Evidence map›Paper›PMID 38714752›Full record

ArticleScientific reports2024

Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

John-Lewis Zinia Zaukuu, Zeenatu Suglo Adams, Nana Ama Donkor-Boateng, Eric Tetteh Mensah, Donald Bimpong, Lois Adofowaa Amponsah

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Maca (Foods (Basel, Switzerland) · 2026
    Review
  3. 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.

John-Lewis Zinia ZaukuuDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. zaukuu.jz@knust.edu.gh.
Zeenatu Suglo AdamsDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Nana Ama Donkor-BoatengDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Eric Tetteh MensahDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Donald BimpongDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Lois Adofowaa AmponsahDepartment of Food Science and Technology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared spectroscopy (NIRS). This study developed models to rapidly classify and predict 0, 10, 20, 30, 40, and 50% w/w of soybean and maize flour in red, black and yellow maca cultivars using a handheld spectrophotometer and chemometrics. Soy and maize adulteration of yellow MP was classified with better accuracy than in red MP, suggesting that red MP may be a more susceptible target for adulteration. Soy flour was discovered to be a more potent adulterant compared to maize flour. Using 18 different pretreatments, MP could be authenticated with R

Indexed as

Machine LearningPowdersSpectroscopy, Near-InfraredZea maysFlourFood ContaminationGlycine maxMacauSpectrophotometryPowdersAdulterationChemometricsMacaNear infraredSpectra-preprocessing

Identifiers

PMID38714752
PMCPMC11076633

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Registered trials

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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.