Evidence map›Paper›PMID 36698480›Full record

ArticleFrontiers in nutrition2022

Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings.

Rong Ma, Huishan Shen, Hao Cheng, Guoquan Zhang, Jianmei Zheng

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
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  5. Article
  6. Article
  7. Article
  8. Characterization ofFoods (Basel, Switzerland) · 2025
    Article
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  10. Review
  11. Characterizing the flavor profiles ofFood chemistry: X · 2024
    Article
  12. Article
  13. Causal Artificial Intelligence Models of Food Quality Data.Food technology and biotechnology · 2024
    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

5 authors.

Rong MaEngineering Research Center of Grain and Oil Functionalized Processing in Universities of Shaanxi, College of Food Science and Engineering, Northwest A&F University, Xianyang, Shaanxi, China.
Huishan ShenEngineering Research Center of Grain and Oil Functionalized Processing in Universities of Shaanxi, College of Food Science and Engineering, Northwest A&F University, Xianyang, Shaanxi, China.
Hao ChengEngineering Research Center of Grain and Oil Functionalized Processing in Universities of Shaanxi, College of Food Science and Engineering, Northwest A&F University, Xianyang, Shaanxi, China.
Guoquan ZhangEngineering Research Center of Grain and Oil Functionalized Processing in Universities of Shaanxi, College of Food Science and Engineering, Northwest A&F University, Xianyang, Shaanxi, China.
Jianmei ZhengEngineering Research Center of Grain and Oil Functionalized Processing in Universities of Shaanxi, College of Food Science and Engineering, Northwest A&F University, Xianyang, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.

Indexed as

combined datae-nosee-tongueinstant starch noodles seasoningsmultilayer perceptron neural networks analysis

Identifiers

PMID36698480
PMCPMC9868914

What OpenQuestion holds

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LicenceCC BY
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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.