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The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs based on atomic absorption spectrometry (AAS) and colorimetry

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Abstract

Mislabeling is a common fraudulent activity in food marketing as producers take advantage of rising demand for ethically produced, high quality animal products such as free-range table eggs. Detection and prevention of this commercial fraud requires robust and widely available tools that can accurately distinguish table eggs from a variety of sources. In this study, the efficacy of multi-elemental fingerprints to discriminate between cage and free-range table whole eggs was assessed using chemometrics. The elemental concentrations of N, P, K, Ca, Mg, Na, Zn, Cu, Fe, and B in cage and free-range table eggs consisting of 99 specimens, with an 80%:20% split between the calibration and verification sets (83 and 16 specimen, respectively) were determined using Flame-Atomic Absorption spectrometry (AAS) and colorimetry. Principal Component Analysis (PCA) for fingerprint determination was applied in combination with Bayesian Machine Learning (PCA-BML), Support Vector Machine (PCA-SVM), and K-Means Clustering (PCA/K-Means). The classification verification set specimens were identified with accuracy and F1-scores ranging from 81.3- 100.0% and 80–100% respectively. PCA/K-Means was the most effective classification model with sensitivity, precision/specificity, accuracy, and FI-score values of 100% while the false positivity rates (FPR) was 0%. The results demonstrated that AAS and colorimetry derived multi-elemental fingerprints and chemometrics were an effective and feasible tool to discriminate between cage and free-range table eggs. Therefore, AAS and colorimetry multi-elemental fingerprints combined with chemometrics can be used to reduce fraudulent marketing practices and improve quality control in the egg industry due to their wide availability, versality, robustness.

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Acknowledgements

This work is financially supported by the University of Mpumalanga, Research and Innovation. Ms. Aylward Elsabe and Yvette Abercrombie for technical assistance on element analysis. Prof. Moses Mbewe of Faculty of Agriculture and Natural Sciences acknowledged for facilitating the purchase of eggs for this study.

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Authors and Affiliations

  1. Faculty of Agriculture and Natural Sciences, School of Agricultural Sciences, University of Mpumalanga, Mbombela, 1200, South Africa

    Siphosethu Richard Dibakoane, Buhlebenkosi Amanda Dube, Obiro Cuthbert Wokadala & Victor Mlambo

  2. Department of Biotechnology and Food Technology, Tshwane University of Technology, Private Bag X680, Pretoria, 0083, South Africa

    Belinda Meiring

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  1. Siphosethu Richard Dibakoane
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Contributions

Siphosethu Richard Dibakoane: Experimental analysis, Writing – review & editing. Belinda Meiring: Supervision, Writing – review & editing. Buhlebenkosi Amanda Dube: Samples preparation, manuscript writing and editing. Obiro Cuthbert Wokadala: Conceptualization, Methodology, Data analysis, Supervision, Writing – review & editing. Victor Mlambo: Supervision, Writing – review & editing.

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Correspondence to Obiro Cuthbert Wokadala.

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Dibakoane, S.R., Meiring, B., Dube, B.A. et al. The application of multi-elemental fingerprints and chemometrics for discriminating between cage and free-range table eggs based on atomic absorption spectrometry (AAS) and colorimetry. Food Measure 17, 3802–3808 (2023). https://doi.org/10.1007/s11694-023-01899-4

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  • DOI: https://doi.org/10.1007/s11694-023-01899-4

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