C. Borggaard

955 total citations
18 papers, 759 citations indexed

About

C. Borggaard is a scholar working on Animal Science and Zoology, Analytical Chemistry and Food Science. According to data from OpenAlex, C. Borggaard has authored 18 papers receiving a total of 759 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Animal Science and Zoology, 9 papers in Analytical Chemistry and 5 papers in Food Science. Recurrent topics in C. Borggaard's work include Meat and Animal Product Quality (15 papers), Spectroscopy and Chemometric Analyses (9 papers) and Animal Nutrition and Physiology (3 papers). C. Borggaard is often cited by papers focused on Meat and Animal Product Quality (15 papers), Spectroscopy and Chemometric Analyses (9 papers) and Animal Nutrition and Physiology (3 papers). C. Borggaard collaborates with scholars based in Denmark, United States and Australia. C. Borggaard's co-authors include Hans Henrik Thodberg, Annemarie Gunvig, J. Renwick Beattie, B.W. Moss, Steven E. J. Bell, Ole Mejlholm, Jens Rikardt Andersen, T Ross, Flemming Hansen and Lars Plejdrup Houmøller and has published in prestigious journals such as Analytical Chemistry, International Journal of Food Microbiology and Meat Science.

In The Last Decade

C. Borggaard

17 papers receiving 720 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C. Borggaard Denmark 13 377 352 147 147 126 18 759
Martin Høy Norway 16 334 0.9× 277 0.8× 154 1.0× 154 1.0× 43 0.3× 23 687
Stefka Atanassova Bulgaria 11 389 1.0× 226 0.6× 124 0.8× 81 0.6× 34 0.3× 44 656
W. Fred McClure United States 17 694 1.8× 159 0.5× 241 1.6× 150 1.0× 35 0.3× 74 1.1k
Ernest Bonah China 19 291 0.8× 155 0.4× 351 2.4× 299 2.0× 92 0.7× 24 871
R. Giangiacomo Italy 14 403 1.1× 132 0.4× 144 1.0× 257 1.7× 51 0.4× 29 763
Adel Elsayed United Kingdom 7 563 1.5× 105 0.3× 196 1.3× 255 1.7× 75 0.6× 35 895
Qiong Dai China 11 409 1.1× 234 0.7× 273 1.9× 69 0.5× 16 0.1× 30 703
Marlon M. Reis New Zealand 24 859 2.3× 436 1.2× 531 3.6× 222 1.5× 36 0.3× 72 1.5k
Feifei Tao United States 15 653 1.7× 205 0.6× 301 2.0× 94 0.6× 77 0.6× 39 877
Huirong Xu China 22 1.0k 2.8× 125 0.4× 462 3.1× 247 1.7× 75 0.6× 81 1.5k

Countries citing papers authored by C. Borggaard

Since Specialization
Citations

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

Fields of papers citing papers by C. Borggaard

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C. Borggaard

This figure shows the co-authorship network connecting the top 25 collaborators of C. Borggaard. A scholar is included among the top collaborators of C. Borggaard 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 C. Borggaard. C. Borggaard is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Lund, Birgitte W., C. Borggaard, Rune Isak Dupont Birkler, Kirsten Jensen, & Susanne Støier. (2021). High throughput method for quantifying androstenone and skatole in adipose tissue from uncastrated male pigs by laser diode thermal desorption-tandem mass spectrometry. Food Chemistry X. 9. 100113–100113. 10 indexed citations
2.
Knight, Matthew I., Eric N. Ponnampalam, Matthew G. Kerr, et al.. (2019). Development of VISNIR predictive regression models for ultimate pH, meat tenderness (shear force) and intramuscular fat content of Australian lamb. Meat Science. 155. 102–108. 33 indexed citations
3.
Gunvig, Annemarie, et al.. (2018). Staphtox predictor - A dynamic mathematical model to predict formation of Staphylococcus enterotoxin during heating and fermentation of meat products. International Journal of Food Microbiology. 285. 81–91. 15 indexed citations
4.
Gunvig, Annemarie, C. Borggaard, Flemming Hansen, Tina Beck Hansen, & Søren Aabo. (2016). ConFerm – A tool to predict the reduction of pathogens during the production of fermented and matured sausages. Food Control. 67. 9–17. 6 indexed citations
5.
Hansen, Flemming, Annemarie Gunvig, & C. Borggaard. (2016). F-value Calculator – A Tool for Calculation of Acceptable F-value in Canned Luncheon Meat Reduced in NaCl. Procedia Food Science. 7. 117–120.
6.
Sharifzadeh, Sara, Line Katrine Harder Clemmensen, C. Borggaard, Susanne Støier, & Bjarne Kjær Ersbøll. (2013). Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat. Engineering Applications of Artificial Intelligence. 27. 211–227. 39 indexed citations
7.
Gunvig, Annemarie, Flemming Hansen, & C. Borggaard. (2012). A mathematical model for predicting growth/no-growth of psychrotrophic C. botulinum in meat products with five variables. Food Control. 29(2). 309–317. 29 indexed citations
8.
Mejlholm, Ole, Annemarie Gunvig, C. Borggaard, et al.. (2010). Predicting growth rates and growth boundary of Listeria monocytogenes — An international validation study with focus on processed and ready-to-eat meat and seafood. International Journal of Food Microbiology. 141(3). 137–150. 107 indexed citations
9.
Beattie, J. Renwick, Steven E. J. Bell, C. Borggaard, & B.W. Moss. (2008). Preliminary investigations on the effects of ageing and cooking on the Raman spectra of porcine longissimus dorsi. Meat Science. 80(4). 1205–1211. 35 indexed citations
10.
Beattie, J. Renwick, Steven E. J. Bell, C. Borggaard, Anna M Fearon, & B.W. Moss. (2007). Classification of Adipose Tissue Species using Raman Spectroscopy. Lipids. 42(7). 679–685. 42 indexed citations
11.
Forrest, J. C., et al.. (2000). Development of technology for the early post mortem prediction of water holding capacity and drip loss in fresh pork. Meat Science. 55(1). 115–122. 61 indexed citations
12.
Joo, Seon-Tea, et al.. (2000). Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature:II. Practical Classification Method on the Cutting-Line. Asian-Australasian Journal of Animal Sciences. 13(1). 77–85. 3 indexed citations
13.
Josell, Åsa, et al.. (2000). Determination of RN- phenotype in pigs at slaughter-line using visual and near-infrared spectroscopy. Meat Science. 55(3). 273–278. 35 indexed citations
14.
Joo, Seon-Tea, et al.. (2000). Objectively Predicting Ultimate Quality of Post-Rigor Pork Musculature: I. Initial Comparison of Techniques. Asian-Australasian Journal of Animal Sciences. 13(1). 68–76. 19 indexed citations
15.
Andersen, Jens Rikardt, et al.. (1999). Optical measurements of pH in meat. Meat Science. 53(2). 135–141. 44 indexed citations
16.
Borggaard, C., et al.. (1996). In-line image analysis in the slaughter industry, illustrated by Beef Carcass Classification. Meat Science. 43. 151–163. 48 indexed citations
17.
Andersen, John Roger, C. Borggaard, & Toke Rammer Nielsen. (1995). Recent advances in real time measurements of meat quality and safety. 3 indexed citations
18.
Borggaard, C. & Hans Henrik Thodberg. (1992). Optimal minimal neural interpretation of spectra. Analytical Chemistry. 64(5). 545–551. 230 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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Rankless by CCL
2026