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基于LF-NMR弛豫特性的煎炸油总极性化合物含量定量建模方法 被引量:3

Quantitative Modeling Method for Predicting Total Polar Compound Contents in Frying Oil Based on LF-NMR Relaxation Parameters
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摘要 将低场核磁共振(low field nuclear magnetic resonance,LF-NMR)分析技术应用于煎炸油脂总极性化合物(total polar compounds,TPC)含量的预测。采用柱层析方法测定油脂样品的TPC含量作为测定值,采集油脂样品的LF-NMR弛豫特性(峰起始时间T21、T22、T23相应的峰面积比例S21、S22、S23、单组分弛豫时间T2W),分别利用向后筛选多元回归分析、主成分回归分析和偏最小二乘回归分析建立LF-NMR弛豫特性与TPC含量的回归方程,比较3种模型的校正集和预测集的决定系数与均方根误差,最终确定最优模型为偏最小二乘回归模型。应用此模型预测预测集样品TPC含量,决定系数R2可达0.928,预测集均方根误差为0.568%,模型稳定。 The contents of total polar compounds(TPC) in frying oils were predicted by using low field nuclear magnetic resonance(LF-NMR). LF-NMR T2 relaxation parameters(the relaxation time: T21, T22, and T23, the corresponding peak areas: S21, S22, S23 and the single component time T2W) of the samples were collected. The TPC contents of frying oil were also determined by column chromatography method as a reference standard. Three mathematic models were established to quantitatively analyze TPC contents, using backward multiple linear regression(BMLR), principal component regression(PCR) and partial least squares regression(PLSR), respectively. Comparing the correlation coefficients and root mean square errors of calibration set and prediction set, the BMLR model showed the best reliability in predicting TPC contents, with a correlation coefficient of prediction sets of 0.928, root mean square error of prediction(RMSEP) of 0.568%.
出处 《食品科学》 EI CAS CSCD 北大核心 2014年第24期110-114,共5页 Food Science
基金 国家自然科学基金青年科学基金项目(NSFC31201365) 上海市科委重点攻关项目(11142200403) 上海市教委科研创新项目(11YZ109)
关键词 低场核磁共振 总极性化合物含量 向后多元回归 主成分回归 偏最小二乘回归 low field nuclear magnetic resonance(LF-NMR) total polar compounds(TPC) content multiple linear regression(BMLR) principal component regression(PCR) partial least squares regression(PLSR)
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