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

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.

KEYWORDS:

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

Acknowledgments

The authors acknowledge the support provided by Institute for Global Food Safety, Queen’s University Belfast, UK and School of Agriculture, University of Cape Coast. We also appreciate proofreading assistance provided by Mrs Winifred Akpene Teye.