Feasibility of Using NIR Spectroscopy with SVM to Identify Kinds of Oil in Character Components

Qiang XU, Shuang WU, Xiao ZHENG

Abstract


To identify the type of edible oil, we proposed a near-infrared (NIR) spectral analysis method based on the contents of four characteristic components: iodine value, palmitic acid, oleic acid, and linoleic acid. Built support vector machine qualitative models that can identify eight kinds of oil. The four characteristic component values of 129 oil samples from 8 kinds of oil were collected and detected. Established three SVC identification models by using three parameter optimization methods including genetic algorithm, grid search and particle swarm optimization. The results showed that the prediction sets’ accuracy rates of all the three models were up to 100%. Especially, both the accuracy rates of the correction and prediction sets of the particle-swarm-optimization-support-vector-machine classification (PSO-SVC) model reached 100%. The results indicate that it is effective and feasible to use the contents of characteristic components to identify the type of edible oil, and this method is fast, convenient, and accurate.

Keywords


Edible oil, Characteristic component, Near infrared spectroscopy, Support vector machine classification


DOI
10.12783/dtcse/amms2018/26194

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