Abstract
Lard adulteration in food products is undesirable particularly among people with certain diet preference or religious restrictions. Previous attempts on detecting lard in wheat-based biscuits using PCR-based method were inconsistent due to the minute amount of DNA present in lard and entrapment of DNA in the starch matrix. Hence, alternative method using fatty acid–based approach is necessary. The present study aimed to detect lard adulterated in wheat biscuit using chemometrics and machine learning–assisted GCMS. Oil was extracted from the laboratory-prepared wheat biscuits using Soxhlet extraction method, converted to fatty acid methyl ester and analysed using GCMS. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were able to cluster lard, wheat biscuits and lard-adulterated samples based on their fatty acid distribution. Random forest outperformed partial least squares-discriminant analysis (PLS-DA) in sample classification. Feature selection using random forest identified two fatty acids as potential biomarkers. C18:3n6 is proposed as the potential biomarker in discriminating pure wheat biscuits and lard-adulterated biscuits due to its dose-dependent composition with lard addition.
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Acknowledgements
The authors thank Mohamed Hanif Hanafy Idris and `Aliya Afnan Mohd Nasir for their technical contributions in the oil extraction and GCMS analysis of samples. Nur Inani Azizan is a beneficiary of Graduate Research Fellowship (GRF) from University Putra Malaysia (UPM).
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This study was supported by Universiti Putra Malaysia through Putra Grant (GP-IPM/2017/9539400) being awarded to Nur Fadhilah Khairil Mokhtar.
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Nur Inani Azizan: investigations, data analysis, draft preparation and writing. Nur Fadhilah Khairil Mokhtar: conceptualization, resources and project administration. Syariena Arshad: investigation and resources. Siti Nurhidayah Sharin: methodology and data analysis. Nornazliya Mohamad: data analysis. Shuhaimi Mustafa: conceptualization and supervision. Amalia Mohd Hashim: conceptualization, methodology, supervision, review and editing.
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Azizan, N.I., Mokhtar, N.F.K., Arshad, S. et al. Detection of Lard Adulteration in Wheat Biscuits Using Chemometrics-Assisted GCMS and Random Forest. Food Anal. Methods 14, 2276–2287 (2021). https://doi.org/10.1007/s12161-021-02046-9
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DOI: https://doi.org/10.1007/s12161-021-02046-9