David W. Jeffery

6.4k total citations · 1 hit paper
157 papers, 4.9k citations indexed

About

David W. Jeffery is a scholar working on Food Science, Plant Science and Biochemistry. According to data from OpenAlex, David W. Jeffery has authored 157 papers receiving a total of 4.9k indexed citations (citations by other indexed papers that have themselves been cited), including 124 papers in Food Science, 94 papers in Plant Science and 41 papers in Biochemistry. Recurrent topics in David W. Jeffery's work include Fermentation and Sensory Analysis (122 papers), Horticultural and Viticultural Research (87 papers) and Phytochemicals and Antioxidant Activities (41 papers). David W. Jeffery is often cited by papers focused on Fermentation and Sensory Analysis (122 papers), Horticultural and Viticultural Research (87 papers) and Phytochemicals and Antioxidant Activities (41 papers). David W. Jeffery collaborates with scholars based in Australia, United States and United Kingdom. David W. Jeffery's co-authors include Dimitra L. Capone, Susan E.P. Bastian, Gavin L. Sacks, Andrew L. Waterhouse, Richard A. Muhlack, Kevin H. Pardon, Mark A. Sefton, Joanna M. Gambetta, Yoji Hayasaka and Ravichandra Potumarthi and has published in prestigious journals such as Analytical Chemistry, PLANT PHYSIOLOGY and Journal of Agricultural and Food Chemistry.

In The Last Decade

David W. Jeffery

150 papers receiving 4.8k citations

Hit Papers

Understanding Wine Chemistry 2016 2026 2019 2022 2016 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. Understanding Wine Chemistry
    2016 452 citations Andrew L. Waterhouse, Gavin L. Sacks et al. Adelaide Research & Scholarship (AR&S) (University of Adelaide) profile →

Peers

David W. Jeffery
Teresa Garde‐Cerdán Spain
Philippe Darriet France
Markus Herderich Australia
Dimitra L. Capone Australia
Roger B. Boulton United States
Susan E. Ebeler United States
Eduardo Dellacassa Uruguay
Mark A. Sefton Australia
I. Leigh Francis Australia
M.S. Pérez-Coello Spain
Teresa Garde‐Cerdán Spain
David W. Jeffery
Citations per year, relative to David W. Jeffery David W. Jeffery (= 1×) peers Teresa Garde‐Cerdán

Countries citing papers authored by David W. Jeffery

Since Specialization
Citations

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

Fields of papers citing papers by David W. Jeffery

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David W. Jeffery. 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 David W. Jeffery. The network helps show where David W. Jeffery may publish in the future.

Co-authorship network of co-authors of David W. Jeffery

This figure shows the co-authorship network connecting the top 25 collaborators of David W. Jeffery. A scholar is included among the top collaborators of David W. Jeffery 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 David W. Jeffery. David W. Jeffery 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
1.
Culbert, Julie A., et al.. (2026). Amelioration of Smoke‐Tainted Wine Using Spinning Cone Column Distillation and Activated Carbon. Australian Journal of Grape and Wine Research. 2026(1).
2.
Sáenz‐Navajas, María‐Pilar, Chelo Ferreira, Susan E.P. Bastian, & David W. Jeffery. (2025). Bagging and boosting machine learning algorithms for modelling sensory perception from simple chemical variables: Wine mouthfeel as a case study. Food Quality and Preference. 129. 105494–105494. 1 indexed citations
3.
4.
Hou, Yu‐Chi, Marlize Z. Bekker, Tracey Siebert, & David W. Jeffery. (2025). Quantitation of Polysulfides in Wine, Beer, and Cider by HPLC-MS/MS after Solid-Phase Extraction. Journal of Agricultural and Food Chemistry. 73(40). 25591–25603.
5.
Jeffery, David W., et al.. (2025). Fourier Spectral Deconvolution to Describe Behaviors of pH-Dependent Monomeric and Self-Associated Anthocyanin Species. Journal of Chemical Information and Modeling. 65(6). 2834–2844.
6.
Pike, J. Wesley, Matthew Taylor, James Raitt, et al.. (2024). Esmolol in persistent ventricular fibrillation/tachycardia with de-emphasised adrenaline – Introducing the REVIVE project. Resuscitation Plus. 21. 100842–100842.
7.
Gautam, Deepak, et al.. (2023). Data-driven irrigation scheduling increases the crop water use efficiency of Cabernet Sauvignon grapevines. Irrigation Science. 42(1). 29–44. 4 indexed citations
8.
9.
Bekker, Marlize Z., et al.. (2023). Effects of Yeast Strain and Juice Nitrogen Status on Glutathione Utilisation during Fermentation of Model Media. Australian Journal of Grape and Wine Research. 2023. 1–13. 3 indexed citations
10.
11.
Fracassetti, Daniela, et al.. (2022). Response Surface Methodology Approach to Evaluate the Effect of Transition Metals and Oxygen on Photo-Degradation of Methionine in a Model Wine System Containing Riboflavin. Journal of Agricultural and Food Chemistry. 70(51). 16347–16357. 3 indexed citations
12.
Bastian, Susan E.P., et al.. (2021). Modelling Cabernet-Sauvignon wine sensory traits from spectrofluorometric data. OENO One. 55(4). 19–33. 7 indexed citations
13.
Niimi, Jun, Kristian Hovde Liland, Oliver Tomić, et al.. (2020). Prediction of wine sensory properties using mid-infrared spectra of Cabernet Sauvignon and Chardonnay grape berries and wines. Food Chemistry. 344. 128634–128634. 13 indexed citations
14.
Niimi, Jun, Oliver Tomić, Tormod Næs, et al.. (2020). Objective measures of grape quality: From Cabernet Sauvignon grape composition to wine sensory characteristics. LWT. 123. 109105–109105. 25 indexed citations
15.
Capone, Dimitra L., et al.. (2020). Defining wine typicity: sensory characterisation and consumer perspectives. Australian Journal of Grape and Wine Research. 27(2). 246–256. 39 indexed citations
16.
Capone, Dimitra L., et al.. (2019). Using Content Analysis to Characterise the Sensory Typicity and Quality Judgements of Australian Cabernet Sauvignon Wines. Foods. 8(12). 691–691. 24 indexed citations
17.
Chen, Liang, et al.. (2019). Green characters: Using magnetic polymers to remove overpowering green capsicum flavour from Cabernet Sauvignon wine. 34(4). 24–26. 3 indexed citations
18.
Bekker, Marlize Z., Gal Y. Kreitman, David W. Jeffery, & John C. Danilewicz. (2018). Liberation of Hydrogen Sulfide from Dicysteinyl Polysulfanes in Model Wine. Journal of Agricultural and Food Chemistry. 66(51). 13483–13491. 44 indexed citations
19.
Gambetta, Joanna M., Susan E.P. Bastian, & David W. Jeffery. (2016). Snapshot of Australian production practices for Chardonnay wine. 27. 2 indexed citations
20.
Waterhouse, Andrew L., Gavin L. Sacks, & David W. Jeffery. (2016). Understanding Wine Chemistry. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 452 indexed citations breakdown →

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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