Juan García‐Olmo

1.3k total citations
36 papers, 1.0k citations indexed

About

Juan García‐Olmo is a scholar working on Analytical Chemistry, Animal Science and Zoology and Plant Science. According to data from OpenAlex, Juan García‐Olmo has authored 36 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Analytical Chemistry, 11 papers in Animal Science and Zoology and 9 papers in Plant Science. Recurrent topics in Juan García‐Olmo's work include Spectroscopy and Chemometric Analyses (20 papers), Meat and Animal Product Quality (11 papers) and Advanced Chemical Sensor Technologies (6 papers). Juan García‐Olmo is often cited by papers focused on Spectroscopy and Chemometric Analyses (20 papers), Meat and Animal Product Quality (11 papers) and Advanced Chemical Sensor Technologies (6 papers). Juan García‐Olmo collaborates with scholars based in Spain, Indonesia and France. Juan García‐Olmo's co-authors include M. D. Luque de Castro, Miguel Ángel Gómez‐Nieto, P. M. Pérez-Juan, Manuel Urbano‐Cuadrado, Emiliano J. de Pedro Sanz, Ana Garrido‐Varo, M.C. Gutiérrez, José Luis García Calvo, C. Alonso and M.A. Martı́n and has published in prestigious journals such as PLoS ONE, The Science of The Total Environment and Journal of Cleaner Production.

In The Last Decade

Juan García‐Olmo

35 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juan García‐Olmo Spain 20 432 260 233 226 169 36 1.0k
Jianping Tian China 16 482 1.1× 242 0.9× 122 0.5× 90 0.4× 98 0.6× 51 750
G.S.V. Raghavan Canada 18 262 0.6× 131 0.5× 294 1.3× 52 0.2× 264 1.6× 47 978
Huihui Wang China 13 206 0.5× 101 0.4× 152 0.7× 237 1.0× 63 0.4× 33 779
Czesław Puchalski Poland 22 123 0.3× 253 1.0× 450 1.9× 71 0.3× 462 2.7× 135 1.7k
Chao‐Hui Feng Japan 21 345 0.8× 321 1.2× 327 1.4× 417 1.8× 198 1.2× 55 1.1k
E. Fulladosa Spain 23 259 0.6× 269 1.0× 221 0.9× 832 3.7× 48 0.3× 67 1.5k
Md. Hafizur Rahman Bangladesh 17 169 0.4× 180 0.7× 277 1.2× 44 0.2× 205 1.2× 70 1.1k
Piotr Zapotoczny Poland 18 291 0.7× 89 0.3× 310 1.3× 82 0.4× 408 2.4× 45 860
Marcus Nagle Germany 29 501 1.2× 185 0.7× 937 4.0× 93 0.4× 1.2k 7.3× 65 2.2k
P. Rajkumar India 12 259 0.6× 113 0.4× 386 1.7× 37 0.2× 241 1.4× 49 810

Countries citing papers authored by Juan García‐Olmo

Since Specialization
Citations

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

Fields of papers citing papers by Juan García‐Olmo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan García‐Olmo. 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 Juan García‐Olmo. The network helps show where Juan García‐Olmo may publish in the future.

