基于近红外光谱快速检测杏鲍菇多糖含量的研究
|
摘要
为建立杏鲍菇多糖含量的近红外光谱(NIR)快速检测方法,采用蒽酮-硫酸法和分光光度计检测杏鲍菇多糖含量,得到多糖含量的吸光度值。利用傅立叶变换近红外透射光谱技术采集杏鲍菇多糖含量的近红外光谱图,通过平滑处理、导数处理、散射校正和小波转换的方法对近红外光谱进行预处理以消除光谱噪音的影响,结合最小二乘支持向量回归(LSSR)、偏最小二乘回归(PLSR)和人工神经网络模型(ANN)建立多糖的定量校正模型。得出最佳模型是近红外光谱结合小波变化-径向基神经网络(RBF-NN)模型,结果为Rc~2=1,Rv~2=0.890 075,RMSEC=9.02E-10,RMSEP=7.728 933,REC=7.63E-12,REP=0.086 268。研究建立的近红外模型为快速检测杏鲍菇多糖含量提供了初步依据。
In order to establish a rapid detection method of Pleurotus eryngii polysaccharide content with near infrared spectroscopy(NIR), the chemical content of polysaccharide was first detected by anthrone-sulfuric acid method, the NIRs of Pleurotus eryngii was scanned using fourier transform near infrared spectrometer and the spectra was later pretreated by methods of smoothing, derivative, scatter correction and wavelet transform afterward,finally the mathematical model of chemical content and NIR of polysaccharide was established using Least squares supp(RBF-NM) model. The best model was RBF-NN model, and the parameters were calculated: Rc~2= 1,Rv~2 =0.890 075,RMSEC = 9.02 E-10,RMSEP =7.728 933,REC = 7.63 E-12,REP = 0.086 268. The near infrared model established in the present study provides preliminary basis for the rapid detection of Pleurotus eryngii polysaccharide content
In order to establish a rapid detection method of Pleurotus eryngii polysaccharide content with near infrared spectroscopy(NIR), the chemical content of polysaccharide was first detected by anthrone-sulfuric acid method, the NIRs of Pleurotus eryngii was scanned using fourier transform near infrared spectrometer and the spectra was later pretreated by methods of smoothing, derivative, scatter correction and wavelet transform afterward,finally the mathematical model of chemical content and NIR of polysaccharide was established using Least squares supp(RBF-NM) model. The best model was RBF-NN model, and the parameters were calculated: Rc~2= 1,Rv~2 =0.890 075,RMSEC = 9.02 E-10,RMSEP =7.728 933,REC = 7.63 E-12,REP = 0.086 268. The near infrared model established in the present study provides preliminary basis for the rapid detection of Pleurotus eryngii polysaccharide content
引文
[1]李树锦,李海强,孙国娟,等.杏鲍菇多糖研究进展[J].现代农业科技,2012(4):43.
[2]颜明娟,江枝和,蔡顺香.杏鲍菇营养成分的分析[J].食用菌,2002(2):11.
[3]潘崇环,孙萍.珍惜食用菌栽培与名贵野生菌的开发利用[M].北京:中国农业出版社,2004:93.
[4]陈九艳,方敏,吴再珍,等.基于近红外光谱测定复方盐酸阿米洛利片有效成分的含量[J].中国药房,2013,24(9):828.
[5]樊玲玲,张雯雯,丛晓东,等.近红外光谱快速检测玉竹酒基提取液中多糖的含量[J].中国实验方剂学杂志,2013,19(20):80.
[6]曹干,周雪秋.近红外光谱法快速测定甘蔗蔗汁蔗糖分[J].甘蔗,2001,8(3):20.
[7]刘春泉,李大婧,刘荣.蒽酮-硫酸法测定冬虫夏草多糖含量[J].江苏农业科学,2006(2):122.
