Daisuke Kaneko

1.0k total citations
28 papers, 669 citations indexed

About

Daisuke Kaneko is a scholar working on Food Science, Sensory Systems and Experimental and Cognitive Psychology. According to data from OpenAlex, Daisuke Kaneko has authored 28 papers receiving a total of 669 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Food Science, 18 papers in Sensory Systems and 12 papers in Experimental and Cognitive Psychology. Recurrent topics in Daisuke Kaneko's work include Sensory Analysis and Statistical Methods (21 papers), Olfactory and Sensory Function Studies (18 papers) and Multisensory perception and integration (12 papers). Daisuke Kaneko is often cited by papers focused on Sensory Analysis and Statistical Methods (21 papers), Olfactory and Sensory Function Studies (18 papers) and Multisensory perception and integration (12 papers). Daisuke Kaneko collaborates with scholars based in Netherlands, Japan and United States. Daisuke Kaneko's co-authors include Anne-Marie Brouwer, Jan B. F. van Erp, Alexander Toet, Victor Kallen, René A. de Wijk, Garmt Dijksterhuis, E.H. Zandstra, Toshinori Igarashi, Kenji Aoyama and Michel Visalli and has published in prestigious journals such as SHILAP Revista de lepidopterología, Immunity and Journal of Agricultural and Food Chemistry.

In The Last Decade

Daisuke Kaneko

28 papers receiving 664 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daisuke Kaneko Netherlands 11 326 203 160 136 126 28 669
A. M. Dalix France 14 73 0.2× 121 0.6× 60 0.4× 167 1.2× 36 0.3× 18 823
Jozef Youssef United Kingdom 18 240 0.7× 315 1.6× 327 2.0× 153 1.1× 251 2.0× 34 754
Anna Bäckström Finland 8 199 0.6× 31 0.2× 31 0.2× 51 0.4× 72 0.6× 15 509
Nicolas Antille Switzerland 12 220 0.7× 75 0.4× 29 0.2× 184 1.4× 17 0.1× 19 523
Emmanuelle Mauger France 13 44 0.1× 43 0.2× 142 0.9× 30 0.2× 61 0.5× 20 694
Sofie Lagast Belgium 10 436 1.3× 167 0.8× 122 0.8× 208 1.5× 190 1.5× 12 703
Monica Borgogno Italy 10 213 0.7× 105 0.5× 15 0.1× 116 0.9× 77 0.6× 11 441
Ekaterina A. Litvinova Russia 12 50 0.2× 50 0.2× 20 0.1× 42 0.3× 45 0.4× 52 395
Hollis Ashman Australia 14 322 1.0× 106 0.5× 56 0.3× 123 0.9× 76 0.6× 31 562
Pavlína Lenochová Czechia 5 27 0.1× 225 1.1× 108 0.7× 35 0.3× 55 0.4× 6 359

Countries citing papers authored by Daisuke Kaneko

Since Specialization
Citations

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

Fields of papers citing papers by Daisuke Kaneko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daisuke Kaneko

