Jović Ozren
Department of Chemistry, Faculty of Science, University of Zagreb, Horvatovac 102A, HR-10000 Zagreb, Croatia.
Food Chem. 2016 Dec 15;213:791-798. doi: 10.1016/j.foodchem.2016.07.016. Epub 2016 Jul 5.
A novel method for quantitative prediction and variable-selection on spectroscopic data, called Durbin-Watson partial least-squares regression (dwPLS), is proposed in this paper. The idea is to inspect serial correlation in infrared data that is known to consist of highly correlated neighbouring variables. The method selects only those variables whose intervals have a lower Durbin-Watson statistic (dw) than a certain optimal cutoff. For each interval, dw is calculated on a vector of regression coefficients. Adulteration of cold-pressed linseed oil (L), a well-known nutrient beneficial to health, is studied in this work by its being mixed with cheaper oils: rapeseed oil (R), sesame oil (Se) and sunflower oil (Su). The samples for each botanical origin of oil vary with respect to producer, content and geographic origin. The results obtained indicate that MIR-ATR, combined with dwPLS could be implemented to quantitative determination of edible-oil adulteration.
本文提出了一种用于光谱数据定量预测和变量选择的新方法，称为杜宾-沃森偏最小二乘回归（dwPLS）。其思路是检查红外数据中的序列相关性，已知该数据由高度相关的相邻变量组成。该方法仅选择那些区间的杜宾-沃森统计量（dw）低于某个最佳临界值的变量。对于每个区间，dw是根据回归系数向量计算得出的。在这项工作中，通过将冷榨亚麻籽油（L）与价格较低的油混合：菜籽油（R）、芝麻油（Se）和葵花籽油（Su），来研究冷榨亚麻籽油（一种众所周知的有益健康的营养物质）的掺假情况。每种植物油来源的样品在生产商、含量和地理来源方面各不相同。所得结果表明，中红外衰减全反射（MIR-ATR）结合dwPLS可用于食用油掺假的定量测定。
