Evidence map›Paper›PMID 35818893›Full record

ArticleScience progress2021

Non-destructive acoustic screening of pineapple ripeness by unsupervised machine learning and Wavelet Kernel methods.

Yenming J Chen, Yeong-Cheng Liou, Wen-Hsien Ho, Jinn-Tsong Tsai, Chia-Chuan Liu, Kao-Shing Hwang

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science progress, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.9field-weighted citation impact, top 19% of its field
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

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Review
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 at 4 institutions in 1 country.

Yenming J ChenDepartment of Information Management, 517768National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan.
Yeong-Cheng LiouDepartment of Healthcare Administration and Medical Informatics, 38023Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Wen-Hsien HoDepartment of Healthcare Administration and Medical Informatics, 38023Kaohsiung Medical University, Kaohsiung 807, Taiwan.ORCID 0000-0001-6194-0563
Jinn-Tsong TsaiDepartment of Healthcare Administration and Medical Informatics, 38023Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Chia-Chuan LiuDepartment of Information Management, 517768National Kaohsiung University of Science and Technology, Kaohsiung 824, Taiwan.
Kao-Shing HwangDepartment of Healthcare Administration and Medical Informatics, 38023Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Kaohsiung Medical University · TWNational Kaohsiung University of Science and TechnologyNational Pingtung University · TWNational Sun Yat-sen University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In a pineapple exporting factory, manual lines are usually built to screen fruits of non-ripen hitting sounds from millions of undecided fruits for long-haul transportation. However, human workers cannot concentratedly listen and make consistent judgments over long hours. Pineapple screening becomes arbitrary after approximately an hour. We developed a non-destructive screening device aside from the conveyor sorter to classify pineapples automatically. The device makes intelligent judgments by tapping a sound source to the skin of pineapples and analyzing the penetrated sounds by wavelet kernel decomposition and unsupervised machine learning (ML). The sound tapping relies on the well-touch of the skin. We also design several acoustic couplers to adapt the vibrator to the skin and pick high-quality penetrated sounds. A Taguchi experiment design was used to determine the most suitable coupler. We found that our unsupervised ML method achieves 98.56% accuracy and 0.93 F1-score by using a specially designed thorn-board for assisting tapping sound to fruit skin.

Indexed as

AnanasAcousticsFruitHumansUnsupervised Machine Learningnon-destructive screeningPineapple exportpineapple ripenessunsupervised clusteringWavelet Kernel decomposition

Identifiers

PMID35818893
PMCPMC10496471
OpenAlexW4285050229

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.