{"id":11826,"date":"2026-04-20T07:22:45","date_gmt":"2026-04-20T07:22:45","guid":{"rendered":"https:\/\/www.proefschriftmaken.nl\/portfolio\/yimin-zhang\/"},"modified":"2026-04-20T07:22:52","modified_gmt":"2026-04-20T07:22:52","slug":"yimin-zhang","status":"publish","type":"us_portfolio","link":"https:\/\/www.proefschriftmaken.nl\/en\/portfolio\/yimin-zhang\/","title":{"rendered":"Yimin Zhang"},"content":{"rendered":"","protected":true},"excerpt":{"rendered":"","protected":true},"author":7,"featured_media":11827,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"us_portfolio_category":[45],"class_list":["post-11826","us_portfolio","type-us_portfolio","status-publish","post-password-required","hentry","us_portfolio_category-new-template"],"acf":{"naam_van_het_proefschift":"Technical and Economic Aspects of Alternaria Mycotoxin Management in Tomatoes in China","samenvatting":"De tomaat (Solanum lycopersicum) is een landbouwkundig en economisch belangrijk gewas dat wereldwijd extensief wordt geteeld en geconsumeerd. China is de grootste producent van tomaten ter wereld en een belangrijke exporteur van verse en verwerkte tomaten. Het hoge vocht- en nutri\u00ebntengehalte van tomaten maakt ze vatbaar voor schimmelinfecties, in het bijzonder die veroorzaakt door Alternaria-soorten, wat leidt tot opbrengstverliezen van 10% tot 40% in China. Alternaria-soorten produceren toxische secundaire metabolieten die bekend staan als Alternaria-mycotoxinen, waarvan AOH, AME en TeA het meest karakteristiek en frequent worden gedetecteerd, met name in tomaten en op tomaten gebaseerde voedingsmiddelen. Deze Alternaria-mycotoxinen, door de Europese Autoriteit voor Voedselveiligheid (EFSA) ge\u00efdentificeerd als opkomende gevaren voor de volksgezondheid vanwege hun potenti\u00eble genotoxische en cytotoxische eigenschappen, onderstrepen de noodzaak van effectieve beheersing om de gezondheid van de consument te beschermen. Omdat Alternaria-mycotoxinen chemisch stabiel zijn en moeilijk te verwijderen tijdens de verwerking, richten de huidige beheersmaatregelen zich voornelijk op interventies v\u00f3\u00f3r de oogst. Beslissingsondersteunende systemen (DSS) zijn een krachtig hulpmiddel om beheersacties te sturen door tijdige informatie te verstrekken. Bestaande DSS hebben zich primair gericht op het voorspellen van de ernst van ziekten in plaats van de werkelijke mycotoxineverontreiniging. Voorspellingsmodellen kunnen dienen als een DSS om beheersacties te sturen door het voorkomen van mycotoxinen en de bijbehorende risico's te voorspellen.\n\nEen voorspellingsmodel dat zich specifiek richt op Alternaria-mycotoxinen zou nauwkeurige voorspellingen van verontreiniging kunnen bieden, wat op feiten gebaseerde beheersstrategie\u00ebn vergemakkelijkt. Het algemene doel van dit proefschrift was het ontwerpen van een kwantitatief kader om de beheersing van Alternaria-mycotoxineverontreiniging in tomaten in China te verbeteren door chemische analyse, voorspellende modellering en sociaaleconomische analyse te integreren.\n\nHoofdstuk 2 vatte de actuele kennis over Alternaria-mycotoxinen in voeding en diervoeder tussen 2011 en 2024 samen. De resultaten identificeerden tomaten en graanproducten als de meest frequent verontreinigde voedingsmiddelen. AOH en AME werden consequent in verband gebracht met cytotoxiciteit en genotoxiciteit. De huidige beheersaanpak richt zich primair op het gebruik van fungiciden v\u00f3\u00f3r de oogst, maar dit kan leiden tot overmatig gebruik en schade aan de ecologische duurzaamheid.\n\nHoofdstuk 3 presenteerde de ontwikkeling van een op QuEChERS gebaseerde LC-MS\/MS-methode om Alternaria-mycotoxinen in tomatenproducten te kwantificeren. Deze methode is geoptimaliseerd voor kleine monsterhoeveelheden (100 mg). De resultaten toonden aan dat alle monsters uit de markt verontreinigd waren met ten minste \u00e9\u00e9n Alternaria-mycotoxine. De dieetblootstellingsbeoordeling onthulde dat kinderen een groter risico liepen dan volwassenen, waarbij AME het meest bijdroeg aan de totale blootstelling.\n\nHoofdstuk 4 beschreef de ontwikkeling van een voorspellingsmodel (AltoSafe) op basis van het Random Forest-algoritme. Het model vertoonde sterke voorspellende prestaties voor AOH, AME en TeA. Gevoeligheidsanalyse gaf aan dat het bloeistadium een optimaal moment is voor effectieve fungicide-toepassing. Dit was het eerste voorspellingsmodel dat expliciet werd ontwikkeld voor Alternaria-mycotoxinen in de tomatenproductie.