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. 2024 May 22:8:100773.
doi: 10.1016/j.crfs.2024.100773. eCollection 2024.

Hyperspectral identification of oil adulteration using machine learning techniques

Muhammad Aqeel  1 , Ahmad Sohaib  1 , Muhammad Iqbal  1   2 , Hafeez Ur Rehman  3 , Furqan Rustam  4
Affiliations

Hyperspectral identification of oil adulteration using machine learning techniques

Muhammad Aqeel et al. Curr Res Food Sci. .

Abstract

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Keywords: Adulteration; Artificial intelligence; Edible oil; Food quality control; Hyperspectral imaging; Machine learning.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image 1
Graphical abstract
Fig. 1
Fig. 1
HSI based proposed model.
Fig. 2
Fig. 2
HSI experimental setup and spectral signature of the oil.
Fig. 3
Fig. 3
Visual difference of the pure oil classes (a–g) and adulterated oil (h).
Fig. 4
Fig. 4
Compressed spectral signatures of oils (a–d) using absorption, steady, median, average, and Savitzky-Golay smoothing filters.
Fig. 5
Fig. 5
The visualization (a to d) of pixel level HSI spectral signature of different oil samples.
Fig. 6
Fig. 6
The HSI spectral signature distinguishes pure and adulterated oils (0–60%). (a): Olive and sunflower oils, (b): Coconut and Paraffin oils, (c): Sunflower oil with Almond oil, (d): Castor oil (4–60%) with Mustard oil.
Fig. 7
Fig. 7
Comparison of performance evaluations of each model.
Fig. 8
Fig. 8
Confusion matric of the proposed model.
See this image and copyright information in PMC

References

    1. Abed H.M., Khairy H.L. The effect of adding pumpkin seed oil on physicochemical and sensory properties of the mayonnaise. IOP Conf. Ser. Earth Environ. Sci. 2023;1214 IOP Publishing.
    1. Abed H.M., Luma Khairy H. The effect of adding pumpkin seed oil on physicochemical and sensory properties of the mayonnaise. IOP Conf. Ser. Earth Environ. Sci. 2023;1214 doi: 10.1088/1755-1315/1214/1/012037. - DOI
    1. Barreto A., Cruz-Tirado J.P., Siche R., Quevedo R. Determination of starch content in adulterated fresh cheese using hyperspectral imaging. Food Biosci. 2018;21:14–19.
    1. Basri K.N., Hussain M.N., Bakar J., Sharif Z., Khir M.F.A., Zoolfakar A.S. Classification and quantification of palm oil adulteration via portable NIR spectroscopy. Spectrochim. Acta Part A Mol Biomol Spectrosc. 2017;173:335–342. - PubMed
    1. Chaudhari B.A., Patel M.P., Dharajiya D.T., Patel A.M., Thakur M.R. 2021. Oil Content and Fatty Acid Composition in Castor (Ricinus communis L.) Genotypes.

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