Non-targeted detection of adulterants in almond powder using spectroscopic techniques combined with chemometrics

Authors: FAQEERZADA, MOHAMMAD - Chungnam National University; LOHUMO, SANTOSH - Chungnam National University; JOSHI, RAHUL - Chungnam National University; Kim, Moon; BAEK, INSUCK - Orise Fellow; CHO, BYOUNG-KWAN - Chungnam National University

Submitted to: Foods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2020
Publication Date: 7/3/2020
Citation: Faqeerzada, M., Lohumo, S., Joshi, R., Kim, M.S., Baek, I., Cho, B. 2020. Non-targeted detection of adulterants in almond powder using spectroscopic techniques combined with chemometrics. Foods. 9(7), 976. https://doi.org/doi:10.3390/foods9070876.
DOI: https://doi.org/10.3390/foods9070876

Interpretive Summary: Effective efforts to ensure authenticity of food materials must include methods to detect both targeted adulterants, already known to be of interest, and unknown adulterants. Unknown adulterants can be more difficult and require different methods of detection. This study investigated the use of Fourier Transform spectroscopic infrared (IR) and near-infrared (NIR) spectroscopy measurements with non-targeted chemometric analysis methods to detect adulterants in almond powder. IR and NIR measurements of pure almond powders were collected and used to create adulterant detection models that were then tested using additional samples that were mixed with varying concentrations of peanut and apricot powders. Detection accuracies between 89% and 100% were achieved, demonstrating that these IR- and NIR-based detection methods could be used for spectroscopy-based high-throughput screening of almond powder for potential adulterants in food processing systems. These results provide the basis of a potential tool to help processors and regulators ensure safety and authenticity of powdered food materials for consumers.

Technical 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 89–100% 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 91–100% for the different validation 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.