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Toxins
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Advanced Analytical Technologies in Mycotoxins Detection for the One Health
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Advanced Analytical Technologies in Mycotoxins Detection for the One Health

A Special Issue of Toxins (ISSN 2072-6651) belonging to the section "Mycotoxins".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2695

Editors

Prof. Dr. Laura Anfossi

E-Mail Website
Guest Editor
Department of Chemistry, Universita degli Studi di Torino, 10124 Torino, Italy
Interests: mycotoxins; analytical chemistry; immunoassay; biosensors
Special Issues, Collections and Topics in MDPI journals
Dr. Simone Cavalera

E-Mail Website
Guest Editor
Department of Chemistry, University of Torino, 10125 Torino, Italy
Interests: mycotoxins; analytical chemistry; immunoassay; biosensors

Special Issue Information

Dear Colleagues,

The detection and quantification of mycotoxins remain a critical challenge in analytical chemistry. In fact, despite significant progress, current methodologies often struggle with sensitivity, selectivity, matrix complexity, and the simultaneous detection of multiple mycotoxins. This Special Issue, “Advanced Analytical Technologies in Mycotoxins Detection for the One Health”, aims to address these challenges by showcasing state-of-the-art analytical strategies and fostering innovation in the field, focusing on new applications beyond ensuring food safety and regulatory compliance.

This Special Issue will explore the development and application of advanced techniques such as high-resolution mass spectrometry, novel chromatographic systems, biosensors, and immunoassays. It will also highlight emerging trends in sample preparation, miniaturization, automation, and the use of nanomaterials and synthetic receptors (i.e., molecularly imprinted polymers, aptamers, synthetic peptides, etc.). A key focus will be on multi-mycotoxin detection, method validation, and regulatory alignment, reflecting the growing demand for comprehensive, rapid, and cost-effective solutions. In line with the One Health approach, this Special Issue will also emphasize the importance of detecting mycotoxins in non-conventional matrices, including environmental samples and biological fluids from humans and animals. These efforts support early warning systems, remediation strategies, and preventive measures that contribute to holistic health outcomes across ecosystems.

Researchers are invited to submit original research articles, reviews, and technical notes that advance the analytical capabilities for mycotoxin detection and contribute to safer food systems worldwide.

Prof. Dr. Laura Anfossi
Dr. Simone Cavalera
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Toxins is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mycotoxins
  • advanced analytical techniques
  • multi-mycotoxin detection
  • non-conventional matrices
  • one health
  • biosensors
  • mass spectrometry

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Published Papers (2 papers)

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Research

15 pages, 21135 KB  
Article
Feasibility of Hyperspectral Imaging and Machine Learning for Rapid Prescreening of Aflatoxin B1 in Maize Kernels
by Yongping Jiang, Bowen Tai, Yufan Yang, Xinyue Zhang, Jing Jin and Fuguo Xing
Toxins 2026, 18(7), 308; https://doi.org/10.3390/toxins18070308 - 15 Jul 2026
Viewed by 502
Abstract
Aflatoxin B1 (AFB1) contamination in maize poses serious risks to food and feed safety; however, conventional laboratory-based assays are often constrained by time and throughput for large-scale screening. This study proposes a hyperspectral imaging (HSI) workflow for rapid, non-destructive prediction [...] Read more.
Aflatoxin B1 (AFB1) contamination in maize poses serious risks to food and feed safety; however, conventional laboratory-based assays are often constrained by time and throughput for large-scale screening. This study proposes a hyperspectral imaging (HSI) workflow for rapid, non-destructive prediction of AFB1. A total of 236 hyperspectral images were acquired in the 400–1000 nm range (256 bands) from maize samples covering a broad gradient of AFB1 contamination, and spectral features were extracted from regions of interest (ROI) for model development. The results demonstrate that appropriate spectral preprocessing and wavelength selection play a critical role in improving model robustness, with the SNV–CARS–KNN model achieving the best prediction performance (test R2 = 0.9341, RMSE = 4.8938). Based on the predicted AFB1 values, contamination grading was further explored to enable rapid screening and risk management in agricultural applications. Aflatoxin B1 (AFB1) contamination in maize poses substantial risks to food and feed safety, creating a need for rapid screening tools that can support high-throughput prescreening. In this study, hyperspectral imaging (HSI) was explored as a non-destructive approach for the preliminary assessment of AFB1 contamination in maize kernels. A total of 236 hyperspectral images were collected in the 400–1000 nm range, and region-of-interest spectra were extracted for model development. Spectral preprocessing and wavelength selection strategies were compared in combination with several conventional regression models to examine their influence on predictive performance using the current dataset. Among the tested combinations, the SNV–CARS–KNN model showed the most favorable performance on the held-out test set (R2 = 0.9341; RMSE = 4.8938). In addition, a grade-based classification derived from predicted values was explored as an application-oriented extension for rapid risk sorting. However, the study was conducted on artificially contaminated samples and relied on rapid-test-derived reference values, so the findings should be interpreted as a proof of concept rather than a fully validated quantitative method. Overall, the results support the potential of HSI for rapid prescreening of AFB1 in maize and provide a basis for further validation using more rigorous reference analysis and independent sample sets. Full article
(This article belongs to the Special Issue Advanced Analytical Technologies in Mycotoxins Detection for the One Health)
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15 pages, 2042 KB  
Article
Matrix Effect Variability in Urine Samples from Different Cohorts and Implications on LC-ESI-MS Mycotoxin Biomarker Analysis
by Michael Kuhn, Åsa Svanström, Nicholas N. A. Kyei, Sanna Lignell, Hans-Ulrich Humpf and Benedikt Cramer
Toxins 2026, 18(3), 135; https://doi.org/10.3390/toxins18030135 - 10 Mar 2026
Viewed by 1513
Abstract
Matrix effects (ME) during LC-ESI-MS analysis are a commonly acknowledged issue for a variety of matrices and analytes. Although sample preparation techniques are steadily evolving to reduce ME, the complexity and variability of the urine matrix remain a challenge, especially for multi-analyte methods. [...] Read more.
Matrix effects (ME) during LC-ESI-MS analysis are a commonly acknowledged issue for a variety of matrices and analytes. Although sample preparation techniques are steadily evolving to reduce ME, the complexity and variability of the urine matrix remain a challenge, especially for multi-analyte methods. To investigate the extent of ME implications on method performance and quantification, we used stable isotope-labelled standards (SIL-IS) of 11 mycotoxins to evaluate the magnitude and variability of ME in urine samples from two cohorts: Bangladeshi adult women (n = 50) and Swedish children of both sexes (n = 340). Significant ME differences were observed between the two cohorts for eight of the 11 mycotoxins. Additionally, intra-cohort ME variability turned out to be very high with interquartile ranges (IQR) above 15% for 14 out of 22 analyte-cohort combinations. Maximum IQR values were observed for sterigmatocystin in the Bangladeshi cohort (318%), strongly impacting quantitative results obtained with matrix(-matched) calibration. Further experiments on a small German cohort of four subjects, each providing four to five urine samples, revealed high variability of ME within each individual. Factors influencing ME were investigated, showing little to no impact of sex and a moderate impact of age for some analytes in the Swedish cohort. Nonetheless, especially the more polar analytes, showing stronger signal suppression, demonstrated clear correlation of ME with density and creatinine concentration of the urine samples. As a result, urine samples with very high or low density or creatinine values require careful handling in regard to sensitivity or quantification errors when matrix(-matched) calibration without SIL-IS is applied. Full article
(This article belongs to the Special Issue Advanced Analytical Technologies in Mycotoxins Detection for the One Health)
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