[8]岑益郎,宋韬,何勇,等.基于可见/近红外漫反射光谱的土壤有机质含量估算方法[J].浙江大学学报(农业与生命科学版),2011,37(3):300.
[9]赵杰文,张海东,刘木华.简化苹果糖度预测模型的近红外光谱预处理方法[J].光学学报,2006,26(1):136.
[10]田高友,袁洪福,渚小立,等.结合小波转换与微积分改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4):516.
[11]尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J].药物分析杂志,2008,28(5):824.
[12]张敏.近红外光谱变量选择和建模方法研究[D].天津:南开大学,2011.
[13]袁洪福,刘慧颖.小波转换在近红外光谱分析中的应用进展[J].光谱学与光谱分析,2003,23(6):1111.
[14]张龙,潘家荣,朱诚.基于近红外光谱的重金属汞、镉和铅污染水稻叶片鉴别[J].浙江大学学报(农业与生命科学版),2013,39(1):50.
[15]陈鹏强,陆辉山,闫红伟.基于近红外光谱的煤粉样品定量检测研究[J].工矿自动化,2013,39(8):68.
[16]褚小立,王彦斌,陆婉珍.近红外光谱定量校正模型的建立及应用[J].理化检验:化学分册,2008,44(8):796.
[17]管亮,冯新泸.基于小波转换的信号消噪效果影响因素研究及其Matlab实践[J].自动化与仪器仪表,2004(6):43.
[2]颜明娟,江枝和,蔡顺香.杏鲍菇营养成分的分析[J].食用菌,2002(2):11.
[3]潘崇环,孙萍.珍惜食用菌栽培与名贵野生菌的开发利用[M].北京:中国农业出版社,2004:93.
[4]陈九艳,方敏,吴再珍,等.基于近红外光谱测定复方盐酸阿米洛利片有效成分的含量[J].中国药房,2013,24(9):828.
[5]樊玲玲,张雯雯,丛晓东,等.近红外光谱快速检测玉竹酒基提取液中多糖的含量[J].中国实验方剂学杂志,2013,19(20):80.
[6]曹干,周雪秋.近红外光谱法快速测定甘蔗蔗汁蔗糖分[J].甘蔗,2001,8(3):20.
[7]刘春泉,李大婧,刘荣.蒽酮-硫酸法测定冬虫夏草多糖含量[J].江苏农业科学,2006(2):122.
[8]岑益郎,宋韬,何勇,等.基于可见/近红外漫反射光谱的土壤有机质含量估算方法[J].浙江大学学报(农业与生命科学版),2011,37(3):300.
[9]赵杰文,张海东,刘木华.简化苹果糖度预测模型的近红外光谱预处理方法[J].光学学报,2006,26(1):136.
[10]田高友,袁洪福,渚小立,等.结合小波转换与微积分改善近红外光谱分析精度[J].光谱学与光谱分析,2005,25(4):516.
[11]尼珍,胡昌勤,冯芳.近红外光谱分析中光谱预处理方法的作用及其发展[J].药物分析杂志,2008,28(5):824.
[12]张敏.近红外光谱变量选择和建模方法研究[D].天津:南开大学,2011.
[13]袁洪福,刘慧颖.小波转换在近红外光谱分析中的应用进展[J].光谱学与光谱分析,2003,23(6):1111.
[14]张龙,潘家荣,朱诚.基于近红外光谱的重金属汞、镉和铅污染水稻叶片鉴别[J].浙江大学学报(农业与生命科学版),2013,39(1):50.
[15]陈鹏强,陆辉山,闫红伟.基于近红外光谱的煤粉样品定量检测研究[J].工矿自动化,2013,39(8):68.
[16]褚小立,王彦斌,陆婉珍.近红外光谱定量校正模型的建立及应用[J].理化检验:化学分册,2008,44(8):796.
[17]管亮,冯新泸.基于小波转换的信号消噪效果影响因素研究及其Matlab实践[J].自动化与仪器仪表,2004(6):43.