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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Characterization and Interventions for Foodborne Pathogens » Research » Publications at this Location » Publication #406423

Research Project: Development of Innovative Technologies and Strategies to Mitigate Biological, Chemical, Physical, and Environmental Threats to Food Safety

Location: Characterization and Interventions for Foodborne Pathogens

Title: Analytical approaches for food authentication using LIBS fingerprinting

item SHIN, SUNGHO - Purdue University
item WU, XI - Purdue University
item PATSEKIN, VALERY - Purdue University
item DOH, IYLL-JOON - Purdue University
item BAE, EUIWON - Purdue University
item ROBINSON, J - Purdue University
item RAJWA, BARTEK - Purdue University

Submitted to: Spectrochimica Acta B
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/28/2023
Publication Date: 4/29/2023
Citation: Shin, S., Wu, X., Patsekin, V., Doh, I., Bae, E., Robinson, J.P., Rajwa, B. 2023. Analytical approaches for food authentication using LIBS fingerprinting. Spectrochimica Acta B. 205:106693.

Interpretive Summary: The authenticity of foods can be an important food safety issue as well as an economic one. The techniques that are available to authenticate foods are somewhat limited and a method for rapid analysis in the field would be beneficial. This research examines using chemical analyses with laser-induced breakdown spectroscopy (LIBS) for food authentication to ensure food safety and quality. LIBS is a quick way to analyze elements in food without laborious sample preparation. We tested both a custom-made LIBS system and a portable one that's commercially available. We applied machine learning methods to the LIBS results in order to enable food authentication and identification, which helps address food fraud. Our results suggest that portable LIBS equipment used for real-world food authentication and fingerprinting scenarios can benefit significantly from using machine learning methods and can contribute significantly to addressing food fraud.

Technical Abstract: Laser-induced breakdown spectroscopy (LIBS) is a widely acknowledgedelemental analysis approach used in various study domains due to its rapid measurement capability and minimal sample-preparation requirements. Recently, there has been an increase in interest in the applications of LIBS in the realm of food safety and quality. Given that the majority of commonly consumed foods exhibit only modest trace-element variations, discovering predictive spectral patterns through multivariate analysis is crucial for the data-analysis pipeline. The efficacy of multivariate analysis and machine-learning algorithms to identify the most predictive spectral features, conduct class recognition and classification was evaluated in this paper, utilizing both a custom-developed benchtop LIBS system and a commercially available portable one. Specifically, this study's objective was to evaluate the performance of spectral variable selection using elastic-net multinomial logistic regression. The data processing pipeline and the LIBS hardware were evaluated in the context of food authentication and identification, a rising field of research addressing the issue of food fraud. Our findings indicated that classifying food samples with carefully selected fewer variables reduces model overfitting and improves the accuracy of LIBS pattern classification. In a broader sense, the results support the continued development of field-deployable, portable LIBS equipment designed for food authentication and fingerprinting activities.