Rapid and nondestructive fraud detection of palm oil adulteration with Sudan dyes using portable NIR spectroscopic techniques

Ernest Teye*, Chris Elliott, Livingstone Kobina Sam-Amoah, Cheetham Mingle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Non-destructive, simple and fast techniques for identifying authentic palm oil and those adulterated with Sudan dyes using portable NIR spectroscopy would be very beneficial to West Africa countries and the world at large. In this study, a portable NIR spectroscopy coupled with multivariate models were developed for detecting palm oil adulteration. A total of 520 samples of palm oil were used comprising; 40 authentic samples together with 480 adulterated samples containing Sudan dyes (I, II, III, IV of 120 samples each). Multiplicative scatter correction (MSC) preprocessing technique plus Principal component analysis (PCA) was used to extract relevant spectral information which gave visible cluster trends for authentic samples and adulterated ones. The performance of Linear discriminant analysis (LDA) and Support vector machine (SVM) were compared, and SVM showed superiority over LDA. The optimised results by cross-validation revealed that MSC-PCA + SVM gave an identification rate above 95% for both calibration and prediction sets. The overall results show that portable NIR spectroscopy together with MSC-PCA + SVM model could be used successfully to identify authentic palm oils from adulterated ones. This would be useful for quality control officers and consumers to manage and control Sudan dyes adulteration in red palm oil.

Original languageEnglish
Pages (from-to)1589-1596
Number of pages8
JournalFood Additives and Contaminants - Part A Chemistry, Analysis, Control, Exposure and Risk Assessment
Volume36
Issue number11
Early online date19 Sep 2019
DOIs
Publication statusPublished - 02 Nov 2019

Keywords

  • linear discriminant analysis
  • Palm oil
  • portable NIR spectroscopy
  • quality control
  • support vector machine

ASJC Scopus subject areas

  • Food Science
  • Chemistry(all)
  • Toxicology
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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