近红外光谱技术结合化学计量方法用于大米的快速分析
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摘要
基于近红外光谱结合化学计量学建立一种可以快速、无损鉴别大米品种的新方法。采集不同厂家以及不同品牌的大米共115类并得到其光谱数据。利用连续小波变换技术消除背景干扰和基线漂移,从而加强光谱特征信息,提高信噪比。此外,结合主成分分析方法对不同品牌与不同厂家的大米进行鉴别分析。结果表明:连续小波变换可以有效地消除背景干扰,极大地提高模型的鉴别能力。采用近红外光谱技术结合化学计量学方法可以准确地鉴别不同大米的品种及产地。
Based on near infrared spectroscopy and chemometrics method,a fast and nondestructive method for the analysis of rice samples from different brands and different manufacturers was established. Firstly,115 spectra from different brands and different manufacturers were collected. Continuous wavelet transform technology was used to subtract background interference and baseline drift,thereby enhancing the spectral feature information and improving the signal to noise ratio. Combining with principal component analysis method,rice samples of different brands and different manufacturers could be identified. The results showed that the baseline interference was effectively eliminated and the discriminant ability of the model was greatly improved by continuous wavelet transform method. Near-infrared spectroscopy combined with chemometric methods could accurately identify rice from different brands and different manufacturers.
Based on near infrared spectroscopy and chemometrics method,a fast and nondestructive method for the analysis of rice samples from different brands and different manufacturers was established. Firstly,115 spectra from different brands and different manufacturers were collected. Continuous wavelet transform technology was used to subtract background interference and baseline drift,thereby enhancing the spectral feature information and improving the signal to noise ratio. Combining with principal component analysis method,rice samples of different brands and different manufacturers could be identified. The results showed that the baseline interference was effectively eliminated and the discriminant ability of the model was greatly improved by continuous wavelet transform method. Near-infrared spectroscopy combined with chemometric methods could accurately identify rice from different brands and different manufacturers.
引文
[1]杨福臣,孙兆远,孙芝杨.乳粉中蛋白质和脂肪近红外测定模型研究[J].食品研究与开发,2017,38(2):169-173
[2]Zhang Z H,Sun D W.Effects of cooling methods on the coling efficiency and quality of cooked rice[J].Journal of Food Engineering,2006,77:269-274
[3]Chen D,Cai W S,Shao X G.Representative subset selection in modified iterative predictor weighting(mIPW)-PLS models for parsimonious multivariate calibration[J].Chemometrics and Intelligent Laboratory Systems,2007,87:312-318
[4]Li P,Du G,Cai W,et al.Rapid and nondestructive analysis of pharmaceutical oroducts using near-infrared diffuse reflectance spectroscopy[J].Journal of Pharmaceutical and Biomedical Analysis,2012,70(21):288-294
[5]Zhang C X,Bian X H,Liu P,et al.Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples[J].Chemometrics and Intelligent Laboratory Systems,2016,161:43-48
[6]Li P,Du G R,Ma Y J,et al.A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm[J].Chemometrics and Intelligent Laboratory Systems,2018,176:157-161
[7]蒋璐璐,谈黎虹,裘正军,等.基于可见-近红外光谱的制动液品牌鉴别方法研究[J].光谱学与光谱分析,2008,28(6):1296-1299
