ArticleFood chemistry: X2025
Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms.
Article in Food chemistry: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed.
- SERS-Based Detection of Food Contaminants: From Laboratory Sensitivity to Practical Implementation-Bottlenecks and Pathways to Standardization.Foods (Basel, Switzerland) · 2026Review
- Near-Infrared Spectroscopy in Food Analysis: Applications, Chemometric Strategies, and Technological Advances.Foods (Basel, Switzerland) · 2026Review
- Research Progress in the Detection of Mycotoxins in Cereals and Their Products by Vibrational Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- A Ten-Year Trend Analysis ofFoods (Basel, Switzerland) · 2025Article
- Advancements in Intelligent Sensing Technologies for Food Safety Detection.Research (Washington, D.C.) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fumonisins occurrence in maize represents a significant global challenge, impacting economic stability and food safety. This study evaluates the potential of near-infrared (NIR) spectroscopy combined with chemometric algorithms to detect fumonisins in maize. For fumonisin B1 (FB1) and B2 (FB2) levels were developed predictive NIR models using partial least squares (PLS) and artificial neural networks (ANN). PLS models demonstrated strong correlation coefficient (R
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Registered trials
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.