Abstract
Methods that combine targeted techniques and chemometrics for analyzing food authenticity can only facilitate the detection of predefined or known adulterants, while unknown adulterants cannot be detected using such methods. Therefore, the non-targeted detection of adulterants in food products is currently in great demand. In this study, FT-IR and FT-NIR spectroscopic techniques were used in combination with non-targeted chemometric approaches, such as one-class partial least squares (OCPLS) and data-driven soft independent modeling of class analogy (DD-SIMCA), to detect adulterants in almond powder adulterated with apricot and peanut powders. The reflectance spectra of 100 pure almond powder samples from two different varieties (50 each) were collected to develop a calibration model based on each spectroscopic technique; each model was then evaluated for four independent sets of two varieties of almond powder samples adulterated with different concentrations of apricot and peanut powders. Classification using both techniques was highly sensitive, the OCPLS approach yielded 90–100% accuracy in different varieties of samples with both spectroscopic techniques, and the DD-SIMCA approach achieved the highest accuracy of 100% when used in combination with FT-IR in all validation sets. Moreover, DD-SIMCA, combined with FT-NIR, achieved a detection accuracy between 91% and 100% for the different validation sets and the misclassified samples belong to the 5% and 7% adulteration sets. These results suggest that spectroscopic techniques, combined with one-class classifiers, can be used effectively in the high-throughput screening of potential adulterants in almond powder.
Highlights
Food authentication networks are expanding to meet the demands of determining the safety of supplemented food products, to assess whether a food product matches the package description and adheres to regulations, and to offer advanced, non-destructive technologies
We investigated the feasibility of using Fourier transform infrared (FT-IR) and Fourier transform near-infrared (FT-NIR) spectroscopic techniques combined with one-class classification (OCC) approaches for the non-targeted detection of adulterants in almond powder
This study primarily aimed to develop a non-targeted detection model based on FT-IR and FT-NIR spectroscopic techniques for the detection of unknown adulterants in almond powder samples
Summary
Food authentication networks are expanding to meet the demands of determining the safety of supplemented food products, to assess whether a food product matches the package description and adheres to regulations, and to offer advanced, non-destructive technologies. The trend of adulterated high-value powdered food products has risen as a result of production practices that encourage and enable the illegal gain of profits. Owing to its high price and high global consumption, almond powder has become highly susceptible to economic adulteration with powders of less expensive nuts. Apricot powder is the most typical adulterant in almond powder, and is used because it has a lower price and similar color, texture, odor, and other physical characteristics compared to almond [2]. Peanuts are the second most commonly used almond adulterant globally; they have a substantially similar chemical composition and a considerably lower price. It is essential to develop promising tools and techniques for determining the purity of almond powder
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