Co-authorship network of co-authors of Juan García‐Olmo

This figure shows the co-authorship network connecting the top 25 collaborators of Juan García‐Olmo. A scholar is included among the top collaborators of Juan García‐Olmo 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 Juan García‐Olmo. Juan García‐Olmo 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
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Navarro‐Cerrillo, Rafael M., et al.. (2016). Discriminant analysis of Mediterranean pine nuts (Pinus pinea L.) from Chilean plantations by near infrared spectroscopy (NIRS). Food Control. 73. 634–643. 27 indexed citations
4.
Sánchez‐González, Mariola, et al.. (2016). Correlation between porosity of cork planks before and after boiling using near infrared spectroscopy. European Journal of Wood and Wood Products. 74(4). 509–517. 7 indexed citations
5.
Gutiérrez, M.C., et al.. (2015). Dynamic olfactometry and GC–TOFMS to monitor the efficiency of an industrial biofilter. The Science of The Total Environment. 512-513. 572–581. 31 indexed citations
6.
García‐Olmo, Juan, et al.. (2015). Estimation of moisture curves in cork granulate by Vis/NIRS technology. Wood Science and Technology. 49(5). 1003–1020. 4 indexed citations
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Dorado, M.P., S. Pinzi, Antonio de Haro Bailón, Rafael Font, & Juan García‐Olmo. (2011). Visible and NIR Spectroscopy to assess biodiesel quality: Determination of alcohol and glycerol traces. Fuel. 90(6). 2321–2325. 42 indexed citations
10.
Garrido‐Varo, Ana, et al.. (2011). Espectroscopía de infrarrojo cercano (NIRS) en el análisis cuantitativo y cualitativo de productos derivados del cerdo ibérico. Universidad de Córdoba Insitutional Repository (Universidad de Córdoba).
11.
López‐Bellido, Luis, et al.. (2011). Soil Carbon Determination in a Mediterranean Vertisol by Visible and near Infrared Reflectance Spectroscopy. Journal of Near Infrared Spectroscopy. 19(4). 253–263. 5 indexed citations
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García‐Olmo, Juan, et al.. (2010). Methodology for cork plank characterization (Quercus suber L.) by near-infrared spectroscopy and image analysis. Measurement Science and Technology. 21(6). 65602–65602. 20 indexed citations
13.
García‐Olmo, Juan, Ana Garrido‐Varo, & Emiliano J. de Pedro Sanz. (2009). Classification of real farm conditions Iberian pigs according to the feeding regime with multivariate models developed by using fatty acids composition or NIR spectral data. Grasas y Aceites. 60(3). 233–237. 15 indexed citations
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Calvo, José Luis García, et al.. (2008). Microstructural Evolution of Calcium Aluminate Cements Hydration with Silica Fume and Fly Ash Additions by Scanning Electron Microscopy, and Mid and Near‐Infrared Spectroscopy. Journal of the American Ceramic Society. 91(4). 1258–1265. 97 indexed citations
15.
Núñez-Sánchez, Nieves, et al.. (2005). The use of near infrared spectroscopy for quality control of Iberian pig carcasses in the slaughterhouse.. 759–761. 1 indexed citations
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García‐Olmo, Juan, et al.. (2005). Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe. Meat Science. 70(2). 357–363. 49 indexed citations
17.
Priego‐Capote, Feliciano, José Ruiz‐Jiménez, Juan García‐Olmo, & M. D. Luque de Castro. (2004). Fast method for the determination of total fat and trans fatty-acids content in bakery products based on microwave-assisted Soxhlet extraction and medium infrared spectroscopy detection. Analytica Chimica Acta. 517(1-2). 13–20. 34 indexed citations
18.
Urbano‐Cuadrado, Manuel, M. D. Luque de Castro, P. M. Pérez-Juan, Juan García‐Olmo, & Miguel Ángel Gómez‐Nieto. (2004). Near infrared reflectance spectroscopy and multivariate analysis in enologyDetermination or screening of fifteen parameters in different types of wines. Analytica Chimica Acta. 527(1). 81–88. 96 indexed citations
19.
García‐Olmo, Juan, Ana Garrido‐Varo, & Emiliano J. de Pedro Sanz. (2001). The Transfer of Fatty Acid Calibration Equations Using Four Sets of Unsealed Liquid Standardisation Samples. Journal of Near Infrared Spectroscopy. 9(1). 49–62. 27 indexed citations
20.
García‐Olmo, Juan, Emiliano J. de Pedro Sanz, & Ana Garrido‐Varo. (1998). Methodological Aspects on near Infrared Analysis of Iberian Pig Fat Using Interactance-Reflectance Fiber Optic Mode. Journal of Near Infrared Spectroscopy. 6(A). A307–A312. 11 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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