ZOU X,SHEN T,et al. Near-Infrared(NIR)spectroscopy for rapid measurement of antioxidant properties and discrimination of sudanese honeys from different botanical origin[J]. Food Analytical Methods,2016,9(9):2 631-2 641.
[9] RIOS-REINA R,LUIS GARCIA-GONZALEZ D,MARIA CALLEJON R,et al. NIR spectroscopy and chemometrics for the typification of Spanish wine vinegars with a protected designation of origin[J]. Food Control,2018,89:108-116.
[10] BALLABIOD,ROBOTTI E,GRISONI F,et al. Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey[J]. Food Chemistry,2018,266:79-89.
[11] LIU N,PARRA H A,PUSTJENS A,et al. Evaluation of portable near-infrared spectroscopy for organic milk authentication[J]. Talanta,2018,184:128-135.
[12]潘冰燕,鲁晓翔,张鹏,等.近红外光谱对甜椒果实质地的无损检测[J].食品与发酵工业,2015,41(11):143-147.
[13] GHOSH S,MISHRA P,MOHAMAD S N H,et al. Discrimination of peanuts from bulk cereals and nuts by near infrared reflectance spectroscopy[J]. Biosystems Engineering,2016,151:178-186.
[14] MEES C,SOUARD F,DELPORTE C,et al. Identification of coffee leaves using FT-NIR spectroscopy and SIMCA[J]. Talanta,2018,177:4-11.
[15] LIU J,WEN Y,DONG N,et al. Authentication of lotus root powder adulterated with potato starch and/or sweet potato starch using Fourier transform mid-infrared spectroscopy[J]. Food Chemistry,2013,141(3):3 103-3 109.
[16] XU L,SHI P,YE Z,et al. Rapid analysis of adulterations in Chinese lotus root powder(LRP)by near-infrared(NIR)spectroscopy coupled with chemometric class modeling techniques[J]. Food Chemistry,2013,141(3):2 434-2 439.
[17] MAHOOD F,JABEEN F,HUSSAIN J,et al. FT-NIRS coupled with chemometric methods as a rapid alternative tool for the detection&quantification of cow milk adulteration in camel milk samples[J]. Vibrational Spectroscopy,2017,92:245-250.
[18] LU G,HUANG H,ZHANG D. Application of near-infrared spectroscopy to predict sweetpotato starch thermal properties and noodle quality[J]. Journal of Zhejiang University Science B,2006,7(6):475-481.
[19] DING X,NI Y,KOKOT S. NIR spectroscopy and chemometrics for the discrimination of pure,powdered,purple sweet potatoes and their samples adulterated with the white sweet potato flour[J]. Chemometrics and Intelligent Laboratory Systems,2015,144:17-23.
[20] CHUANG C C,SU S F,JENG J T,et al. Robust support vector regression networks for function approximation with outliers[J]. IEEE Transactions on Neural Networks,2002,13(6):1 322-1 330.
[21]张丽华,郝莉花,李顺峰,等.基于支持向量机的近红外光谱技术快速鉴别掺假羊肉[J].食品工业科技,2015,36(23):289-293.
[22] PIERNA J,VOLERY P,BESSON R,et al. Classification of modified starches by Fourier transform infrared spectroscopy using support vector machines[J]. Journal of Agricultural and Food Chemistry,2005,53(17):6 581-6 585.
[23] CHEN J,ZHU S,ZHAO G. Rapid determination of total protein and wet gluten in commercial wheat flour using si SVR-NIR[J]. Food Chemistry,2017,221:1 939-1 946.
[24]褚小立,袁洪福,陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展,2004,16(4):528-542.
[25] CHANG C,LIN C. LIBSVM:A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[26] FILGUEIRAS P R,ALVES J C L,POPPI R J. Quantification of animal fat biodiesel in soybean biodiesel and B20diesel blends using near infrared spectroscopy and synergy interval support vector regression[J]. Talanta,2014,119:582-589.
[27]邹婷婷,窦英,王莹,等.近红外光谱法结合C-SVM及v-SVM方法快速无损鉴别淀粉种类[J].食品工业科技,2013,34(17):317-319.
[28] CHEN J,YE F,ZHAO G. Rapid determination of farinograph parameters of wheat flour using data fusion and a forward interval variable selection algorithm[J]. Analytical Methods,2017,9(45):6 341-6 348.
[29] RANZAN C,TRIERWEILER L F,HITZMANN B,et al.NIR pre-selection data using modified changeable size moving window partial least squares and pure spectral chemometrical modeling with ant colony optimization for wheat flour characterization[J]. Chemometrics and Intelligent Laboratory Systems,2015,142:78-86.
[30] CHUANG C C,JENG J T,TAO C W. Two-Stages support vector regression for fuzzy neural networks with outliers[J]. International Journal of Fuzzy Systems,2009,11(1):20-28.
[31] SHAO Y,CEN Y,HE Y,et al. Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice[J]. Food Chemistry,2011,126(4):1 856-1 861.
[32] LIN C,CHEN X,JIAN L,et al. Determination of grain protein content by near-infrared spectrometry and multivariate calibration in barley[J]. Food Chemistry,2014,162:10-15.
[33] AL-MBAIDEEN A,BENAISSA M. Frequency self deconvolution in the quantitative analysis of near infrared spectra[J]. Analytica Chimica Acta,2011,705(1-2):135-147.
[34] WU F,MENG Y,YANG N,et al. Effects of mung bean starch on quality of rice noodles made by direct dry flour extrusion[J]. LWT-Food Science and Technology,2015,63(2):1 199-1 205.
[35] PHOTINAM R,MOONGNGARM A,PASEEPHOL T.Process optimization to increase resistant starch in vermicelli prepared from mung bean and cowpea starch[J]. Emirates Journal of Food and Agriculture,2016,28(7):449-458.