Comprehensive Bibliometric Analysis on High Hydrostatic Pressure as New Sustainable Technology for Food Processing: Key Concepts and Research Trends
Bibliographic record
Abstract
The industrial application of high hydrostatic pressure (HHP) can be traced back to the late 19th century in the fields of mechanical and chemical engineering. Its growth as a food preservation technique has developed and massified in certain countries in the last 30 years. However, there is no global overview of the research conducted on this topic. The aim of this study was to recognize global trends in the scientific population on the subject of HHP over time at the main levels of analysis: sources, authors, and publications. This article provides a summary of research related to the use of HHP through a bibliometric analysis using information obtained from the Web of Science (WoS) database between the years 1975–2023, using the terms “pascalization”,“high-pressure processing”, and “high hydrostatic pressure” as input keywords. The results are shown in tables, graphs, and relationship diagrams. The countries most influential and productive in high hydrostatic pressure are the People’s R China, the USA, and Spain, with 1578, 1340, and 1003 articles, respectively. Conversely, the authors with the highest metrics are Saraiva, J. (Universidade Aveiro-Portugal), Hendrickx, M. (Katholieke Universiteit Leuven-Belgium), and Wang, T. (China Agricultural University-China). The most productive journals are Innovative Food Science & Emerging Technologies, Food Chemistry, and LWT-Food Science and Technology, all belonging to Elsevier, with 457, 281, and 264 documents, respectively. In relation to the connection between the documents under study and the United Nations Sustainable Development Goals (SDGs), most documents in the period 1975–2023 are linked to SDG 03 (good health and well-being), followed by SDG 02 (zero hunger), and SDG 07 (affordable and clean energy). Finally, the information presented in this work may give valuable key insights for those interested in the development of this interesting topic in non-thermal food preservation. Additionally, it serves as a strategic resource for stakeholders, such as food industry leaders, policymakers, and research funding bodies, by providing a clear understanding of the current state of knowledge and innovation trends. This enables informed decision-making regarding research priorities, investment opportunities, and the development of regulatory frameworks to support the adoption and advancement of non-thermal preservation technologies, ultimately contributing to safer and more sustainable food systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.161 | 0.273 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".