Classification of Edible Oils by Infrared Spectroscopy with Optimized K-Means Clustering by a Hybrid Particle Swarm Algorithm

Abstract

The K-means algorithm has some limitations including dead-unit properties, heavy dependence on the initial choice of cluster centers, convergence to local optima, and sensitivity to the number of clusters. This paper presents an efficient algorithm that optimizes K-means clustering by a hybrid particle swarm algorithm. The modified discrete algorithm is used to select variables and is continuously applied to update cluster centers simultaneously. The nearest center classification is then employed to classify the test samples. The proposed algorithm was applied to discriminate various edible oil varieties by employing Fourier transform infrared spectroscopy. As a comparison, the common K-means clustering, principal component analysis, and partial least squares techniques were also applied to classify these edible oil samples. Results demonstrated that the proposed method is an accurate and rapid strategy for identifying edible oils.

Keywords:

• Edible oil
• FT-IR spectra
• K-means algorithm
• Particle swarm algorithm

Acknowledgments

The work was financially supported by the National Natural Science Foundation of China (Grant No. 21175119, 51243004).