A proof-of-concept study: determining the geographical origin of crude palm oil with the combined use of GC-IMS fingerprinting and chemometrics

Covington J. A., . and Brodrick E., . and Davies A. N., . and Goggin K. A., . and Wicaksono A., . and Murphy D. J., . A proof-of-concept study: determining the geographical origin of crude palm oil with the combined use of GC-IMS fingerprinting and chemometrics. pp. 227-234. ISSN 1511-2780

Current administrative controls used to verify geographical provenance within palm oil supply chains require enhancement and strengthening by more robust analytical methods. In this study the application of volatile organic compound fingerprinting in combination with five different analytical classification models has been used to verify the regional geographical provenance of crude palm oil (CPO) samples. For this purpose 108 CPO samples were collected from two regions within Malaysia namely Peninsular Malaysia (32) and Sabah (76). Samples were analysed by gas chromatography-ion mobility spectrometer (GC-IMS) and the five predictive models (Sparse Logistic Regression Random Forests Gaussian Processes Support Vector Machines and Artificial Neural Networks) were built and applied. Models were validated using 10-fold cross-validation. The area under curve (AUC) measure was used as a summary indicator of the performance of each classifier. All models performed well (AUC 0.96) with the Sparse Logistic Regression model giving best performance (AUC 0.98). This demonstrates that the verification of the geographical origin of CPO is feasible by volatile organic compound fingerprinting using GC-IMS supported by chemometric analysis.

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Abstract

Current administrative controls used to verify geographical provenance within palm oil supply chains require enhancement and strengthening by more robust analytical methods. In this study the application of volatile organic compound fingerprinting in combination with five different analytical classification models has been used to verify the regional geographical provenance of crude palm oil (CPO) samples. For this purpose 108 CPO samples were collected from two regions within Malaysia namely Peninsular Malaysia (32) and Sabah (76). Samples were analysed by gas chromatography-ion mobility spectrometer (GC-IMS) and the five predictive models (Sparse Logistic Regression Random Forests Gaussian Processes Support Vector Machines and Artificial Neural Networks) were built and applied. Models were validated using 10-fold cross-validation. The area under curve (AUC) measure was used as a summary indicator of the performance of each classifier. All models performed well (AUC 0.96) with the Sparse Logistic Regression model giving best performance (AUC 0.98). This demonstrates that the verification of the geographical origin of CPO is feasible by volatile organic compound fingerprinting using GC-IMS supported by chemometric analysis.

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Item Type:	Article
AGROVOC Term:	Palm oils
AGROVOC Term:	Elaeis guineensis
AGROVOC Term:	Crude fibre
AGROVOC Term:	Geographical origin
AGROVOC Term:	Volatile compounds
AGROVOC Term:	Gas chromatography
AGROVOC Term:	Multivariate analysis
AGROVOC Term:	Prediction
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/9953

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