[8]Zsolt S,Tanas D,Gyorgy D.Distinguishing melon genotypes using NIR spectroscopy[J].Chemometrics and Intelligent Laboratory Systems,2004,72:195-203
[9]陈建,陈晓,李伟,等.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究[J].光谱学与光谱分析,2008,28(8):1806-1808
[10]Estcban D,Gonzalez S,Pizarro C.An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS[J].Analytica Chimica Acta,2004,514:57-67
[11]Miralbes C.Discrimination of European wheat varieties using near infrared reflectance spectroscopy[J].Food Chemistry,2008,106(1):386-389
[12]毕京翠,张文伟,肖应辉,等.应用近红外光谱技术分析稻米蛋白质含量[J].作物学报,2006,32(5):709-715
[13]王海莲,万向元,胡培松,等.稻米脂肪含量近红外光谱分析技术研究[J].中国农业科学,2005,38(8):1540-1546
[14]张斌,张大鹏,章文斌,等.稻米色氨酸含量近红外反射光谱全局和局部飞速定标技术研究[J].光谱学与光谱分析,2011,31(1):73-76
[15]ZHU Zhenhua,KIM Kiyoung,YUAN Pingrong,et al.Effects of different ecological conditions on grain quality and RVA profile characteristics of Japonica rice from Yunnan plateau of China and Korea[J].Ecological Engineering,2011,37(8):1104-1111
[16]夏立娅,申世刚,刘峥颢,等.基于近红外光谱和模式识别技术鉴别大米产地的研究[J].光谱学与光谱分析,2013,33(1):102-105
[17]周子立,张瑜,何勇,等.基于近红外光谱技术的大米品种快速鉴别方法[J].农业工程学报,2009,25(8):131-135
[2]Zhang Z H,Sun D W.Effects of cooling methods on the coling efficiency and quality of cooked rice[J].Journal of Food Engineering,2006,77:269-274
[3]Chen D,Cai W S,Shao X G.Representative subset selection in modified iterative predictor weighting(mIPW)-PLS models for parsimonious multivariate calibration[J].Chemometrics and Intelligent Laboratory Systems,2007,87:312-318
[4]Li P,Du G,Cai W,et al.Rapid and nondestructive analysis of pharmaceutical oroducts using near-infrared diffuse reflectance spectroscopy[J].Journal of Pharmaceutical and Biomedical Analysis,2012,70(21):288-294
[5]Zhang C X,Bian X H,Liu P,et al.Subagging for the improvement of predictive stability of extreme learning machine for spectral quantitative analysis of complex samples[J].Chemometrics and Intelligent Laboratory Systems,2016,161:43-48
[6]Li P,Du G R,Ma Y J,et al.A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm[J].Chemometrics and Intelligent Laboratory Systems,2018,176:157-161
[7]蒋璐璐,谈黎虹,裘正军,等.基于可见-近红外光谱的制动液品牌鉴别方法研究[J].光谱学与光谱分析,2008,28(6):1296-1299
[8]Zsolt S,Tanas D,Gyorgy D.Distinguishing melon genotypes using NIR spectroscopy[J].Chemometrics and Intelligent Laboratory Systems,2004,72:195-203
[9]陈建,陈晓,李伟,等.基于近红外光谱技术和人工神经网络的玉米品种鉴别方法研究[J].光谱学与光谱分析,2008,28(8):1806-1808
[10]Estcban D,Gonzalez S,Pizarro C.An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS[J].Analytica Chimica Acta,2004,514:57-67
[11]Miralbes C.Discrimination of European wheat varieties using near infrared reflectance spectroscopy[J].Food Chemistry,2008,106(1):386-389
[12]毕京翠,张文伟,肖应辉,等.应用近红外光谱技术分析稻米蛋白质含量[J].作物学报,2006,32(5):709-715
[13]王海莲,万向元,胡培松,等.稻米脂肪含量近红外光谱分析技术研究[J].中国农业科学,2005,38(8):1540-1546
[14]张斌,张大鹏,章文斌,等.稻米色氨酸含量近红外反射光谱全局和局部飞速定标技术研究[J].光谱学与光谱分析,2011,31(1):73-76
[15]ZHU Zhenhua,KIM Kiyoung,YUAN Pingrong,et al.Effects of different ecological conditions on grain quality and RVA profile characteristics of Japonica rice from Yunnan plateau of China and Korea[J].Ecological Engineering,2011,37(8):1104-1111
[16]夏立娅,申世刚,刘峥颢,等.基于近红外光谱和模式识别技术鉴别大米产地的研究[J].光谱学与光谱分析,2013,33(1):102-105
[17]周子立,张瑜,何勇,等.基于近红外光谱技术的大米品种快速鉴别方法[J].农业工程学报,2009,25(8):131-135