M. Prevolnik

594 total citations
21 papers, 475 citations indexed

About

M. Prevolnik is a scholar working on Animal Science and Zoology, Analytical Chemistry and Genetics. According to data from OpenAlex, M. Prevolnik has authored 21 papers receiving a total of 475 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Animal Science and Zoology, 7 papers in Analytical Chemistry and 4 papers in Genetics. Recurrent topics in M. Prevolnik's work include Meat and Animal Product Quality (18 papers), Animal Nutrition and Physiology (8 papers) and Spectroscopy and Chemometric Analyses (7 papers). M. Prevolnik is often cited by papers focused on Meat and Animal Product Quality (18 papers), Animal Nutrition and Physiology (8 papers) and Spectroscopy and Chemometric Analyses (7 papers). M. Prevolnik collaborates with scholars based in Slovenia, Italy and France. M. Prevolnik's co-authors include Dejan Škorjanc, Marjeta Čandek‐Potokar, Martin Škrlep, Š. Velikonja‐Bolta, Marjana Novič, Nina Batorek‐Lukač, M. Bonneau, Valentina Kubale, B. Žlender and G. Fazarinc and has published in prestigious journals such as Journal of Food Engineering, Meat Science and Applied Animal Behaviour Science.

In The Last Decade

M. Prevolnik

20 papers receiving 457 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Prevolnik Slovenia 12 381 257 128 56 53 21 475
D. Alomar Chile 8 212 0.6× 220 0.9× 90 0.7× 82 1.5× 33 0.6× 16 347
B.N. Nilsen Norway 9 410 1.1× 349 1.4× 168 1.3× 49 0.9× 12 0.2× 9 501
Stephanie M. Fowler Australia 17 412 1.1× 281 1.1× 149 1.2× 154 2.8× 20 0.4× 44 597
R.W. Kranen Netherlands 10 384 1.0× 105 0.4× 79 0.6× 38 0.7× 120 2.3× 11 447
Eli Gjerlaug‐Enger Norway 11 223 0.6× 66 0.3× 36 0.3× 36 0.6× 49 0.9× 15 320
Alessandro Ferragina Italy 12 243 0.6× 178 0.7× 73 0.6× 66 1.2× 12 0.2× 28 444
Alin Khaliduzzaman Japan 12 128 0.3× 124 0.5× 54 0.4× 57 1.0× 23 0.4× 30 280
Tomislav Mikuš Croatia 7 90 0.2× 56 0.2× 74 0.6× 147 2.6× 45 0.8× 26 297
M. Høy Norway 8 200 0.5× 112 0.4× 74 0.6× 76 1.4× 2 0.0× 10 306
Didier Veselko Belgium 5 198 0.5× 105 0.4× 30 0.2× 33 0.6× 14 0.3× 8 382

Countries citing papers authored by M. Prevolnik

Since Specialization
Citations

This map shows the geographic impact of M. Prevolnik'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 M. Prevolnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Prevolnik more than expected).

Fields of papers citing papers by M. Prevolnik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Prevolnik. 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 M. Prevolnik. The network helps show where M. Prevolnik may publish in the future.

