Danielle van Hout

718 total citations
29 papers, 548 citations indexed

About

Danielle van Hout is a scholar working on Food Science, Social Psychology and Nutrition and Dietetics. According to data from OpenAlex, Danielle van Hout has authored 29 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Food Science, 13 papers in Social Psychology and 12 papers in Nutrition and Dietetics. Recurrent topics in Danielle van Hout's work include Sensory Analysis and Statistical Methods (29 papers), Biochemical Analysis and Sensing Techniques (12 papers) and Color perception and design (12 papers). Danielle van Hout is often cited by papers focused on Sensory Analysis and Statistical Methods (29 papers), Biochemical Analysis and Sensing Techniques (12 papers) and Color perception and design (12 papers). Danielle van Hout collaborates with scholars based in Netherlands, South Korea and New Zealand. Danielle van Hout's co-authors include Michael J. Hautus, Hye-Seong Lee, Min-A Kim, Inah Kim, Michael O’Mahony, Andrew Hopkinson, Michael O’Mahony, In-Ah Kim, Yeon Joo Lee and Daniel Shepherd and has published in prestigious journals such as Food Research International, Journal of Food Science and Food Quality and Preference.

In The Last Decade

Danielle van Hout

29 papers receiving 534 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Danielle van Hout Netherlands 14 447 256 147 120 107 29 548
John M. Ennis United States 15 493 1.1× 280 1.1× 73 0.5× 81 0.7× 149 1.4× 44 812
Benoı̂t Rousseau United States 14 557 1.2× 403 1.6× 134 0.9× 56 0.5× 176 1.6× 22 611
KWANG‐OK KIM South Korea 15 537 1.2× 294 1.1× 84 0.6× 78 0.7× 87 0.8× 22 627
Thierry Worch Netherlands 17 504 1.1× 189 0.7× 92 0.6× 112 0.9× 75 0.7× 30 636
Michael Meyners Germany 16 1.0k 2.3× 476 1.9× 242 1.6× 126 1.1× 209 2.0× 47 1.3k
Pieter Punter Netherlands 16 373 0.8× 209 0.8× 139 0.9× 91 0.8× 51 0.5× 21 629
Michel Rogeaux France 10 441 1.0× 212 0.8× 151 1.0× 85 0.7× 133 1.2× 16 626
Sara De Pelsmaeker Belgium 12 453 1.0× 179 0.7× 107 0.7× 167 1.4× 67 0.6× 18 729
M. O'MAHONY United States 11 243 0.5× 241 0.9× 146 1.0× 31 0.3× 92 0.9× 14 408
Jean A. McEwan United Kingdom 17 785 1.8× 386 1.5× 135 0.9× 168 1.4× 180 1.7× 34 975

Countries citing papers authored by Danielle van Hout

Since Specialization
Citations

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

Fields of papers citing papers by Danielle van Hout

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danielle van Hout. 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 Danielle van Hout. The network helps show where Danielle van Hout may publish in the future.

Co-authorship network of co-authors of Danielle van Hout

This figure shows the co-authorship network connecting the top 25 collaborators of Danielle van Hout. A scholar is included among the top collaborators of Danielle van Hout 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 Danielle van Hout. Danielle van Hout 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.
Lawlor, John, Danielle van Hout, Jean A. McEwan, et al.. (2024). Opinion note: Digitalization in sensory and consumer science – Summary perspectives from presentations at the 15th Pangborn sensory science symposium. Food Quality and Preference. 124. 105372–105372. 1 indexed citations
2.
Lee, Yeon‐Joo, Danielle van Hout, & Hye-Seong Lee. (2024). The signal detection expectation profiling method with a two-step rating for guiding product optimization. Food Quality and Preference. 117. 105170–105170. 1 indexed citations
3.
Hout, Danielle van, et al.. (2022). Improving the performance of A-Not AR discrimination test using a sensory panel: Effects of the test protocols on sensory data quality. Food Quality and Preference. 104. 104740–104740. 7 indexed citations
4.
Hautus, Michael J., et al.. (2022). The observed variance of dʹ estimates compared across the 2-AFCR, Triangle, and Tetrad tasks. Food Quality and Preference. 100. 104578–104578. 2 indexed citations
5.
Lee, Yeon Joo, Inah Kim, Danielle van Hout, & Hye-Seong Lee. (2021). Investigating effects of cognitively evoked situational context on consumer expectations and subsequent consumer satisfaction and sensory evaluation. Food Quality and Preference. 94. 104330–104330. 18 indexed citations
6.
Kim, Min-A, Danielle van Hout, Elizabeth H. Zandstra, & Hye-Seong Lee. (2019). Consumer acceptance measurement focusing on a specified sensory attribute of products: Can the attribute-specified degree of satisfaction-difference (DOSD) method replace hedonic scaling?. Food Quality and Preference. 75. 198–208. 9 indexed citations
7.
Hautus, Michael J., et al.. (2018). Variation of d′ estimates in two versions of the A‐Not A task. Journal of Sensory Studies. 33(6). 4 indexed citations
8.
9.
Kim, Inah, et al.. (2015). Development of A Consumer‐Relevant Lexicon for Testing Kitchen Cleansers Considering Different Product Usage Stages. Journal of Sensory Studies. 30(6). 448–460. 7 indexed citations
10.
Kim, Min-A, et al.. (2014). Consumer context-specific sensory acceptance tests: Effects of a cognitive warm-up on affective product discrimination. Food Quality and Preference. 41. 163–171. 30 indexed citations
11.
Kim, Min-A, et al.. (2013). Higher performance of constant-reference duo–trio test incorporating affective reference framing in comparison with triangle test. Food Quality and Preference. 32. 113–125. 32 indexed citations
12.
Hout, Danielle van, et al.. (2013). Cognitive Decision Strategies Adopted in Reminder Tasks by Trained Judges When Discriminating Aqueous Solutions Differing in the Concentration of Citric Acid. Journal of Sensory Studies. 28(3). 217–229. 13 indexed citations
13.
Hout, Danielle van, et al.. (2013). Cognitive decision strategies adopted by trained judges in reminder difference tests when tasting yoghurt, mayonnaise, and ice tea. Food Quality and Preference. 34. 14–23. 16 indexed citations
14.
15.
Kim, Min-A, et al.. (2011). Discriminations of the A–Not A difference test improved when “A” was familiarized using a brand image. Food Quality and Preference. 23(1). 3–12. 26 indexed citations
16.
Hout, Danielle van, et al.. (2009). Quantification of Sensory and Food Quality: The R‐Index Analysis. Journal of Food Science. 74(6). R57–64. 60 indexed citations
17.
Hautus, Michael J., et al.. (2008). Variants of A Not-A and 2AFC tests: Signal Detection Theory models. Food Quality and Preference. 20(3). 222–229. 46 indexed citations
18.
19.
Hout, Danielle van, et al.. (2007). Can the same-different test use a β-criterion as well as a τ-criterion?. Food Quality and Preference. 18(4). 605–613. 32 indexed citations
20.
Hout, Danielle van, et al.. (2006). Sensory difference tests for margarine: A comparison of R-Indices derived from ranking and A-Not A methods considering response bias and cognitive strategies. Food Quality and Preference. 18(4). 675–680. 30 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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2026