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偏最小二乘线性拟合滴定甘蔗糖蜜中多组分有机酸

Titration analysis of multi-organic acids in sugarcane molasses by partial least squares regression
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摘要 将滴定体系调节至pH 2.0,用碱标准溶液滴定至特定pH所消耗滴定剂为测量指标,构建了多组分有机酸滴定数据阵,分别以主成分回归法、偏最小二乘法以及人工神经元网络法进行多组分拟合。结果表明,偏最小二乘法的拟合结果最佳,对混合体系中乙酸、乳酸、草酸、琥珀酸、柠檬酸和乌头酸总量的相对预测均方根误差分别为5.80%、8.88%、7.40%、5.55%和3.16%。应用该法对两种糖蜜中有机酸进行了分析并与离子色谱分析结果作了对照。 For multi-organic acid mixture,adjusted to pH 2.0,the consumed titrant to designated pH in the titration is taken to construct measurement matrix,some multivariate calibration models,including principal component regression(PCR),partial least squares regression(PLS),and artificial neural networks(ANN) applied on the titrating data set to multi-components analysis.Among the three kinds of models for prediction the PLS is the best.The relative mean square root errors of prediction results of acetic acid,lactic acid,oxalic acid,succinic acid,and the total amount of citric acid and aconitic acid obtained by PLS in mixture are 5.80%,8.88%,7.40%,5.55% and 3.16% respectively.Some organic acids in two sugarcane molasses samples are determined and compared with results from ion chromatography.
出处 《分析试验室》 CAS CSCD 北大核心 2011年第3期9-12,共4页 Chinese Journal of Analysis Laboratory
基金 广西壮族自治区科技厅基金(桂科基0778006-16)资助
关键词 偏最小二乘法 多变量校正 有机酸分析 甘蔗糖蜜 Partial least squares regression Multivariate calibration Mixed organic acids Sugarcane molasses
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