Co-authorship network of co-authors of M. Prevolnik

This figure shows the co-authorship network connecting the top 25 collaborators of M. Prevolnik. A scholar is included among the top collaborators of M. Prevolnik 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 M. Prevolnik. M. Prevolnik 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.
Čandek‐Potokar, Marjeta, M. Prevolnik, Martin Škrlep, Maria Font‐i‐Furnols, & Marjana Novič. (2015). An Attempt to Predict Conformation and Fatness in Bulls by Means of Artificial Neural Networks Using Weight, Age and Breed Composition Information. Italian Journal of Animal Science. 14(1). 3198–3198. 3 indexed citations
2.
Prevolnik, M., et al.. (2014). Behavioural patterns established during suckling reappear when piglets are forced to form a new dominance hierarchy. Applied Animal Behaviour Science. 161. 42–50. 12 indexed citations
3.
Prevolnik, M., et al.. (2013). Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks. Meat Science. 96(1). 14–20. 27 indexed citations
4.
Škrlep, Martin, Marjeta Čandek‐Potokar, Nina Batorek‐Lukač, et al.. (2012). Length of the interval between immunocastration and slaughter in relation to boar taint and carcass traits. Florence Research (University of Florence). 247–251. 5 indexed citations
5.
6.
Batorek‐Lukač, Nina, et al.. (2012). Effect of immunocastration in group-housed commercial fattening pigs on reproductive organs, malodorous compounds, carcass and meat quality. Czech Journal of Animal Science. 57(6). 290–299. 27 indexed citations
7.
Prevolnik, M., et al.. (2011). gROWTH, CARCASS ANd mEAT QuALITY TRAITS OF pIgS RAISEd uNdER ORgANIC OR CONVENTIONAL REARINg SYSTEmS uSINg COmmERCIALLY AVAILABLE FEEd mIXTuRES. Slovenian Veterinary Research. 48(1). 15–26. 2 indexed citations
8.
Prevolnik, M., et al.. (2011). Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham. Meat Science. 88(2). 299–304. 60 indexed citations
9.
Prevolnik, M., et al.. (2011). Effect of linseed supplementation on carcass, meat quality and fatty acid composition in pigs.. University of Zagreb University Computing Centre (SRCE). 76(3). 183–186. 4 indexed citations
10.
Prevolnik, M., et al.. (2011). Differences in Carcass and Meat Quality between Organically Reared Cocks and Capons. University of Zagreb University Computing Centre (SRCE). 76(3). 153–156. 15 indexed citations
11.
Prevolnik, M., et al.. (2011). The effect of the abattoir on beef carcass classification results.. University of Zagreb University Computing Centre (SRCE). 76(3). 169–173. 1 indexed citations
12.
Škrlep, Martin, et al.. (2010). Effect of immunocastration (Improvac®) in fattening pigs II: Carcass traits and meat quality.. Slovenian Veterinary Research. 47(2). 65–72. 21 indexed citations
13.
Prevolnik, M., Martin Škrlep, Dejan Škorjanc, & Marjeta Čandek‐Potokar. (2010). Application of near infrared spectroscopy to predict chemical composition of meat and meat products. 51(2). 133–142. 11 indexed citations
14.
Prevolnik, M., Marjeta Čandek‐Potokar, & Dejan Škorjanc. (2010). Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods. Journal of Food Engineering. 98(3). 347–352. 53 indexed citations
15.
Škrlep, Martin, et al.. (2009). Association of plasma stress markers at slaughter with carcass or meat quality in pigs.. Slovenian Veterinary Research. 46(4). 133–142. 9 indexed citations
16.
Prevolnik, M., Marjeta Čandek‐Potokar, Marjana Novič, & Dejan Škorjanc. (2009). An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks. Meat Science. 83(3). 405–411. 35 indexed citations
17.
Čandek‐Potokar, Marjeta, et al.. (2006). The uncertainty of results when estimating daily milk records. Animal Research. 55(6). 521–532. 6 indexed citations
18.
Prevolnik, M., et al.. (2006). Ability of near Infrared Spectroscopy to Predict Pork Technological Traits. Journal of Near Infrared Spectroscopy. 14(4). 269–277. 23 indexed citations
19.
Prevolnik, M., et al.. (2005). Predicting Intramuscular Fat Content in Pork and Beef by near Infrared Spectroscopy. Journal of Near Infrared Spectroscopy. 13(2). 77–85. 56 indexed citations
20.
Prevolnik, M., et al.. (2004). Ability of NIR spectroscopy to predict meat chemical composition and quality - a review. Czech Journal of Animal Science. 49(11). 500–510. 103 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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