Seattle Pacific University, 3307 Third Avenue West, Seattle, WA 98119-1950, USA.
Talanta. 2012 May 30;94:320-7. doi: 10.1016/j.talanta.2012.03.050. Epub 2012 Mar 28.
The two main goals of the analytical method described herein were to (1) use principal component analysis (PCA), hierarchical clustering (HCA) and K-nearest neighbors (KNN) to determine the feedstock source of blends of biodiesel and conventional diesel (feedstocks were two sources of soy, two strains of jatropha, and a local feedstock) and (2) use a partial least squares (PLS) model built specifically for each feedstock to determine the percent composition of the blend. The chemometric models were built using training sets composed of total ion current chromatograms from gas chromatography-quadrupole mass spectrometry (GC-qMS) using a polar column. The models were used to semi-automatically determine feedstock and blend percent composition of independent test set samples. The PLS predictions for jatropha blends had RMSEC=0.6, RMSECV=1.2, and RMSEP=1.4. The PLS predictions for soy blends had RMSEC=0.5, RMSECV=0.8, and RMSEP=1.2. The average relative error in predicted test set sample compositions was 5% for jatropha blends and 4% for soy blends.
(1) 使用主成分分析 (PCA)、层次聚类 (HCA) 和 K 最近邻 (KNN) 来确定生物柴油和传统柴油混合物的原料来源（原料是两种大豆、两种麻疯树和一种本地原料）；(2) 使用专门为每种原料构建的偏最小二乘 (PLS) 模型来确定混合物的组成百分比。使用气相色谱-四极杆质谱联用仪 (GC-qMS) 的极性柱的总离子流色谱图构建化学计量模型的训练集。使用这些模型半自动确定独立测试集样品的原料和混合物组成百分比。麻疯树混合物的 PLS 预测值为 RMSEC=0.6、RMSECV=1.2 和 RMSEP=1.4。大豆混合物的 PLS 预测值为 RMSEC=0.5、RMSECV=0.8 和 RMSEP=1.2。麻疯树混合物预测测试集样品组成的平均相对误差为 5%，大豆混合物为 4%。
