Bibliometric study and potential applications in smartphone-based digital images: A perspective from 2013 to 2024

This paper presents the first bibliometric review on the application of smartphone-based digital images in food matrices, conducted specifically using the Bibliometrix package in R. Although there are already reviews on the topic, this is the first to employ a quantitative approach based on bibliometric methods, analyzing 125 articles published between 2013 and 2024 and identified in the Web of Science. The retrieved works were written by 431 researchers and published in 38 journals across 33 countries, demonstrating an annual growth rate of 45.92 %. The results reveal that a substantial portion of the articles focus on adulteration detection and substance quantification in a practical, cost-effective, and accessible manner. More than 60 % of the studies did not use chemometric tools. Among the studies that did, the most frequently employed were Partial Least Squares Regression (PLS), Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Multiple Linear Regression (MLR).