Evidence map›Paper›PMID 42289544›Full record

ArticleAnalytical sciences : the international journal of the Japan Society for Analytical Chemistry2026

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

Ryota Nomura, Yoshihito Yamauchi, Hiroto Misawa, Kosuke Nishigaya, Satoshi Ooyama, Hiromasa Kaneko

Abstract read
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In one paragraph

Article in Analytical sciences : the international journal of the Japan Society for Analytical Chemistry, 2026. 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
–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

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.

Ryota NomuraDepartment of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan.
Yoshihito YamauchiMitsubishi Chemical Corporation, 3-10 Ushiodori, Kurashiki, Okayama, 712-8054, Japan.
Hiroto MisawaMitsubishi Chemical Corporation, 3-10 Ushiodori, Kurashiki, Okayama, 712-8054, Japan.
Kosuke NishigayaMitsubishi Chemical Corporation, 3-10 Ushiodori, Kurashiki, Okayama, 712-8054, Japan.
Satoshi OoyamaMitsubishi Chemical Corporation, 3-10 Ushiodori, Kurashiki, Okayama, 712-8054, Japan.
Hiromasa KanekoDepartment of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan. hkaneko@meiji.ac.jp.ORCID http://orcid.org/0000-0001-8367-6476

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed a soft sensor to predict polyvinyl alcohol viscosity and designed process conditions to reach the target range. Using squared and cross terms and time-series data improved prediction accuracy. In the case study, optimal conditions brought off-target products into range.

Indexed as

Design process conditionsMachine learningPolyvinyl alcoholPredictionSoft sensorViscosity

Identifiers

PMID42289544
PMCPMC13294264

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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.