Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

Tachie, Christabel Y.E.; Obiri-Ananey, Daniel; Alfaro-Cordoba, Marcela; Tawiah, Nii Adjetey; Aryee, Alberta N.A.

doi:10.1016/j.foodchem.2023.137077

Cited by 9 publications

(1 citation statement)

References 31 publications

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“…Few studies have involved the use of ML-based methods for similar matrices. Here, it is worth mentioning the use of the fatty acid profile in combination with deep learning for edible oil adulteration [17], the identification of olive oil adulteration using fatty acid compositions and ML models [16], the prediction of fatty acid classes in snacks [42], and the authentication of oils and margarine [43].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Covaciu,

Berghian-Grosan,

Hategan

et al. 2023

Foods

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Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Covaciu,

Berghian-Grosan,

Hategan

et al. 2023

Foods

View full text Add to dashboard Cite

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Comparison of machine learning models for classifying edible oils using Fourier‐transform infrared spectroscopy

Lim,

Lee,

Kim

et al. 2024

Bulletin Korean Chem Soc

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Accurate classification and authentication of edible oils are essential for maintaining product quality, ensuring consumer safety, and preserving market integrity. Therefore, this study aims to propose Fourier‐transform infrared (FT‐IR) spectroscopy, combined with advanced machine learning models, as a rapid and non‐destructive technique for classifying edible oils. The FT‐IR spectra of seven edible oil types were analyzed across three spectral regions: the full range, the C‐H stretching range, and the fingerprint region. Both absorbance and second derivative spectra were used to evaluate the influence of spectral preprocessing on classification accuracy. Six machine learning models—principal component analysis followed by linear discriminant analysis (PCA‐LDA), k‐nearest neighbors, decision tree, random forest, eXtreme Gradient Boosting, and support vector machines (SVM)—were employed to classify the oils, achieving training accuracies of 96.4%–100% and testing accuracies of 88.1%–100%. The second derivative spectra enhanced model performance by improving the resolution of overlapping peaks, particularly in the CH and CO stretching regions. Additionally, the SHapley Additive exPlanations analysis further revealed the most critical spectral features influencing model predictions, offering valuable insights into the decision‐making processes. This study demonstrates the effectiveness of combining FT‐IR spectroscopy, second derivative preprocessing, and machine learning techniques for classifying edible oils. The findings highlight the benefits of second derivative spectra in enhancing spectral resolution and the superior classification performance of PCA‐LDA and SVM models. These results offer a robust framework for advancing edible oil analysis and emphasize the potential of artificial intelligence in food authentication and quality control.

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Hyperspectral identification of oil adulteration using machine learning techniques

Aqeel,

Sohaib,

Iqbal

et al. 2024

Current Research in Food Science

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Classification of oils and margarines by FTIR spectroscopy in tandem with machine learning

Cited by 9 publications

References 31 publications

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Comparison of machine learning models for classifying edible oils using Fourier‐transform infrared spectroscopy

Hyperspectral identification of oil adulteration using machine learning techniques

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