adulteration

In the realm of analytical chemistry, multivariate calibration involves creating mathematical models that connect diverse instrumental signals with analyte concentrations. This approach provides a mean to quantitatively analyze complex mixtures, particularly in multicomponent systems. To address food adulteration concerns, this paper explores the application of Raman spectroscopy and Partial Least Squares Regression (PLSR) using the MVC1 software. The main objective is to demonstrate the software's efficiency in quantifying the adulteration of hazelnut oil in extra virgin olive oil (EVOO). The analysis leverages the MVC1 software, a valuable tool for multivariate linear and nonlinear calibrations. One-leave-out cross-validation and the Durbin-Watson statistical test are employed to determine the optimal number of PLS factors and identify outliers. Statistical parameters including RMSEP, %REP, R², and explained variance are used to evaluate the calibration model's performance. Key figures of merit including sensitivity, analytical sensitivity, LOD, and LOQ, are computed to assess the analytical technique's precision and reliability.Results and The study effectively quantifies the percentage of adulteration in EVOO by hazelnut oil, a pressing concern in food authenticity and safety. The results demonstrate the MVC1 software's capability in establishing reliable calibration models. By achieving a balance between sensitivity and analytical sensitivity, the model accurately predicts analyte concentrations. It also sets robust detection and quantitation limits, ensuring precise analysis. This research showcases the practical application of advanced analytical techniques and software tools to address real-world problems, contributing to the authenticity and purity of food products in the market.

In response to the escalating demand for food products, the assessment of quality and safety has become paramount for regulatory authorities, industry stakeholders, and consumers. This review comprehensively examines the applications of Raman spectroscopy in food evaluation, focusing on its efficacy in detecting food adulteration, unauthorized additives, antibiotics, drugs, residues of pesticides, fungicides, and heavy metals. Results and

While conventional analytical techniques often suffer from issues such as time consumption, high costs, destructiveness, and dependence on the skilled personnel, there is a collective effort in the scientific community to explore innovative diagnostic approaches. Among the growing methodologies, Raman spectroscopy has emerged as a promising candidate due to its simplicity, rapidity, non-destructiveness, and high accuracy. Furthermore, it investigates its utility in identifying foodborne pathogenic microorganisms. Drawing insights from diverse research reports, this study highlights Raman spectroscopy as a potent tool for monitoring and ensuring food quality and safety. As advancements continue to be revealed, it is anticipated that the industrial application of this method will witness significant growth in the future.