Nathalie Przygodzki-Lionet | Food Safety | Best Researcher Award

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Best Researcher Award

Nathalie Przygodzki-Lionet
University of Lille, France
Nathalie Przygodzki-Lionet
Affiliation University of Lille
Country France
Scopus ID 6507867701
Documents 21
Citations 98
h-index 6
Subject Area Food Safety and Quality Control
Event International Food Scientist Awards

Nathalie Przygodzki-Lionet is a researcher affiliated with the University of Lille, France, whose scholarly profile is associated with food safety and quality control. The supplied bibliometric information records 21 documents, 98 citations, and an h-index of 6 in Scopus. Her research profile can be considered within the broader scientific fields concerned with food safety, quality assessment, analytical methods, risk evaluation, and the protection of consumers across food systems.[1]

Abstract

This academic recognition profile presents the research background of Nathalie Przygodzki-Lionet, University of Lille, France. The supplied Scopus record identifies 21 documents, 98 citations, and an h-index of 6. Her research area is identified as Food Safety and Quality Control, a multidisciplinary field encompassing microbiological, chemical, analytical, regulatory, and quality-related aspects of food systems. Researcher identifiers such as Scopus Author ID and ORCID provide persistent mechanisms for scholarly identification and publication discovery.[1] [2]

Keywords

Food Safety, Food Quality, Quality Control, Food Analysis, Risk Assessment, Food Microbiology, Consumer Protection, Food Science, Research Impact, Bibliometrics.

Introduction

Food safety and quality control are important components of modern food science because they address the safety, consistency, authenticity, and suitability of food products. Research in this area may involve the detection and characterization of hazards, assessment of food quality, analytical validation, monitoring procedures, and development of methods supporting reliable food production. International research evaluation commonly combines publication records with citation-based indicators to describe scholarly activity.[3]

Research Profile

Przygodzki-Lionet is affiliated with the University of Lille in France. Her supplied academic profile identifies Food Safety and Quality Control as the principal subject area. The combination of publication activity and citation indicators provides a quantitative description of her indexed research presence, while the subject classification places the work within a field that connects food science, analytical research, public health, and quality assurance.[1]

  • Affiliation: University of Lille
  • Country: France
  • Scopus Documents: 21
  • Scopus Citations: 98
  • h-index: 6

Research Contributions

The research domain associated with this profile concerns scientific approaches to food safety and quality control. Such research can contribute to improved hazard identification, food-quality monitoring, analytical reliability, and evidence-based assessment of food products. The academic value of individual contributions is appropriately considered through the specific methodology, publication venue, collaboration, reproducibility, and relevance of each study rather than through bibliometric measures alone.[3]

Publications

The supplied Scopus information indicates 21 indexed documents associated with the researcher. These publications form the documented basis for evaluating research activity within the stated subject area. A complete publication assessment should consider article-level metadata, authorship, journal information, citation context, and persistent identifiers such as DOI records where available.[1]

  • Total indexed documents: 21
  • Total citations: 98
  • h-index: 6
  • Research domain: Food Safety and Quality Control

Research Impact

The supplied bibliometric indicators record 98 citations and an h-index of 6 across 21 documents. The h-index was introduced as a measure combining publication productivity and citation impact, although its interpretation varies across disciplines and career stages.[4] Citation counts likewise depend on publication age, field characteristics, collaboration patterns, and database coverage. Consequently, these metrics provide useful quantitative context but do not independently describe the full scientific value of a researcher’s work.

Award Suitability

For consideration under the Best Researcher Award at the International Food Scientist Awards, the supplied profile documents research activity in Food Safety and Quality Control, an established area of food-science research. The reported publication, citation, and h-index data provide quantitative evidence that can be considered alongside publication quality, originality, research methodology, collaboration, scientific relevance, and other nomination materials. Final award assessment should rely on the applicable evaluation criteria and independently verified evidence.

Conclusion

Nathalie Przygodzki-Lionet is associated with the University of Lille, France, with a documented research profile in Food Safety and Quality Control. The supplied Scopus information records 21 documents, 98 citations, and an h-index of 6. Her ORCID and Scopus identifiers provide persistent routes for scholarly identification. Together, these data establish a concise academic profile for consideration within the International Food Scientist Awards framework.

References

  1. Elsevier. (n.d.). Scopus author details: Nathalie Przygodzki-Lionet, Author ID 6507867701. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=6507867701
  2. ORCID. (n.d.). Nathalie Przygodzki-Lionet: ORCID record 0000-0001-5387-6039. ORCID.
    https://orcid.org/0000-0001-5387-6039
  3. World Health Organization. Child Interviewing Practices: Effects of the Implementation of the NICHD Protocol Training in France.
    https://psycnet.apa.org/record/2025-98057-001
  4. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
    https://doi.org/10.1073/pnas.0507655102
  5. Acceptance and use of the NICHD child investigative interview protocol: Testing the unified theory of acceptance and use of technology (UTAUT).
    https://www.sciencedirect.com/org/science/article/abs/pii/S2009382924000444

Wenfeng Han | Food Safety and Quality Control | Excellence in Food Science Education

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Dr. Wenfeng Han | Food Safety and Quality Control | Excellence in Food Science Education

Zhejiang Industry Polytechnic College | China

Dr. Wenfeng Han is an Associate Professor and accomplished food scientist specializing in postharvest science and technology of horticultural products. She holds a Ph.D. in Agriculture from Hunan Agricultural University, where her research focused on the inhibition mechanisms of Nε-carboxymethyllysine (CML) in foods using natural polyphenols. With over two decades of academic and industry experience, she has made significant contributions to food safety, processing technology, and nutritional quality enhancement. Dr. Han has published extensively in peer-reviewed journals, contributed to multiple textbooks, and holds several patents and research grants as Principal Investigator. Her work emphasizes low glycemic index foods, functional food development, and advanced analytical techniques. Recognized with numerous awards and honors, she is also an active reviewer and academic mentor, contributing to innovation and excellence in food science and technology.

