Evidence map›Paper›PMID 42122594›Full record

ArticleSensors (Basel, Switzerland)2026

A Fluorescence-Based Sensor Combined with Chemometric and Deep Learning Approaches for Detecting and Quantifying Coconut Milk Fraud in Bovine Milk.

Stella Maria Dyah Cahyarani, Hoonsoo Lee

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

2 authors.

Stella Maria Dyah CahyaraniDepartment of Biosystems Engineering, College of Agriculture, Life, and Environment Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea.
Hoonsoo LeeDepartment of Biosystems Engineering, College of Agriculture, Life, and Environment Sciences, Chungbuk National University, Cheongju 28644, Republic of Korea.ORCID 0000-0001-8074-4234

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bovine milk adulteration with coconut milk poses a significant threat to food safety, as both liquids are visually similar yet nutritionally distinct. This study presents an integrated analytical framework combining excitation-emission matrix (EEM) fluorescence spectroscopy with chemometric and deep learning techniques to detect and quantify coconut milk adulteration in bovine milk across nine concentration levels (0-100%

Indexed as

CocosDeep LearningFood ContaminationMilkAnimalsCattleFluorescenceRiboflavinSpectrometry, FluorescenceTryptophanRiboflavinTryptophandeep learningexcitation–emission matrixfluorescence spectroscopyfood authenticationmilk adulteration

Identifiers

PMID42122594
PMCPMC13165615

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.