Deepa Agarwal

424 citations
27 papers · 324 · h-index 13

Impact in

Papers in

    • Polysaccharides Composition and Applications 4
    • Proteins in Food Systems 3
    • Sensory Analysis and Statistical Methods 3
    • Food composition and properties 5

Deepa Agarwal

21 papers receiving 316 citations

Peers

Deepa Agarwal
Comparison fields: 5 of 89
  • Automotive Engineering 67
  • Biomaterials 55
  • Food Science 73
  • Health, Toxicology and Mutagenesis 37
  • Nutrition and Dietetics 39
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Lishan Yao China
Parisa Mostashari Iran
Jaspin Stephen India
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M. Dharini India
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Citations per field
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Citations per year

Countries citing papers authored by Deepa Agarwal

Since Specialization
Citations

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

Fields of papers citing papers by Deepa Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Deepa Agarwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Deepa Agarwal Line = papers co-authored together Deepa Agarwal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199450
2 201231
3 202029
4 201828
5 202127
6 202225
7 202219
8 201717
9 201817
10 201714
11 202114
12 199313
13 201913
14 20247
15 20226
16 20225
17 19873
18 20212
19 19922
20 20251

About Deepa Agarwal

Deepa Agarwal is a scholar working on Food Science, Nutrition and Dietetics, Animal Science and Zoology, Plant Science and Biomedical Engineering, having authored 27 papers that have together received 324 indexed citations. Recurring topics across this work include Food composition and properties (5 papers), Polysaccharides Composition and Applications (4 papers), Meat and Animal Product Quality (4 papers), Proteins in Food Systems (3 papers), Sensory Analysis and Statistical Methods (3 papers), Advanced Cellulose Research Studies (3 papers), Pesticide Exposure and Toxicity (2 papers) and 3D Printing in Biomedical Research (2 papers). The work is most often cited by research in Automotive Engineering (67 citations), Biomaterials (55 citations), Food Science (73 citations), Health, Toxicology and Mutagenesis (37 citations) and Nutrition and Dietetics (39 citations). Deepa Agarwal has collaborated with scholars based in New Zealand, United Kingdom and Australia. Frequent co-authors include Tim Foster, Benjamin S. Schon, J. J. Nijdam, Jai Krishna Pandey, William MacNaughtan, V. Sundararaman, Lalit Kumar Singh Chauhan, Savita Gupta, Louise Hewson and Esther H.‐J. Kim. Their work appears in journals such as Food & Function, Journal of Food Engineering, Carbohydrate Polymers, Food Research International and International Journal of Biological Macromolecules.

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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