Citation Metrics (Scopus)

120

90

60

30

0

Citations
103

Documents
12

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

Featured Publications

Qing Liang | Food Safety and Quality Control | Best Researcher Award

Posted on by Food Scientist

Ms. Qing Liang | Food Safety and Quality Control | Best Researcher Award

College of Mechanical and Electrical Engineering at College of Mechanical and Electrical Engineering, China

Qing Liang is a graduate student in Mechanical Engineering with a strong focus on food safety and quality control. His research combines non-destructive testing methods, such as dielectric spectroscopy, with machine learning to address challenges in food quality, particularly within the dairy industry. Liang has contributed to several publications in well-regarded journals, demonstrating his expertise and commitment to advancing food science. With a passion for innovation, he is working towards bridging the gap between mechanical engineering and food safety. Liang’s ongoing research positions him as a promising young researcher in the fields of engineering and food technology.

Professional Profile

Education

Qing Liang is currently pursuing a postgraduate degree in Mechanical Engineering, graduating in the class of 2023. His academic journey has been marked by a strong foundation in engineering principles, alongside specialized focus areas such as non-destructive testing and machine learning applications in food safety and quality control. Liang’s academic training has equipped him with advanced problem-solving skills, research methodologies, and a deep understanding of both mechanical engineering and food science. His rigorous coursework and research have allowed him to contribute to innovative solutions in food safety, further solidifying his expertise in the intersection of engineering and food technology.

Professional Experience

Qing Liang currently works as a graduate student researcher at the Xinjiang Production and Construction Corps Key Laboratory of Utilization and Equipment of Special Agricultural and Forestry Products in Southern Xinjiang, China. In this role, he focuses on developing and testing innovative solutions for food safety and quality control, particularly within the dairy industry. His professional experience spans non-destructive testing, machine learning, and food safety methodologies. Qing has contributed to various projects that explore advanced techniques like dielectric spectroscopy to improve the quality and safety of agricultural products. Through his research, he actively bridges mechanical engineering and food science to address industry challenges.

Research Interests

Qing Liang’s research interests lie at the intersection of mechanical engineering, food safety, and machine learning. He focuses on applying non-destructive testing methods, such as dielectric spectroscopy, to enhance food quality control and safety, particularly in dairy products. His work explores innovative approaches to detecting adulteration, protein content, and other key factors influencing food safety. Liang is also passionate about integrating machine learning techniques with traditional testing methods to improve accuracy and efficiency in food quality assessment. By combining engineering principles with food science, his research aims to develop sustainable solutions that address current challenges in food safety and quality control.

Awards and Honors

Although Qing Liang is still early in his career, his research has already earned recognition in academic circles. He has authored multiple research papers in high-impact journals, such as the Journal of Food Science and Foods, focusing on innovative methods for food safety and quality control. Liang’s work has garnered citations, reflecting its growing influence in the field. His contributions to advancing non-destructive testing and machine learning applications in food science showcase his potential for future awards and honors. As a promising researcher, Liang’s continuous commitment to innovation positions him for further accolades in his field.

Conclusion

While LiangQing has demonstrated promise as an emerging researcher with a focus on innovative topics, they may currently lack the breadth and depth of achievements typically expected for the Best Researcher Award. Strengthening their profile with completed high-impact research, industry collaborations, and leadership roles in the scientific community would make them a stronger contender for future awards.

Publications Top Noted

  • Non-destructive detection of water adulteration level in fresh milk based on combination of dielectric spectrum technology and machine learning method
    • Authors: Liang, Q., Liu, Y., Zhang, H., Xia, Y., Li, S.
    • Journal: Journal of Food Composition and Analysis
    • Year: 2024
    • Volume: 136
    • Article Number: 106807
    • Citations: 0
  • The Study on Nondestructive Detection Methods for Internal Quality of Korla Fragrant Pears Based on Near-Infrared Spectroscopy and Machine Learning
    • Authors: Che, J., Liang, Q., Xia, Y., Zhang, H., Lan, H.
    • Journal: Foods
    • Year: 2024
    • Volume: 13(21)
    • Article Number: 3522
    • Citations: 3
  • Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk
    • Authors: Liang, Q., Liu, Y., Zhang, H., Che, J., Guo, J.
    • Journal: Journal of Food Science
    • Year: 2024
    • Volume: 89(11)
    • Pages: 7791–7802
    • Citations: 0
  • Non-Destructive Testing of the Internal Quality of Korla Fragrant Pears Based on Dielectric Properties
    • Authors: Tang, Y., Zhang, H., Liang, Q., Che, J., Liu, Y.
    • Journal: Horticulturae
    • Year: 2024
    • Volume: 10(6)
    • Article Number: 572
    • Citations: 1