Evidence map›Paper›PMID 37069310›Full record

ArticleScientific reports2023

Comparison of learning models to predict LDPE, PET, and ABS concentrations in beach sediment based on spectral reflectance.

Faisal Raiyan Huda, Florina Stephanie Richard, Ishraq Rahman, Saeid Moradi, Clarence Tay Yuen Hua, Christabel Anfield Sim Wanwen, Ting Lik Fong, Aazani Mujahid, Moritz Müller

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

Article in Scientific reports, 2023. 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 32% 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, 13 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

9 authors at 3 institutions in 2 countries.

Faisal Raiyan HudaFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.ORCID http://orcid.org/0000-0002-1529-5185
Florina Stephanie RichardFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.ORCID http://orcid.org/0000-0001-7041-1663
Ishraq RahmanFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.
Saeid MoradiFaculty of Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC, V2C0C8, Canada.
Clarence Tay Yuen HuaFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.
Christabel Anfield Sim WanwenFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.
Ting Lik FongFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia.
Aazani MujahidInstitute of Sustainable and Renewable Energy (ISuRE), Universiti Malaysia Sarawak, Kota Samarahan, 94300, Sarawak, Malaysia.ORCID http://orcid.org/0000-0002-8672-8021
Moritz MüllerFaculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, 93350, Kuching, Sarawak, Malaysia. mmueller@swinburne.edu.my.ORCID http://orcid.org/0000-0001-8485-1598
Swinburne University of Technology Sarawak Campus · MYThompson Rivers University · CAUniversiti Malaysia Sarawak · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microplastic (MP) contamination on land has been estimated to be 32 times higher than in the oceans, and yet there is a distinct lack of research on soil MPs compared to marine MPs. Beaches are bridges between land and ocean and present equally understudied sites of microplastic pollution. Visible-near-infrared (vis-NIR) has been applied successfully for the measurement of reflectance and prediction of low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC) concentrations in soil. The rapidity and precision associated with this method make vis-NIR promising. The present study explores PCA regression and machine learning approaches for developing learning models. First, using a spectroradiometer, the spectral reflectance data was measured from treated beach sediment spiked with virgin microplastic pellets [LDPE, PET, and acrylonitrile butadiene styrene (ABS)]. Using the recorded spectral data, predictive models were developed for each microplastic using both the approaches. Both approaches generated models of good accuracy with R

Identifiers

PMID37069310
PMCPMC10110612
OpenAlexW4366162966

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.