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<p>Adulteration of edible oils poses significant health risks and economic concerns, highlighting the need for rapid, reliable, and non-destructive detection techniques. This study integrates Fourier Transform Infrared (FTIR) spectroscopy, microwave-based electric parameter measurements, and gas chromatography (GC) with multivariate chemometric methods to detect and quantify palm oil adulteration in coconut and sesame oils. Quality parameters, namely dielectric constant (DC), dielectric loss (DL), iodine value (IV), and refractive index (RF), were predicted using partial least squares regression (PLSR). The models achieved high validation accuracy for DC, DL, and RF (<i>R</i><sup>2</sup> &gt; 0.87). Key FTIR spectral bands at 1654.9&#xa0;cm⁻<sup>1</sup> and 1099.4&#xa0;cm⁻<sup>1</sup>, associated with fatty acid composition, were identified as important markers to differentiate types of oils. SHAP (SHapley Additive exPlanations) and Variable Importance in Projection (VIP) analyses identified FTIR bands associated with ester C = O and unsaturated C = C vibrations as the most influential features. These bands reflect changes in fatty acid saturation and ester composition between pure and adulterated oils, providing chemically interpretable markers for reliable discrimination of adulteration in edible oil. However, all parameters were validated using PLSR with GC spectra as the input, showing high accuracy as <i>R</i><sup>2</sup> &gt; 0.92. In pure coconut oil, GC revealed lower palmitic acid (C16:0), which increased with adulteration, along with higher oleic (C18:1) and linoleic (C18:2) acids. In sesame oil, C18:1 and C18:2 were dominant, but adulteration elevated C16:0 levels, indicating the addition of oils richer in saturated fats. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) effectively differentiated pure and adulterated samples, confirming the consistency of spectral changes across oil types. The combined use of FTIR, dielectric analysis, and explainable machine learning models offers a fast, reagent-free, and interpretable solution for edible oil quality assessment.</p>

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FTIR Spectroscopy Coupled with Machine Learning for Assessment of Dielectric and Physicochemical Properties of Pure and Adulterated Coconut and Sesame Oils

  • 期刊论文

  • 发表日期：

    2026年2月13日

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  • Shruti O. Varma,
  • Ajay L. Vishwakarma,
  • M. R. Sonawane,
  • Ajay Chaudhari

摘要

Adulteration of edible oils poses significant health risks and economic concerns, highlighting the need for rapid, reliable, and non-destructive detection techniques. This study integrates Fourier Transform Infrared (FTIR) spectroscopy, microwave-based electric parameter measurements, and gas chromatography (GC) with multivariate chemometric methods to detect and quantify palm oil adulteration in coconut and sesame oils. Quality parameters, namely dielectric constant (DC), dielectric loss (DL), iodine value (IV), and refractive index (RF), were predicted using partial least squares regression (PLSR). The models achieved high validation accuracy for DC, DL, and RF (R2 > 0.87). Key FTIR spectral bands at 1654.9 cm⁻1 and 1099.4 cm⁻1, associated with fatty acid composition, were identified as important markers to differentiate types of oils. SHAP (SHapley Additive exPlanations) and Variable Importance in Projection (VIP) analyses identified FTIR bands associated with ester C = O and unsaturated C = C vibrations as the most influential features. These bands reflect changes in fatty acid saturation and ester composition between pure and adulterated oils, providing chemically interpretable markers for reliable discrimination of adulteration in edible oil. However, all parameters were validated using PLSR with GC spectra as the input, showing high accuracy as R2 > 0.92. In pure coconut oil, GC revealed lower palmitic acid (C16:0), which increased with adulteration, along with higher oleic (C18:1) and linoleic (C18:2) acids. In sesame oil, C18:1 and C18:2 were dominant, but adulteration elevated C16:0 levels, indicating the addition of oils richer in saturated fats. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) effectively differentiated pure and adulterated samples, confirming the consistency of spectral changes across oil types. The combined use of FTIR, dielectric analysis, and explainable machine learning models offers a fast, reagent-free, and interpretable solution for edible oil quality assessment.