多元统计分析在小麦粉产地溯源中的应用

目的筛选小麦粉产地溯源特征元素,为深入挖掘食品安全风险监测数据,开发成熟有效的食品溯源技术积累基础。方法采用电感耦合等离子体质谱法测定河北省、新疆维吾尔自治区和江苏省共计173份小麦粉样品中的10种无机元素含量,利用主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)、正交偏最小二乘判别分析(OPLS-DA)建立模式识别模型,考察建模效果。结果 PCA模型可以将新疆维吾尔自治区样品与其他两省实现分离;PLS-DA可以实现3个地区样品的分离;河北省和新疆维吾尔自治区、江苏省和新疆维吾尔自治区均在OPLSDA模型中得到良好分离。结论利用PCA、PLS-DA和OPLS-DA三种多元统计分析方法对河北省、新疆维吾尔自治区和江苏省3个地区小麦粉中10种无机元素进行分析,筛选出铜(Cu)、铁(Fe)和砷(As)三个特征元素,这些特征元素有望被应用于小麦粉产地溯源。

The application of multivariate data analysis to determine the geographical origin of wheat flour

Objective Screening characteristic elements in wheat starch for geographical origin. Building foundation for developing mature and effective food traceability technology by analyzing the data of food safety risk monitoring. Methods The concentrations of 10 elements in 173 wheat flour samples from Hebei, Xinjiang and Jiangsu Provinces were determined by inductively coupled plasma mass spectrometry. Principal component analysis(PCA), partial least squares discriminant analysis(PLS-DA)and orthogonal partial least-squares discriminant analysis(OPLS-DA)models were implemented for data analysis. Results The result of PCA model could be separated. The samples from Xinjiang were isolated from other provinces in PCA score scatter plot. The samples of the three provinces could achieve separation by each other in PLS-DA score scatter plot. The samples from Hebei and Xinjiang could be isolated in OPLS-DA score scatter plot as well as Jiangsu and Xinjiang. Conclusion Cu, Fe, and As were the characteristic elements for determining the geographical origin of wheat flour by multivariate data analysis such as PCA, PLS-DA and OPLS-DA.