D. Lorente

1.5k total citations · 1 hit paper
15 papers, 1.2k citations indexed

About

D. Lorente is a scholar working on Analytical Chemistry, Plant Science and Biomedical Engineering. According to data from OpenAlex, D. Lorente has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Analytical Chemistry, 7 papers in Plant Science and 6 papers in Biomedical Engineering. Recurrent topics in D. Lorente's work include Spectroscopy and Chemometric Analyses (10 papers), Advanced Chemical Sensor Technologies (4 papers) and Smart Agriculture and AI (4 papers). D. Lorente is often cited by papers focused on Spectroscopy and Chemometric Analyses (10 papers), Advanced Chemical Sensor Technologies (4 papers) and Smart Agriculture and AI (4 papers). D. Lorente collaborates with scholars based in Spain, Germany and Mexico. D. Lorente's co-authors include J. Blasco, Juan Gómez‐Sanchís, Sergio Cubero, Nuria Aleixos, Oscar Leonardo García-Navarrete, Pablo Escandell-Montero, Emilio Soria‐Olivas, Antonio J. Serrano-López, Manuela Zude-Sasse and Marcelino Martínez‐Sober and has published in prestigious journals such as Expert Systems with Applications, Journal of Food Engineering and Knowledge-Based Systems.

In The Last Decade

D. Lorente

15 papers receiving 1.1k citations

Hit Papers

Recent Advances and Applications of Hyperspectral Imaging... 2011 2026 2016 2021 2011 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment
    2011 413 citations D. Lorente, Nuria Aleixos et al. Food and Bioprocess Technology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
D. Lorente Spain 13 845 400 362 166 153 15 1.2k
Xiuqin Rao China 14 712 0.8× 326 0.8× 293 0.8× 165 1.0× 100 0.7× 48 903
Kunshan Yao China 24 888 1.1× 280 0.7× 422 1.2× 288 1.7× 133 0.9× 49 1.4k
J.M. Prats-Montalbán Spain 19 531 0.6× 281 0.7× 183 0.5× 136 0.8× 116 0.8× 55 1.1k
Laijun Sun China 16 527 0.6× 233 0.6× 172 0.5× 158 1.0× 90 0.6× 68 885
Guantao Xuan China 17 494 0.6× 343 0.9× 170 0.5× 85 0.5× 98 0.6× 36 765
Sajad Sabzi Iran 22 625 0.7× 745 1.9× 174 0.5× 48 0.3× 120 0.8× 61 1.1k
Nico Scheerlinck Belgium 25 408 0.5× 702 1.8× 178 0.5× 100 0.6× 460 3.0× 79 1.6k
Yan Tian China 15 474 0.6× 171 0.4× 220 0.6× 124 0.7× 56 0.4× 16 716
Tom Pearson United States 22 700 0.8× 697 1.7× 194 0.5× 191 1.2× 172 1.1× 64 1.5k

Countries citing papers authored by D. Lorente

Since Specialization
Citations

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

Fields of papers citing papers by D. Lorente

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of D. Lorente

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

All Works

15 of 15 papers shown
1.
Martínez‐Martínez, Francisco, María José Rupérez, Marcelino Martínez‐Sober, et al.. (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine. 90. 116–124. 80 indexed citations
2.
Lorente, D., Francisco Martínez‐Martínez, María José Rupérez, et al.. (2016). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications. 71. 342–357. 40 indexed citations
3.
Blasco, J., D. Lorente, Victoria Cortés, et al.. (2016). Application of near Infrared Spectroscopy to the Quality Control of Citrus Fruits and Mango. NIR news. 27(7). 4–7. 9 indexed citations
4.
Martín‐Guerrero, José D., María José Rupérez, Francisco Martínez‐Martínez, et al.. (2016). Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue. 247–253. 12 indexed citations
5.
Escandell-Montero, Pablo, D. Lorente, José M. Martínez-Martínez, et al.. (2016). Online fitted policy iteration based on extreme learning machines. Knowledge-Based Systems. 100. 200–211. 9 indexed citations
6.
Lorente, D., Manuela Zude-Sasse, C. Idler, Juan Gómez‐Sanchís, & J. Blasco. (2015). Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. Journal of Food Engineering. 154. 76–85. 62 indexed citations
7.
Lorente, D., Pablo Escandell-Montero, Sergio Cubero, Juan Gómez‐Sanchís, & J. Blasco. (2015). Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit. Journal of Food Engineering. 163. 17–24. 71 indexed citations
8.
Gómez‐Sanchís, Juan, José Jorge Chanona‐Pérez, Juan J. Carrasco, et al.. (2014). Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning. Biosystems Engineering. 122. 91–98. 130 indexed citations
9.
Lorente, D., et al.. (2013). Early decay detection in citrus fruit using laser-light backscattering imaging. Postharvest Biology and Technology. 86. 424–430. 62 indexed citations
10.
Gómez‐Sanchís, Juan, J. Blasco, Emilio Soria‐Olivas, et al.. (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology. 82. 76–86. 63 indexed citations
11.
Gómez‐Sanchís, Juan, D. Lorente, Emilio Soria‐Olivas, et al.. (2013). Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay. Food and Bioprocess Technology. 7(4). 1047–1056. 46 indexed citations
12.
Lorente, D., J. Blasco, Antonio J. Serrano-López, et al.. (2012). Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit Using Hyperspectral Images. Food and Bioprocess Technology. 6(12). 3613–3619. 56 indexed citations
13.
Lorente, D., Nuria Aleixos, Juan Gómez‐Sanchís, et al.. (2011). Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment. Food and Bioprocess Technology. 5(4). 1121–1142. 413 indexed citations breakdown →
14.
Lorente, D., Nuria Aleixos, Juan Gómez‐Sanchís, Sergio Cubero, & J. Blasco. (2011). Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Food and Bioprocess Technology. 6(2). 530–541. 114 indexed citations
15.
Ahern, F.J., et al.. (1991). Investigation of continental aerosols with high-spectral-resolution solar-extinction measurements. Applied Optics. 30(36). 5276–5276. 29 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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