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This study (USD$39.95 purchase required) reports a rapid and sensitive ultra-performance liquid chromatography-tandem triple quadrupole mass spectrometry (UPLC-MS/MS) method to detect adulteration of high-value camellia oil and olive oil with lower-cost sesame, soybean, and peanut oils.

Four characteristic markers-sesamin and sesamolin (sesame oil), 4′,7-dimethoxyisoflavone (soybean oil), and sativanone (peanut oil) were identified and quantified with high specificity.

The authors report limits of detection of 0.025%–0.10% for sesame oil, 1.0%–5.0% for soybean oil and peanut oil. Adulteration model experiments and method comparison analysis confirmed reliable multi-component adulteration detection in complex matrices.

Analysis of 106 commercial samples revealed adulteration rates of 16.0% (camellia oil) and 25.0% (olive oil), primarily with soybean oil. The analysis of two law enforcement samples confirmed adulteration with soybean oil, consistent with the official regulatory findings.

The authors conclude that this approach overcomes limitations of traditional methods.

Photo by jonathan ocampo on Unsplash

In this study (purchase required), Fourier transform near-infrared spectroscopy (FT-NIRS) was combined with two distinct machine learning algorithms to detect and quantify the peanut adulteration rate (%) in ground hazelnut.

Ground hazelnut samples were mixed with various levels of peanut content (0–50%). The spectral data were collected in the wavelength (λ) range of 4000–10000 cm⁻¹. Feature selection was carried out using the Lasso and Elastic Net algorithms to determine and eliminate unnecessary spectral variables and improve the accuracy of prediction. The Lasso model was found to be more accurate compared to the Elastic Net model for the same λ value (0.001). The authors report that the prediction accuracy indicator values improved as λ values decreased. Cross-validation confirmed the robustness of the Lasso model, indicating it is highly generalisable.

The authors conclude that FT-NIRS, supported by ML-based feature selection and modelling, provides an efficient, fast and non-destructive approach for the detection and quantification of hazelnut adulteration with ground peanut. This approach offers a rapid, waste-free, and eco-friendly solution to food adulteration detection, aligning with sustainable production principles by minimising sample preparation and resource consumption in the frame of greener analytical workflows.

Photo by David Gabrielyan on Unsplash