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This repository contains the bibliometric search results discussed in the manuscript: García, MR, Férez-Rubio, JA, Vilas, C. (2022). Assessment and Prediction of Fish Freshness using Mathematical Modelling: A Review. MDPI Foods, 11(15):2312. DOI:10.3390/foods11152312.

Fish freshness can be considered as the combination of different nutritional and organoleptic attributes that rapidly deteriorate after fish capture, i.e., during processing (cutting, gutting, packaging), storage, transport, distribution, and retail. The rate at which this degradation occurs is affected by several stress variables such as temperature, water activity, or pH, among others. The food industry is aware that fish freshness is a key feature influencing consumers’ willingness to pay for the product. Therefore, tools that allow rapid and reliable assessment and prediction of the attributes related to freshness are gaining relevance. The main objective of this work is to provide a comprehensive review of the mathematical models used to describe and predict the changes in the key quality indicators in fresh fish and shellfish during storage. The work also briefly describes such indicators, discusses the most relevant stress factors affecting the quality of fresh fish, and presents a bibliometric analysis of the results obtained from a systematic literature search on the subject

In the era of big data, turning data into useful knowledge and insight is a critical challenging to understand AMR (antimicrobial resistance). Multivariate analysis contributes to this insight by studying how different and multiples variables (interfere among them and how they work in combination to allow afterwards, building appropriate mechanistic predictive models