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- MakaleAçık Erişim2026Vibrational Spectroscopy Coupled with Chemometrics for Olive Oil Authentication: Recent Advances and Future Perspectives
İlyasoğlu, Huri
Food authentication has become a critical issue for consumers, producers, and regulatory authorities, particularly for high-value products such as olive oil, which is highly susceptible to adulteration. In recent years, vibrational spectroscopy techniques, including mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopy, have attracted significant attention as rapid and non-destructive analytical tools for assessing the authenticity of olive oil. When combined with multivariate chemometric approaches, these techniques enable the extraction of meaningful spectral fingerprints and the development of predictive models for adulteration detection. This review provides a comprehensive evaluation of studies conducted over the past decade, with a focus on the application of vibrational spectroscopy coupled with chemometrics for the authentication of olive oil. The reviewed studies demonstrate that combining vibrational spectroscopy with chemometric methods offers advantages, including minimal sample preparation, rapid analysis, environmental sustainability, and potential suitability for routine quality control and official food inspection. The current limitations and future research directions are also highlighted to facilitate the extensive implementation of these techniques in olive oil authentication frameworks.
- MakaleYayıncıda açık erişim2022Information theory and machine learning based authentication of flaxseed oil using portable and handheld vibrational spectroscopy sensors
Aykas, Didem-Peren; Menevseoglu, Ahmed; Gunes, Nurhan
Flaxseed oil is one of the popular edible oils, and its adulteration has become a concern because of its high commodity value. Therefore, there is a need to detect adulterated flaxseeds with a rapid, non-invasive, and simple method to keep the quality of flaxseed oil. This study aimed to develop a machine learning algorithm and information theory to detect adulterated flaxseed oils by vibrational spectroscopy units combined with chemometrics. Oil spectra were collected with portable/handheld vibrational spectroscopic units and analyzed using conditional entropy, support vector machines (SVM), and soft independent modeling of class analogy (SIMCA) to generate classification models to identify the pureness of oils and partial least square regression (PLSR) to predict the adulterant levels and fatty acid distributions. Conditional entropy showed that two or three wavenumbers/shifts could discriminate the oils using machine learning algorithms with an accuracy of over 95%. SIMCA models determined the oils with an interclass distance (ICD) over 1.1. Specifically, the Fourier transform mid-infrared (FT-MIR) unit provided the best classification of the oils with ICD over 4.9. All instruments showed good performance in predicting fatty acids and adulteration levels with rval≥0.89 and standard error prediction (SEP) ≤1.7%. Overall, these portable/handheld units showed great potential for rapid, simple, and non-invasive monitoring to identify adulterated flaxseed oils.
- MakaleYalnız künye2021A rapid method to detect green pea and peanut adulteration in pistachio by using portable FT-MIR and FT-NIR spectroscopy combined with chemometrics
Aykas, Didem Peren; Menevseoglu, Ahmed
In this research, the determination of green pea and peanut adulteration in pistachio by portable Fourier transform mid-infrared (FT-MIR) and Fourier transform near-infrared (FT-NIR) spectroscopy was studied. A rapid method to detect adulteration in pistachio is a necessity in the food industry because of its high commodity value and being one of the most consumed tree nuts. Pistachio nut is a target for economically motivated adulteration, and green pea, spinach, and peanut are the most seen adulterants in pistachio. Portable FT-MIR and FT-NIR spectrometers are simple, non-invasive, rapid, easy to transport, and cheaper alternatives to traditional methods, including chromatography (GC, HPLC), mass-spectroscopy (GC-MS, LC-MS) for the detection of pistachio adulteration. Powdered pistachio samples were adulterated with powdered green pea and peanut samples at different concentrations (5–40%). Spectra were collected with a portable FT-MIR and FT-NIR spectrometers and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate a classification model to authenticate pure pistachio, and Partial Least Square Regression (PLSR) to predict the levels of adulterants in pistachio. SIMCA provided, in both units, very distinct clusters for pure and adulterated samples with inter-class distance (ICD) over 3. Both units also showed superior performance in predicting adulterant levels with rVal>0.99, standard error of prediction (SEP) < 2.5%. Based on SIMCA and PLSR models, portable FT-NIR spectrometer provided more advantages over portable FT-MIR unit because the prediction with the FT-NIR spectrometer was more precise than the FT-MIR unit. Both portable FT-MIR and FT-NIR units can be used as alternative methods to traditional methods and showed great potential for real-time surveillance to detect green pea and peanut adulteration in pistachio.
- MakaleYalnız künye2021Non-targeted approach to detect green pea and peanut adulteration in pistachio by using portable FT-IR, and UV-Vis spectroscopy
Menevseoglu, Ahmed; Aykas, Didem Peren; Adal, Eda
Pistachio is one of the most expensive nuts with having high economic importance in Turkey. It has become more prone to adulteration because of its high commodity value. Peanut with color additives and green pea are generally used to adulterate ground pistachio. Vibrational spectroscopy is a potential technique to detect adulterations in pistachio. The objective of this study was to generate a non-targeted method for portable FT-IR and UV-Vis spectrometers to authenticate pistachio and detect green pea and peanut adulterations. Pistachio granules were adulterated with green pea and peanut at different concentrations (5 to 40% w/w). Spectra were collected by a portable FT-IR spectrometer and by a conventional UV-Vis spectrometer and analyzed by Soft Independent Modeling of Class Analogy (SIMCA) to generate classification algorithms to authenticate pistachio, and Partial Least Square Regression (PLSR) to predict the concentrations of adulterants. SIMCA showed very distinct clusters for pure samples. Moreover, adulterated pistachio samples were discriminated by SIMCA even in low levels of adulteration (5%). Portable FTIR showed excellent performance (r(val) > 0.93) of predicting the adulterant levels with a standard error of prediction (SEP) 0.66% and 0.80% for green pea and peanut, respectively. Similarly, UV-VIS predicted (r(val) > 0.93) the adulterant levels with SEP 0.58% and 0.14% for green pea and peanut, respectively. The results supported that portable FT-IR, and UV-Vis units present great potential for real-time surveillance of green pea and peanut adulteration in pistachio.