Evidence map›Paper›PMID 35789802›Full record

ArticleCurrent research in food science2022

Flavour network-based analysis of food pairing: Application to the recipes of the sub-cuisines from Northeast India.

L V Makinei, M K Hazarika

Abstract read
In one paragraph

Article in Current research in food science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

L V MakineiDepartment of Food Engineering and Technology, Tezpur University, Tezpur, India.
M K HazarikaDepartment of Food Engineering and Technology, Tezpur University, Tezpur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The flavour network-based analysis of food pairing was applied to the sub-cuisines from Northeast India to examine the food pairing behaviour in terms of the co-occurrence of ingredients with the shared flavouring compounds in food recipes. The method applied was based on an existing procedure in computational gastronomy, wherein the preference for positive pairing is attributed to dairy-based ingredients and negative pairing behaviour is attributed primarily to spice based ingredients. Recipe data was subjected to backbone extraction, projection of the recipe-ingredient-compound tri-partite network, and analysis for prevalence and authenticity of ingredients. Further, the average flavour sharing index of the cuisine was determined with the help of the flavour profiles of the ingredients. The extent of deviation for the original cuisine in comparison to a random cuisine was used to determine the degree of bias in the food pairing behaviour, with the sign as the indicator of the nature of pairing. The analysis identified the ingredients responsible to exhibit a positive or negative pairing pattern in the sub-cuisines. The ingredients from the spice category were the most prevalent and have resulted in the negative pairing behaviour in the cuisines. This role of spices in effecting a negative pairing behaviour is in line with the earlier reports for other Indian regional cuisines.

Indexed as

Computational gastronomyFlavour compoundsFood-pairingIngredientsNetwork theoryRecipe

Identifiers

PMID35789802
PMCPMC9249598

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.