Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Intelligent Agriculture Engineering Laboratory of Anhui Province, Hefei 230031, China.
Biosensors (Basel). 2021 Aug 3;11(8):261. doi: 10.3390/bios11080261.
The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils.
脂肪酸的组成和含量是鉴别食用油质量的关键指标。本研究旨在建立一种基于棕榈酸、硬脂酸、花生酸和山嵛酸定量分析的食用油质量快速检测方法。对 7 种油进行了 Vis-NIR 光谱测量。采用多元方法结合预处理方法，建立了 4 种脂肪酸的定量分析模型。标准正态变量（SNV）预处理的支持向量机（SVM）模型对 4 种脂肪酸具有最佳的预测性能。对于棕榈酸，预测系数（RP2）为 0.9504，预测均方根误差（RMSEP）为 0.8181。对于硬脂酸，RP2 和 RMSEP 分别为 0.9636 和 0.2965。在预测花生酸时，RP2 和 RMSEP 分别为 0.9576 和 0.0577。在预测山嵛酸时，RP2 和 RMSEP 分别为 0.9521 和 0.1486。此外，连续投影算法（SPA）选择的有效波长可用于建立简化的预测模型。结果表明，Vis-NIR 光谱结合多元方法可为食用油中脂肪酸的检测提供一种快速准确的方法。
