Science Explorer Interactive view Map
Topic · Food Science

Sensory Analysis and Statistical Methods

Sensory Analysis and Statistical Methods is a research topic within Food Science. Science Explorer counts 19k research works in it since 1950. 21.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on sensory analysis in food science research, exploring topics such as consumer perception, emotional response to food, product characterization, and the influence of cultural diversity on food preferences. The papers cover various methods including multivariate analysis, preference mapping, temporal dominance of sensations, and cross-cultural studies.

  • Sensory Analysis
  • Consumer Perception
  • Food Choice
  • Emotional Response
  • Product Characterization
  • Multivariate Analysis
  • Neophobia
  • Preference Mapping
  • Temporal Dominance of Sensations
  • Cross-Cultural Study
Research works
19k
fractional, since 1950
In the world top 10%
4k
per year above
Top-10% rate
21.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+24%
the tick is no change

Which countries lead Sensory Analysis and Statistical Methods research?

By volume, the United States and Brazil publish the most (351 and 320 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 351 works
  2. 2 Brazil 320 works
  3. 3 China 239 works
  4. 4 Italy 135 works
  5. 5 France 120 works
  6. 6 Spain 118 works
  7. 7 India 114 works
  8. 8 Indonesia 106 works
  9. 9 Türkiye 95 works
  10. 10 Japan 94 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 20.7%Brazil: 18.9%China: 14.1%Italy: 8.0%6 others listed: 38.2%21%largest
United States351 · 20.7%Brazil320 · 18.9%China239 · 14.1%Italy135 · 8.0%6 others listed647 · 38.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Sensory Analysis and Statistical Methods research?

By volume in 2022–2025, Wageningen University & Research publishes the most Sensory Analysis and Statistical Methods research, followed by Universidade Federal de Lavras and Universidade Estadual de Campinas (UNICAMP).

Who are the leading researchers in Sensory Analysis and Statistical Methods?

The most-cited researchers publishing on Sensory Analysis and Statistical Methods include Peter M. Bentler, Marko Sarstedt and Christian M. Ringle.

  1. 1 Peter M. Bentler United States 16k citations
  2. 2 Marko Sarstedt Germany 11k citations
  3. 3 Christian M. Ringle Germany 11k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Sensory Analysis and Statistical Methods research done?

The largest centres of Sensory Analysis and Statistical Methods research in 2022–2025 are Beijing (China), Paris (France), São Paulo (Brazil) and Tokyo (Japan). Among places with at least 20 works in it, it is an unusually large share of all research in Wageningen.

Largest cities, 2022–2025

  1. 1 Beijing China 46 works
  2. 2 Paris France 36 works
  3. 3 São Paulo Brazil 35 works
  4. 4 Tokyo Japan 35 works
  5. 5 Seoul South Korea 25 works
  6. 6 Wageningen Netherlands 23 works
  7. 7 Jakarta Indonesia 19 works
  8. 8 Rio de Janeiro Brazil 19 works
  9. 9 Shanghai China 19 works
  10. 10 London United Kingdom 18 works

Where it is the local speciality

  1. WageningenNL · 22.8 works20×
← less than its size predictsmore →

Location quotient: how much more of its research is in Sensory Analysis and Statistical Methods than the world average.

See Sensory Analysis and Statistical Methods on the map

Where is the best place to study Sensory Analysis and Statistical Methods?

Among universities, judged by research, Wageningen University & Research, Acadia University and Universidade Federal de Lavras score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 20.23%fractional works in this node (log) →share in the world top 10% →Wageningen University & Research: 21, 27.4%Acadia University: 10, 32.2%Universidade Federal de Lavras: 18, 15.5%Universidade Estadual de Campinas (UNICAMP): 14, 18.1%The Ohio State University: 8, 34.2%Universitat Politècnica de València: 9, 25.0%Pennsylvania State University: 9, 23.1%Universidade Federal de Viçosa: 8, 10.0%Binus University: 10, 12.9%Universidad Técnica de Manabí: 9, 3.9%Acadia UniversityWageningen Universit…Universidade Estadua…Universidade Federal…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Wageningen University & ResearchNetherlands 83.227.4%19.9×21 +31.8%
2 Acadia UniversityCanada 65.732.2%147.1×10
3 Universidade Federal de LavrasBrazil 60.415.5%37.6×18 +18.1%
4 Universidade Estadual de Campinas (UNICAMP)Brazil 52.218.1%6.6×14 +78.4%
5 The Ohio State UniversityUnited States 51.934.2%3.2×8 +165.8%
6 Universitat Politècnica de ValènciaSpain 46.825.0%8.4×9 -50.0%
7 Pennsylvania State UniversityUnited States 36.023.1%3.3×9 +52.9%
8 Universidade Federal de ViçosaBrazil 35.410.0%11.7×8 -26.0%
9 Binus UniversityIndonesia 34.012.9%6.5×10
10 Universidad Técnica de ManabíEcuador 32.83.9%20.8×9

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Sensory Analysis and Statistical Methods research growing?

Output in 2018–2022 was 24% higher than in 2013–2017, peaking in 2021. The fastest-growing topics are Sensory Analysis and Statistical Methods.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.