## 2 Bibliometric Analysis
### Authors' Keywords Analysis  
The results of the authors' keywords analysis are shown in Figure 2. In the 8812 analyzed publications, 42,819 authors' keywords were found. The word cloud in Figure 2a highlights the most frequently occurring terms in the analyzed literature on plant antimicrobial compounds and their mechanisms of action. In this representation, the size of the letters corresponds to the frequency of each keyword, offering a graphical insight into the prevalence of specific terms. The most prominent words, such as \"mechanism\" (124 occurrences), \"resistance\" (98), \"antimicrobial\" (91), \"_Escherichia coli_\" (87), and \"activity\" (83), suggest that a significant portion of research focuses on understanding the specific ways in which antimicrobial compounds interact with spoilage and pathogenic bacteria. The frequent mention of \"in vitro\" (76) indicates that many studies rely on controlled experimental settings to evaluate antimicrobial efficacy. Additionally, terms like \"antibacterial\" (73), \"peptides\" (68), \"antibiotics\" (65), and \"_Staphylococcus aureus_\" (61) underscore the relevance of antimicrobial peptides and antibiotic resistance as key research themes. The presence of words such as \"oxidative stress\" (58), \"membrane\" (55), \"binding\" (52), and \"gene expression\" (49) suggests that mechanistic studies often explore molecular interactions and cellular targets. Thus, this word cloud provides a clear snapshot of research priorities over the past two decades, emphasizing both fundamental mechanisms and applied aspects of plant-derived antimicrobials. Figure 1: Overview of scientific production from the timespan 2004–2024. (**a**) Annual scientific production. (**b**) Country scientific production. (**c**) Leading reputed journals where articles were published. (**d**) Source impact. (**e**) Treemap chart of publications in different disciplines according to Web of Science categories. Figure 2: Cont. Figure 2: Scientometric mapping based on authors’ keywords analysis (2004–2024). (**a**) Word cloud containing the 50 main authors’ keywords. Scientometric mapping with co-occurrence of the 42,819 main authors’ keywords: (**b**) Network visualization, (**c**) overlay visualization, and (**d**) density visualization. Figure 2b-d show the scientometric mapping, carried out with the software VOSviewer v. 1.6.20 as follows:  
1. The option \"Create map based on bibliographic data\" was selected to extract keyword co-occurrence relationships from the dataset;
2. In the \"Choose data source\" section, the option \"Read data from reference manager files\" was selected;
3. A RIS file (Research Information Systems file) containing the bibliometric dataset was uploaded in the \"Select files\" step;
4. In the \"Choose type of analysis and counting method\" section, \"Co-occurrence\" was selected as the type of analysis, \"Keywords\" as the unit of analysis, and \"Full counting\" as the counting method. At this stage, a thesaurus file (CSV format) was included to standardize terms by grouping similar expressions. Specifically, the terms \"mechanism\", \"mechanisms\", \"mechanism of action\", \"mechanisms of action\", \"molecular-mechanisms\", and \"molecular-mechanism\" were replaced with \"mechanisms\". Similarly, \"system\" and \"systems\" were unified under \"systems\", \"enzyme\" and \"enzymes\" were grouped as \"enzymes\", and \"gene\" and \"genes\" were consolidated under \"genes\";
5. A threshold was applied by setting the \"Minimum number of documents of an author\" to five, filtering out less frequently occurring terms. Of the initial 29,361 keywords, 2794 met the threshold;
6. To refine the visualization, the \"Number of keywords to be selected\" was set to 1000, focusing on the most relevant terms;
7. The map was generated, and three visualizations--network, overlay, and density--were created to illustrate keyword relationships, temporal trends, and research intensity;
8.