Evidence map›Paper›PMID 37630339›Full record

ArticleMolecules (Basel, Switzerland)2023

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

Sungho Shin, Iyll-Joon Doh, Kennedy Okeyo, Euiwon Bae, J Paul Robinson, Bartek Rajwa

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sungho ShinDepartment of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA.
Iyll-Joon DohDepartment of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-2003-5433
Kennedy OkeyoWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA.
Euiwon BaeSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-6095-3223
J Paul RobinsonDepartment of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA.
Bartek RajwaBindley Bioscience Center, Discovery Park, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-7540-8236

Funding

Agricultural Research Service 58-8042-0-061Agricultural Research Service 59-8072-1-002
6 · The paper itself

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.

Indexed as

CheeseSpectrum Analysis, RamanDrug ContaminationFraudIndustrycompact and combined systemfood authenticationlaser-induced breakdown spectroscopy (LIBS)Raman spectroscopysimultaneous atomic and molecular analysis

Identifiers

PMID37630339
PMCPMC10458226

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.