This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Kaneko. A scholar is included among the top collaborators of Daisuke Kaneko 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 Daisuke Kaneko. Daisuke Kaneko 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.
Wijk, René A. de, Daisuke Kaneko, Garmt Dijksterhuis, et al.. (2022). A preliminary investigation on the effect of immersive consumption contexts on food-evoked emotions using facial expressions and subjective ratings. Food Quality and Preference. 99. 104572–104572. 10 indexed citations
2.
Toet, Alexander, Erik Van der Burg, Tim J. van den Broek, et al.. (2022). Linking Categorical and Dimensional Approaches to Assess Food-Related Emotions. Foods. 11(7). 972–972. 2 indexed citations
3.
Kaneko, Daisuke, et al.. (2022). EEG measures of attention toward food-related stimuli vary with food neophobia. Food Quality and Preference. 106. 104805–104805. 8 indexed citations
4.
Kaneko, Daisuke, et al.. (2021). Comparing Explicit and Implicit Measures for Assessing Cross-Cultural Food Experience. PubMed. 2. 646280–646280. 5 indexed citations
5.
Dijksterhuis, Garmt, et al.. (2021). Exploring impact on eating behaviour, exercise and well-being during COVID-19 restrictions in the Netherlands. Appetite. 168. 105720–105720. 10 indexed citations
6.
Vingerhoeds, Monique H., et al.. (2021). Some insights into the development of food and brand familiarity: The case of soy sauce in the Netherlands. Food Research International. 142. 110200–110200. 8 indexed citations
7.
8.
Bergen, Geertje van, E.H. Zandstra, Daisuke Kaneko, Garmt Dijksterhuis, & René A. de Wijk. (2021). Sushi at the beach: Effects of congruent and incongruent immersive contexts on food evaluations. Food Quality and Preference. 91. 104193–104193. 20 indexed citations
9.
Kaneko, Daisuke, Anne-Marie Brouwer, Maarten A. Hogervorst, et al.. (2020). Emotional State During Tasting Affects Emotional Experience Differently and Robustly for Novel and Familiar Foods. Frontiers in Psychology. 11. 558172–558172. 4 indexed citations
10.
Brouwer, Anne-Marie, Tim J. van den Broek, Maarten A. Hogervorst, et al.. (2020). Estimating Affective Taste Experience Using Combined Implicit Behavioral and Neurophysiological Measures. IEEE Transactions on Affective Computing. 14(1). 849–856. 6 indexed citations
11.
Toet, Alexander, et al.. (2019). CROCUFID: A Cross-Cultural Food Image Database for Research on Food Elicited Affective Responses. Frontiers in Psychology. 10. 58–58. 43 indexed citations
12.
Wijk, René A. de, et al.. (2019). Food perception and emotion measured over time in-lab and in-home. Food Quality and Preference. 75. 170–178. 53 indexed citations
13.
Toet, Alexander, Yingxuan Liu, Stella F. Donker, et al.. (2019). The Relation Between Valence and Arousal in Subjective Odor Experience. Chemosensory Perception. 13(2). 141–151. 10 indexed citations
14.
Kaneko, Daisuke, et al.. (2019). Explicit and Implicit Responses to Tasting Drinks Associated with Different Tasting Experiences. Sensors. 19(20). 4397–4397. 26 indexed citations
15.
Toet, Alexander, et al.. (2018). EmojiGrid: A 2D Pictorial Scale for the Assessment of Food Elicited Emotions. Frontiers in Psychology. 9. 2396–2396. 58 indexed citations
16.
Kaneko, Daisuke, Alexander Toet, Anne-Marie Brouwer, Victor Kallen, & Jan B. F. van Erp. (2018). Methods for Evaluating Emotions Evoked by Food Experiences: A Literature Review. Frontiers in Psychology. 9. 911–911. 99 indexed citations
17.
Kaneko, Daisuke, et al.. (2018). EmojiGrid: A 2D pictorial scale for cross-cultural emotion assessment of negatively and positively valenced food. Food Research International. 115. 541–551. 34 indexed citations
18.
Kaneko, Daisuke, Victor Kallen, Maarten A. Hogervorst, et al.. (2017). Physiological Responses to Tasting Drinks are Associated with Different Tasting Experiences. Psychophysiology. 54. 1 indexed citations
19.
Kaneko, Daisuke, Toshinori Igarashi, & Kenji Aoyama. (2014). Reduction of the Off-Flavor Volatile Generated by the Yogurt Starter Culture Including Streptococcus thermophilus and Lactobacillus delbrueckii subsp.bulgaricusin Soymilk. Journal of Agricultural and Food Chemistry. 62(7). 1658–1663. 40 indexed citations
20.
Kawashima, Tadaomi, Akemi Kosaka, Huimin Yan, et al.. (2013). Double-Stranded RNA of Intestinal Commensal but Not Pathogenic Bacteria Triggers Production of Protective Interferon-β. Immunity. 38(6). 1187–1197. 159 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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