摘要
目的:采用低场核磁共振技术对6个不同品牌的270个奶粉样品进行检测判别。方法:采用主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)、误差反传人工神经网络(BP-ANN)等化学计量学方法对样品数据进行处理。结果:采用PCA方法的主成分三维投影图无法达到对奶粉品牌快速判别的目的;PLS-DA方法的训练集和预测集的正确识别率分别为66.1%,52.2%,可信度较低,也难以实现奶粉品牌的快速判别;BP-ANN方法的训练集和预测集的正确识别率分别为99.4%,100.0%。结论:低场核磁共振与BP-ANN结合可以很好地识别奶粉品牌。
Objective:270 milk powder samples from 6 different brands were detected and distinguished by low field nuclear magnetic resonance combined with chemometrics.Methods:Three chemometrics methods of principal component analysis(PCA),partial least squares discriminant analysis(PLS-DA)and backpropagation artificial neural network(BP-ANN)were used to process experimental data of samples statistically.Results:The PCA method based on three-dimensional projection could not achieve the purpose of rapid identification of milk powder brand;the correct recognition rates of training and prediction sets were 66.1%and 52.2%for the PLS-DA method,respectively,which was low in credibility and challenging to realize the rapid identification of milk powder brand;the correct recognition rates of training and prediction sets of were 99.4%and 100.0%for the BP-ANN method respectively.Conclusion:The combination of low field nuclear magnetic resonance and BP-ANN can identify the milk powder brand well.
作者
杨莉
夏阿林
张榆
YANG Li;XIA A-lin;ZHANG Yu(School of Food and Chemical Engineering,Shaoyang University,Shaoyang,Hunan 422000,China)
出处
《食品与机械》
北大核心
2021年第8期105-109,共5页
Food and Machinery
基金
湖南省教育厅科学研究重点项目(编号:16A236)
邵阳学院研究生创新项目(编号:CX2019SY048)。
关键词
奶粉
品牌
低场核磁共振
化学计量学
判别分析
milk powder
brand
low field nuclear magnetic resonance
chemometrics
discriminant analysis
作者简介
杨莉,女,邵阳学院在读硕士研究生;通信作者:夏阿林(1974-),男,邵阳学院副教授,博士。E-mail:alinxia@126.com。
