The detection of Alternaria mycotoxins in food is a growing concern due to their potential health effects. Although HPLC-MS-based techniques are the most widely used for the accurate determination of mycotoxins, they are not suitable for rapid on-site screening, which emphasises the need for portable, cost-effective and environmentally friendly analytical instruments. In this study, we developed a first-line electroanalytical platform for the rapid detection of alternariol (AOH) and alternariol-9-methyl ether (AME) using screen-printed gold electrodes modified with dodecanethiol self-assembled monolayers (SAMs). Dodecanethiol SAM deposition was optimized using a 22 full factorial design to enhance and differentiate the electrochemical responses of each of the two toxins with even minimal structural differences, as confirmed by cyclic voltammetry and impedance spectroscopy. Good results with LOD and LOQ at the low µg/kg level were achieved, approaching the indicative levels recommended for the target compounds in processed tomato products. Very good inter-electrode repeatability with RSD <10% was also achieved. The integration of Machine Learning (ML) techniques into the analytical workflow was successfully evaluated to enable predictive modelling of contamination events using the dual-channel electroanalytical platform. The method requires minimal sample treatment, produces negligible waste, and is compatible with low-power portable devices. Finally, AGREE, ComplexMoGAPI and SPMS metrics were used to assess the greenness, proving excellent compliance with the principles of green analytical chemistry. For the first time we demonstrated the potential of the integration of ML into an environmentally friendly electrochemical platform to enable a cost-effective primary screening for these representative Alternaria toxins.
First-line electroanalytical screening platform for rapid detection of Alternaria toxins in fresh tomatoes based on chemically-modified screen-printed gold electrodes and Machine Learning predictive models / Fortunati, Simone; Maffezzoni, Cristian; Giliberti, Chiara; Bianchi, Valentina; De Munari, Ilaria; Careri, Maria; Giannetto, Marco. - In: GREEN ANALYTICAL CHEMISTRY. - ISSN 2772-5774. - 17:(2026). [10.1016/j.greeac.2026.100332]
First-line electroanalytical screening platform for rapid detection of Alternaria toxins in fresh tomatoes based on chemically-modified screen-printed gold electrodes and Machine Learning predictive models
Fortunati, Simone;Maffezzoni, Cristian;Giliberti, Chiara;Bianchi, Valentina;De Munari, Ilaria;Careri, Maria;Giannetto, Marco
2026-01-01
Abstract
The detection of Alternaria mycotoxins in food is a growing concern due to their potential health effects. Although HPLC-MS-based techniques are the most widely used for the accurate determination of mycotoxins, they are not suitable for rapid on-site screening, which emphasises the need for portable, cost-effective and environmentally friendly analytical instruments. In this study, we developed a first-line electroanalytical platform for the rapid detection of alternariol (AOH) and alternariol-9-methyl ether (AME) using screen-printed gold electrodes modified with dodecanethiol self-assembled monolayers (SAMs). Dodecanethiol SAM deposition was optimized using a 22 full factorial design to enhance and differentiate the electrochemical responses of each of the two toxins with even minimal structural differences, as confirmed by cyclic voltammetry and impedance spectroscopy. Good results with LOD and LOQ at the low µg/kg level were achieved, approaching the indicative levels recommended for the target compounds in processed tomato products. Very good inter-electrode repeatability with RSD <10% was also achieved. The integration of Machine Learning (ML) techniques into the analytical workflow was successfully evaluated to enable predictive modelling of contamination events using the dual-channel electroanalytical platform. The method requires minimal sample treatment, produces negligible waste, and is compatible with low-power portable devices. Finally, AGREE, ComplexMoGAPI and SPMS metrics were used to assess the greenness, proving excellent compliance with the principles of green analytical chemistry. For the first time we demonstrated the potential of the integration of ML into an environmentally friendly electrochemical platform to enable a cost-effective primary screening for these representative Alternaria toxins.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
