**5. Conclusions**

The LIBS technique, paired with supervised statistical learning methods, has been evaluated in real-world applications as a rapid and robust classifier of high-value food items based on their distinctive spectral fingerprints. This study aimed to demonstrate that an existing field-deployable LIBS device originally built for material science applications may provide a rapid, easy, and inexpensive authentication platform for agricultural products where minimal or no sample preparation is required. To achieve this purpose, our study utilized new, easy, and cost-effective sample preparation techniques for liquid and powdered food samples. Utilizing nitrocellulose paper for liquid food samples improved the quality of the spectra and allowed us to avoid the typical sample splashing caused by LIBS-generated shock waves. The LIBS signal of nitrocellulose paper is readily

distinguished from the spectra of tested food samples. It has also been demonstrated that accurate analysis of solid foods such as cheeses and entire coffee beans may be performed using LIBS without any sample preparation.

Overall, the results point to the feasibility of rapid identification of various high-value foods by LIBS accompanied by supervised classification methods, using not only lab-based benchtop instruments but also portable, field-deployable units.

**Author Contributions:** Conceptualization, B.R.; methodology, B.R.; software, B.R. and V.P.; instrumentation, E.B. and J.P.R.; data acquisition, C.G., X.W. and S.S.; statistical analysis, S.S. and B.R.; resources, J.P.R.; data curation, S.S.; writing—original draft preparation, X.W.; writing—review and editing, B.R., J.P.R., E.B. and S.S.; funding acquisition, B.R., E.B. and J.P.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research represents the subproject "Development of machine learning tools for LIBS food fingerprinting and classification" and is a part of the multi-investigator "Development of Innovative Technologies and Strategies to Mitigate Biological, Chemical, Physical, and Environmental Threats Food Safety" project supported by the Center for Food Safety Engineering at Purdue University, funded by the U.S. Department of Agriculture, Agricultural Research Service, under Agreement No. 59-8072-1-002. Opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not reflect the view of the U.S. Department of Agriculture.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank Lisa J. Mauer for her help with water-activity measurements.

**Conflicts of Interest:** The authors declare no conflict of interest.
