Evidence map›Paper›PMID 32012652›Full record

ArticleSensors (Basel, Switzerland)2020

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model.

Jingjing Liu, Mingxu Zuo, Sze Shin Low, Ning Xu, Zhiqing Chen, Chuang Lv, Ying Cui, Yan Shi, Hong Men

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

9 authors.

Jingjing LiuCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Mingxu ZuoCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Sze Shin LowBiosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China.
Ning XuCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Zhiqing ChenCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Chuang LvCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Ying CuiCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Yan ShiCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
Hong MenCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.

Funding

National Natural Science Foundation of China 31401569National Natural Science Foundation of China 31772059National Natural Science Foundation of China 31871882the Key Science and Technology Project of Jilin Province 20170204004SFthe State Scholarship Fund of China Scholarship Council 201808220037
6 · The paper itself

Abstract

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Indexed as

Chinese liquorelectronic tonguefuzzy wordstwo-dimensional cloud model

Identifiers

PMID32012652
PMCPMC7038490

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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.