基于LS-SVM的草莓固酸比和可滴定酸近红外光谱定量模型  

Improved NIR quantitative model of soluble solids titratable acid ratio and titratable acidity in strawberry based on LS-SVM

作　　者：牛晓颖[1] 周玉宏[2] 邵利敏[2] 

机构地区：[1]河北大学质量技术监督学院,保定071002 [2]河北农业大学机电工程学院,保定071001

出　　处：《农业工程学报》2013年第A01期270-274,共5页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61074174);河北省自然科学基金项目(C2011201096);河北省教育厅项目(2010107)

摘　　要：为提高草莓固酸比和可滴定酸近红外光谱定量模型的性能,该文采用偏最小二乘法提取的潜在变量作为最小二乘-支持向量机模型的输入变量,建立了两指标的近红外定量模型,并与偏最小二乘模型结果进行了比较,建模所使用的光谱范围为6000～12500cm-1。结果表明,草莓可滴定酸和固酸比偏最小二乘模型校正相关系数、校正和预测均方根误差分别为0.430、0.096%、0.096%及0.688、0.926和1.190,而两指标的前10个潜在变量得分作为输入变量的最小二乘—支持向量机模型各项性能均远优于偏最小二乘模型,其校正和预测相关系数、校正和预测均方根误差以及剩余预测偏差分别为:可滴定酸0.965、0.967、0.028%、0.027%、3.881;固酸比0.980、0.973、0.258、0.373、3.111。研究表明,潜在变量作为最小二乘支持向量机模型的输入变量可在较大程度上改善草莓可滴定酸和固酸比指标近红外定量模型的预测性能和稳定性。In order to improve performance of near infrared spectroscopy （NIR） models for quantitative analysis of soluble-solid-content-to-titratable-acidity ratio （SSC-to-TA） and titratable acidity （TA） in fresh strawberry, least squares-support vector machine （LS-SVM） with latent variables （LVs）, extracted by partial least squares （PLS）, as input were used to establish calibration models. And the performance were compared with PLS models. Three hundreds and eighteen fresh strawberry samples of three varieties including ＂Tianbao＂ （n=100）, ＂Fengxiang＂ （n=100） and ＂Mingxing＂ （n=118） were analyzed. The spectral region used in this paper was 6000～12500 cm-1. The detector, scan times and resolution were Pbs, 64 and 8 cm-1 respectively. The internal gold background as the reference spectrum was scanned before samples spectra collection. The reference data of SSC values were measured by a digital refractometer with 0.02°Brix accuracy using temperature correction from 10 to 60 °C. And the TA data were obtained by an acid-base titration method according to the National Standard of the pepole’s republic of China. Before models construction Chauvenet rule was used to detect spectral outliers that should be removed from the sample set, and then concentration outliers were removed based on student residual and leverage values. Various mathematical signal treatments were used and compared when PLS models were constructed, including savitzky-golay smoothing （SG） （points of 5, 15 and 25）, first and second derivative, multiplicative scatter correction （MSC）, and the standard normal variate （SNV）. But the PLS models with these pretreatments either for SSC-to-TA or for TA were deteriorated. The best PLS model was established using full bands raw spectra, with correlation coefficients of calibration, root mean square error of calibration and prediction （rc, RMSEC and RMSEP） of 0.430, 0.096%, and 0.096% for TA; of 0.688, 0.926, and 1.190 for SSC-to-TA, which showed a po

关 键 词：近红外 模型 支持向量机 最小二乘法 草莓 固酸比 可滴定酸 潜在变量 

分 类 号：O657.33[理学—分析化学] TS207.3[理学—化学]