Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications

Author:

Shin Sungho1,Doh Iyll-Joon1ORCID,Okeyo Kennedy2,Bae Euiwon3ORCID,Robinson J. Paul12,Rajwa Bartek4ORCID

Affiliation:

1. Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA

2. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA

3. School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA

4. Bindley Bioscience Center, Discovery Park, Purdue University, West Lafayette, IN 47907, USA

Abstract

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Funder

Agricultural Research Service

Center for Food Safety Engineering at Purdue University

Publisher

MDPI AG

Subject

Chemistry (miscellaneous),Analytical Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Molecular Medicine,Drug Discovery,Pharmaceutical Science

Reference62 articles.

1. Food adulteration and contamination in India: Occurrence, implication and safety measures;Gahukar;Int. J. Basic Appl. Sci.,2013

2. An overview of food adulteration: Concept, sources, impact, challenges and detection;Choudhary;Int. J. Chem. Stud.,2020

3. Food adulteration: Sources, health risks, and detection methods;Bansal;Crit. Rev. Food Sci. Nutr.,2017

4. The use of near infrared spectroscopy for determination of adulteration and contamination in milk and milk powder: Updating knowledge;Cattaneo;J. Near Infrared Spectrosc.,2013

5. Laser-induced breakdown spectroscopy (LIBS) for food analysis: A review;Dixit;Trends Food Sci. Technol.,2017

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3