Unveiling Food Authenticity: LIBS Fingerprinting Revolutionizes Analytical Approaches


A researcher group has utilized laser-induced breakdown spectroscopy (LIBS) to develop a method for food authentication and quality analysis.

A recent Spectrochimica Acta Part B: Atomic Spectroscopy paper presents how researchers used laser-induced breakdown spectroscopy (LIBS) to perform quality analysis of food (1). The researchers developed an improved method for identifying spectral patterns, enabling accurate classification and recognition of food samples (1). Their work helps start a conversation that addresses the concerns of food fraud that have been ongoing.

Top view composition of various Asian food in bowl | Image Credit: © Jag_cz - stock.adobe.com

Top view composition of various Asian food in bowl | Image Credit: © Jag_cz - stock.adobe.com

LIBS is an elemental analysis technique that has garnered significant attention for its rapid measurements and minimal sample preparation requirements (1). Expanding its applications into food safety and quality assessment has become an important focus for researchers (1). However, the modest trace-element variations present in most consumed foods necessitate the discovery of predictive spectral patterns through advanced data analysis techniques (1).

In this study, the research team evaluated the performance of spectral variable selection using elastic-net multinomial logistic regression (1). Their objective was to assess the efficacy of multivariate analysis and machine-learning algorithms in identifying the most influential spectral features for class recognition and classification. To accomplish this, both a custom-developed benchtop LIBS system and a commercially available portable device were employed (1).

Specifically, the researchers employed elastic-net multinomial logistic regression for spectral variable selection (1). By training the machine learning model on the collected LIBS data, they were able to identify the most predictive spectral features and improve the accuracy of class recognition and classification for food samples (1). This integration of machine learning techniques with LIBS technology enhances the effectiveness of food authentication and enables the detection of potential food fraud (1).

The results of their investigation demonstrated the immense potential of LIBS fingerprinting for food authentication and identification. By carefully selecting a reduced set of variables, the researchers achieved a significant reduction in model overfitting, leading to enhanced accuracy in LIBS pattern classification (1). This approach not only improves the reliability of food authenticity assessment but also showcases the feasibility of implementing portable LIBS equipment in real-world scenarios (1).

Food fraud includes acts such as mislabeling, adulteration, and counterfeiting. All the aforementioned items contribute to the threat of ruining consumer trust and damaging public health in a significant way (1). LIBS fingerprinting, as the research team shows, can propel the field of food authentication forward by being a valuable tool that can help fight these fraudulent practices. The ability to rapidly analyze food samples and provide accurate identification not only safeguards consumers but also enables regulatory bodies and industry stakeholders to take effective measures against food fraud (1).

This study opens up new avenues for the development of field-deployable, portable LIBS equipment specifically designed for food authentication and fingerprinting. As researchers continue to explore and refine this innovative technology, it promises to play a pivotal role in safeguarding the integrity and trustworthiness of our global food supply chain.


(1) Shin, S.; Wu, X.; Patsekin, V.; Doh, I.-Y.; Bae, E.; Robinson, J. P.; Rajwa, B. Analytical approaches for food authentication using LIBS fingerprinting. Spectrochimica Acta Part B: At. Spectrosc. 2023, 205, 106693. DOI: 10.1016/j.sab.2023.106693

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