Evidence map›Paper›PMID 30513979›Full record

ArticleSensors (Basel, Switzerland)2018

Evaluation of Food Fineness by the Bionic Tongue Distributed Mechanical Testing Device.

Jingjing Liu, Ying Cui, Yizhou Chen, Wei Wang, Yuanyuan Tang, Hong Men

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Jingjing LiuCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. jingjing_liu@neepu.edu.cn.ORCID 0000-0002-9602-8918
Ying CuiCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. 2201600406@neepu.edu.cn.
Yizhou ChenDepartment of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA. yizhouc1@uci.edu.
Wei WangCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. w_wei110@163.com.
Yuanyuan TangCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. tyying1213@sina.com.
Hong MenCollege of Automation Engineering, Northeast Electric Power University, Jilin 132012, China. menhong@neepu.edu.cn.

Funding

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

Abstract

In this study, to obtain a texture perception that is closer to the human sense, we designed eight bionic tongue indenters based on the law of the physiology of mandibular movements and tongue movements features, set up a bionic tongue distributed mechanical testing device, performed in vitro simulations to obtain the distributed mechanical information over the tongue surface, and preliminarily constructed a food fineness perception evaluation model. By capturing a large number of tongue movements during chewing, we analyzed and simulated four representative tongue movement states including the tiled state, sunken state, raised state, and overturned state of the tongue. By analyzing curvature parameters and the Gauss curvature of the tongue surface, we selected the regional circle of interest. With that, eight bionic tongue indenters with different curvatures over the tongue surface were designed. Together with an arrayed film pressure sensor, we set up a bionic tongue distributed mechanical testing device, which was used to do contact pressure experiments on three kinds of cookies-WZ Cookie, ZL Cookie and JSL Cookie-with different fineness texture characteristics. Based on the distributed mechanical information perceived by the surface of the bionic tongue indenter, we established a food fineness perception evaluation model by defining three indicators, including gradient, stress change rate and areal density. The correlation between the sensory assessment and model result was analyzed. The results showed that the average values of correlation coefficients among the three kinds of food with the eight bionic tongue indenters reached 0.887, 0.865, and 0.870, respectively, that is, a significant correlation was achieved. The results illustrate that the food fineness perception evaluation model is effective, and the bionic tongue distributed mechanical testing device has a good practical significance for obtaining food texture mouthfeel information.

Indexed as

Electronic NoseBionicsFood AnalysisHumansMasticationMechanical PhenomenaMovementPressureToucharrayed film pressure sensorbionic tongue indentercurvature differencefineness perception evaluation modelsensory assessmenttexture perception

Identifiers

PMID30513979
PMCPMC6308582

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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.