E. Fulladosa

1.9k total citations
67 papers, 1.5k citations indexed

About

E. Fulladosa is a scholar working on Animal Science and Zoology, Insect Science and Analytical Chemistry. According to data from OpenAlex, E. Fulladosa has authored 67 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Animal Science and Zoology, 17 papers in Insect Science and 15 papers in Analytical Chemistry. Recurrent topics in E. Fulladosa's work include Meat and Animal Product Quality (49 papers), Bee Products Chemical Analysis (15 papers) and Spectroscopy and Chemometric Analyses (14 papers). E. Fulladosa is often cited by papers focused on Meat and Animal Product Quality (49 papers), Bee Products Chemical Analysis (15 papers) and Spectroscopy and Chemometric Analyses (14 papers). E. Fulladosa collaborates with scholars based in Spain, France and Norway. E. Fulladosa's co-authors include P. Gou, Isabel Villaescusa, J. Arnau, J.C. Murat, Israel Muñoz, José M. Lorenzo, Jean‐Claude Murat, C. Pérez-Santaescolástica, Javier Carballo and J. Benedito and has published in prestigious journals such as The Science of The Total Environment, Journal of Agricultural and Food Chemistry and Food Chemistry.

In The Last Decade

E. Fulladosa

62 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
E. Fulladosa Spain 23 832 280 269 259 249 67 1.5k
Emiko Okazaki Japan 26 1.4k 1.7× 665 2.4× 318 1.2× 325 1.3× 180 0.7× 120 2.2k
Jörg Oehlenschläger Germany 17 1.0k 1.2× 441 1.6× 400 1.5× 227 0.9× 110 0.4× 85 2.0k
Nuray Erkan Türkiye 26 1.3k 1.6× 702 2.5× 233 0.9× 61 0.2× 193 0.8× 102 2.1k
Zdzisław E. Sikorski Poland 18 734 0.9× 478 1.7× 70 0.3× 45 0.2× 112 0.4× 44 1.4k
Mustafa Durmuş Türkiye 24 604 0.7× 628 2.2× 131 0.5× 37 0.1× 201 0.8× 86 2.0k
Zhaoming Wang China 22 1.1k 1.3× 478 1.7× 171 0.6× 37 0.1× 189 0.8× 60 1.7k
I. González-Martı́n Spain 26 568 0.7× 264 0.9× 291 1.1× 650 2.5× 245 1.0× 69 1.4k
Sadettin Turhan Türkiye 24 812 1.0× 245 0.9× 109 0.4× 25 0.1× 132 0.5× 61 1.6k
De‐Wei Chen China 18 598 0.7× 385 1.4× 278 1.0× 40 0.2× 57 0.2× 59 1.5k
Matthias Schreiner Austria 20 184 0.2× 224 0.8× 336 1.2× 70 0.3× 165 0.7× 59 1.5k

Countries citing papers authored by E. Fulladosa

Since Specialization
Citations

This map shows the geographic impact of E. Fulladosa's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by E. Fulladosa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. Fulladosa more than expected).

Fields of papers citing papers by E. Fulladosa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. Fulladosa. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by E. Fulladosa. The network helps show where E. Fulladosa may publish in the future.

Co-authorship network of co-authors of E. Fulladosa

This figure shows the co-authorship network connecting the top 25 collaborators of E. Fulladosa. A scholar is included among the top collaborators of E. Fulladosa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with E. Fulladosa. E. Fulladosa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Cruz, Jurandir Ferreira da, et al.. (2025). Compositional analysis of alternative protein blends using near and mid-infrared spectroscopy coupled with conventional and machine learning algorithms. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 337. 126114–126114. 3 indexed citations
3.
Guerrero, Luís, et al.. (2025). Impact of implicit biases on consumer behavioural intention towards meat alternative proteins. Food Research International. 221(Pt 2). 117372–117372.
4.
5.
Cruz, Jordi, et al.. (2024). Differentiation between fresh and frozen-thawed mackerel fish using low-cost portable near infrared spectrometry devices. Journal of Food Engineering. 379. 112131–112131. 9 indexed citations
6.
Cernadas, Eva, Manuel Fernández-Delgado, Manisha Sirsat, E. Fulladosa, & Israel Muñoz. (2024). MarblingPredictor: A software to analyze the quality of dry-cured ham slices. Meat Science. 221. 109713–109713.
7.
Claret, Anna, et al.. (2022). Use and Understanding of Nutrition Labels: Impact of Diet Attachment. Foods. 11(13). 1918–1918. 14 indexed citations
8.
9.
Fulladosa, E., Luís Guerrero, Antonio Olmos, et al.. (2020). Instrumental texture analysis on the surface of dry-cured ham to define the end of the process. Meat Science. 172. 108334–108334. 8 indexed citations
10.
Bou, Ricard, et al.. (2018). Zinc-protoporphyrin content in commercial Parma hams is affected by proteolysis index and marbling. Meat Science. 139. 192–200. 19 indexed citations
11.
Pérez-Santaescolástica, C., et al.. (2018). Application of temperature and ultrasound as corrective measures to decrease the adhesiveness in dry-cured ham. Influence on free amino acid and volatile compound profile. Food Research International. 114. 140–150. 41 indexed citations
12.
López‐Pedrouso, María, C. Pérez-Santaescolástica, Daniel Franco, et al.. (2017). Comparative proteomic profiling of myofibrillar proteins in dry-cured ham with different proteolysis indices and adhesiveness. Food Chemistry. 244. 238–245. 61 indexed citations
13.
Fulladosa, E., et al.. (2015). Non-destructive determination of fat content in green hams using ultrasound and X-rays. Meat Science. 104. 37–43. 26 indexed citations
14.
García-Gil, N., et al.. (2011). Salting, drying and sensory quality of dry-cured hams subjected to different pre-salting treatments: Skin trimming and pressing. Meat Science. 90(2). 386–392. 30 indexed citations
15.
Fulladosa, E., Margarita Garriga, Belén Martín, et al.. (2010). Volatile profile and microbiological characterization of hollow defect in dry-cured ham. Meat Science. 86(3). 801–807. 37 indexed citations
16.
Fulladosa, E., X. Serra, P. Gou, & J. Arnau. (2009). Effects of potassium lactate and high pressure on transglutaminase restructured dry-cured hams with reduced salt content. Meat Science. 82(2). 213–218. 93 indexed citations
17.
Fulladosa, E., Jean‐Claude Murat, Jean‐Claude Bollinger, & Isabel Villaescusa. (2007). Adverse effects of organic arsenical compounds towards Vibrio fischeri bacteria. The Science of The Total Environment. 377(2-3). 207–213. 24 indexed citations
18.
Fulladosa, E., Valérie Desjardin, Jean‐Claude Murat, Rémy Gourdon, & Isabel Villaescusa. (2006). Cr(VI) reduction into Cr(III) as a mechanism to explain the low sensitivity of Vibrio fischeri bioassay to detect chromium pollution. Chemosphere. 65(4). 644–650. 40 indexed citations
19.
Fulladosa, E., J.C. Murat, María Martínez Martínez, & Isabel Villaescusa. (2004). Effect of pH on Arsenate and Arsenite Toxicity to Luminescent Bacteria (Vibrio fischeri). Archives of Environmental Contamination and Toxicology. 46(2). 176–182. 59 indexed citations
20.
Fulladosa, E., et al.. (2002). Cellular Stress Induced in Cultured Human Cells by Exposure to Sludge Extracts from Water Treatment Plants. Ecotoxicology and Environmental Safety. 53(1). 134–140. 8 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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