Maya Raman
Impact in
- Food Science top 5%
- Probiotics and Fermented Foods
- Nutrition and Dietetics top 5%
- Microbial Metabolites in Food Biotechnology
- Food composition and properties
Papers in
-
- Food composition and properties 10
- Microbial Metabolites in Food Biotechnology 8
- Food Science 19
- Probiotics and Fermented Foods 7
- Co-authors
- Mukesh Doble (13 shared papers)Padma S. Ambalam (12 shared papers)Ravi Kiran Purama (3 shared papers)B. R. M. Vyas (2 shared papers)Charmy Kothari (3 shared papers)Jayantilal M. Dave (1 shared paper)Sheetal P. Pithva (2 shared papers)Kanthi Kiran Kondepudi (1 shared paper)
- Journals
- Starch - Stärke (2 papers)Journal of Applied Phycology (2 papers)Gut Microbes (2 papers)LWT (1 paper)Journal of Nanobiotechnology (1 paper)
- Partner nations
- IndiaUnited StatesSweden
In The Last Decade
Maya Raman
52 papers receiving 958 citations
Peers
Comparison fields: 5 of 119
- Food Science 321
- Nutrition and Dietetics 233
- Aquatic Science 113
- Biotechnology 76
- Biomaterials 87
Countries citing papers authored by Maya Raman
This map shows the geographic impact of Maya Raman'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 Maya Raman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Raman more than expected).
Fields of papers citing papers by Maya Raman
This network shows the impact of papers produced by Maya Raman. 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 Maya Raman. The network helps show where Maya Raman may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Raman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 214 | |
| 2 | 2016 | 200 | |
| 3 | 2015 | 73 | |
| 4 | 2017 | 60 | |
| 5 | 2015 | 45 | |
| 6 | 2016 | 40 | |
| 7 | 2015 | 34 | |
| 8 | 2023 | 29 | |
| 9 | 2019 | 24 | |
| 10 | 2011 | 21 | |
| 11 | 2014 | 20 | |
| 12 | 2019 | 14 | |
| 13 | 2012 | 14 | |
| 14 | Correlation between BOD, COD and TOC | 2003 | 12 |
| 15 | 2023 | 12 | |
| 16 | 2012 | 11 | |
| 17 | 2014 | 11 | |
| 18 | 2014 | 10 | |
| 19 | 2020 | 10 | |
| 20 | 2015 | 10 |
About Maya Raman
Maya Raman is a scholar working on Nutrition and Dietetics, Food Science, Aquatic Science, Biomaterials and Animal Science and Zoology, having authored 58 papers that have together received 982 indexed citations. Recurring topics across this work include Food composition and properties (10 papers), Microbial Metabolites in Food Biotechnology (8 papers), Gut microbiota and health (7 papers), Probiotics and Fermented Foods (7 papers), Meat and Animal Product Quality (6 papers), Seaweed-derived Bioactive Compounds (5 papers), GABA and Rice Research (4 papers) and Nanocomposite Films for Food Packaging (4 papers). The work is most often cited by research in Food Science (321 citations), Nutrition and Dietetics (233 citations), Aquatic Science (113 citations), Biotechnology (76 citations) and Biomaterials (87 citations). Maya Raman has collaborated with scholars based in India, United States and Sweden. Frequent co-authors include Mukesh Doble, Padma S. Ambalam, Ravi Kiran Purama, B. R. M. Vyas, Charmy Kothari, Jayantilal M. Dave, Sheetal P. Pithva, Kanthi Kiran Kondepudi, Mukesh Doble and Wenxia Wang. Their work appears in journals such as Starch - Stärke, Journal of Applied Phycology, Gut Microbes, LWT and Journal of Nanobiotechnology.
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.