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基于近红外光谱技术的老陈醋可溶性固形物定量分析
引用本文:吴远远,陆辉山,高 强,闫宏伟,王福杰.基于近红外光谱技术的老陈醋可溶性固形物定量分析[J].中国酿造,2016,35(8):69.
作者姓名:吴远远  陆辉山  高 强  闫宏伟  王福杰
作者单位:中北大学机械与动力工程学院,山西太原030051
基金项目:山西省科技攻关项目(20150311023-2);山西省2015高校科技创新项目(180012-117);山西省重点研发计划社会发展项目(201603D321117)
摘    要:可溶性固形物含量(SSC)是食品行业的重要技术参数之一。利用近红外光谱技术对不同醋龄的老陈醋SSC进行分析。在不同光谱预处理下,分别采用主成分回归(PCR)和偏最小二乘法(PLS)建立SSC的定量分析模型。结果表明,采用5点平滑预处理后,利用PLS建立的老陈醋SSC的定量分析模型最优,其校正集的相关系数R为0.999 9,校正标准偏差(RMSEC)为0.038 3,预测标准偏差(RMSEP)和交叉验证标准偏差(RMSECV)分别为0.082 1,0.096 4。表明采用近红外光谱技术对不同醋龄的老陈醋SSC进行定量分析建模是可行的。

关 键 词:近红外光谱  可溶性固形物含量  主成分回归  偏最小二乘法  预处理  

Quantitative analysis of soluble solids content in mature vinegar based on near infrared spectroscopy technology
WU Yuanyuan,LU Huishan,GAO Qiang,YAN Hongwei,WANG Fujie.Quantitative analysis of soluble solids content in mature vinegar based on near infrared spectroscopy technology[J].China Brewing,2016,35(8):69.
Authors:WU Yuanyuan  LU Huishan  GAO Qiang  YAN Hongwei  WANG Fujie
Affiliation:College of Mechanical and Power Engineering, North University of China, Taiyuan 030051, China
Abstract:The soluble solid content (SSC) is one of the important technical parameters of the food industry. The SSC in mature vinegar with different vinegar ages was analyzed by near infrared spectroscopy technology. Under the different spectra pretreatment, quantitative analysis model of SSC was established by principal component regression and partial least squares(PLS). The results showed that after using 5 point smooth pretreatment, the quantitative analysis model of SSC established by PLS was the best. The correlation coefficient R of correction set was 0.999 9, root mean square error of calibration, root mean square error of prediction and root mean squares error of cross-validation was 0.038 3, 0.082 1 and 0.096 4, respectively. It was feasible to establish the quantitative analysis model of SSC in mature vinegar with different ages by near infrared spectroscopy technology.
Keywords:near infrared spectroscopy  soluble solid content  principal component regression  partial least squares regression  pretreatment  
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