123
Views
6
CrossRef citations to date
0
Altmetric
CHEMOMETRICS

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

, , &
Pages 2727-2738 | Received 16 Apr 2013, Accepted 12 May 2013, Published online: 15 Oct 2013
 

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.

Acknowledgments

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

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 768.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.