\n\nHoofdstuk 5 richtte zich op de kosteneffectiviteit van beheersaanpakken. Uit de analyse bleek dat een risicogebaseerde aanpak gestuurd door een voorspellingsmodel in het bloeistadium (S3) consequent beter presteerde dan een vast schema. Deze aanpak zorgde voor de hoogste nettowinst en een afname van meer dan 75% in disability-adjusted life years (DALYs). Deze bevindingen benadrukken het potentieel van voorspellende modellen om het risicobeheer van mycotoxinen op een duurzame en kosteneffectieve manier te verbeteren.","summary":"Tomato (Solanum lycopersicum) is an agriculturally and economically important crop, cultivated extensively and consumed worldwide. China is the world's largest producer of tomatoes and a major exporter of fresh and processed tomatoes. The high moisture and nutrient content of tomatoes make them susceptible to fungal infections, particularly those caused by Alternaria species, resulting in yield losses of 10% to 40% in China. Alternaria species generate toxic secondary metabolites known as Alternaria mycotoxins, with AOH, AME, and TeA being the most characteristic and frequently detected, particularly in tomatoes and tomato-derived foods. These Alternaria mycotoxins, identified by the European Food Safety Authority (EFSA) as emerging public health hazards due to their potential genotoxic and cytotoxic properties, underscore the need for effective control to safeguard consumer health. Because Alternaria mycotoxins are chemically stable and difficult to remove during processing, current control measures focus mainly on pre-harvest interventions. Applying fixed-schedule fungicides is the standard method for controlling Alternaria mycotoxin contamination in pre-harvest tomato cultivation. These routine sprays lead to inefficient and excessive fungicide use, resulting in increased environmental contamination and higher labour and chemical costs. Decision-support systems (DSS) are a powerful tool to guide control actions by providing timely information. The existing DSS has primarily focused on predicting Alternaria-induced disease severity rather than assessing the actual level of mycotoxin contamination. The effectiveness of such control measures remains uncertain because disease severity does not reliably indicate the actual level of mycotoxin contamination, highlighting a critical need for a reliable and targeted approach to assess Alternaria mycotoxin risks. Mycotoxin prediction models can serve as a DSS to guide control actions because they can forecast the occurrence of mycotoxins and associated risks.\n\nA prediction model specifically targeting Alternaria mycotoxins could provide accurate forecasts of contamination, facilitating evidence-based control strategies. Assessing the cost effectiveness of this prediction-model-guided control strategy in tomato production can provide deeper insight into the model\u2019s utility and support its broader implementation. The overall objective of this thesis was to design a quantitative framework to improve the control of Alternaria mycotoxin contamination in tomatoes in China by integrating chemical analysis, predictive modelling, and economic analysis.\n\nChapter 2 summarized the review results of the state-of-the-art knowledge on Alternaria mycotoxins in food and feed between 2011 and 2024. A systematic review of peer-reviewed literature from Scopus and PubMed focused on four aspects of Alternaria mycotoxins: occurrence, toxicity, dietary exposure, and control measurements. The results identified tomatoes and cereal-based products as the most frequently contaminated foods. Among all reviewed Alternaria mycotoxins, AOH and AME were consistently linked to cytotoxicity and genotoxicity. Although TeA does not exhibit genotoxic effects, it is commonly detected at high concentrations, notably in products consumed by infants and children. All three Alternaria mycotoxins have been shown to present a food safety risk. Current control approaches primarily focus on using fungicides to reduce Alternaria mycotoxin contamination in pre-harvest. However, current control methods could lead to fungicide overuse and harm environmental sustainability. Combining a predictive with the current control strategies could mitigate fungicide overuse and support more targeted spraying. Nevertheless, this mycotoxin predictive modelling approach has focused predominantly on cereals and has been rarely implemented for vegetables and fruits.\n\nChapter 3 presented the development and validation results of a QuEChERS-based LC-MS\/MS method to quantify multiple Alternaria mycotoxins in tomato products. This analytical method was optimized for small sample sizes (100 mg) while maintaining high sensitivity and precision. The method demonstrated good analytical performance, achieving linearity with R2 values exceeding 0.99 for all analytes, limits of detection ranging from 0.01 to 0.10 \u03bcg\/kg, and mean recoveries exceeding 80%. Precision was confirmed with relative standard deviations (RSDs) below 10% across all spiking levels. This validated analytical method was applied to 43 market samples, and contamination results were further used to support the deterministic dietary exposure assessment. The analytical results revealed that all samples were contaminated with at least one Alternaria mycotoxin, with maximum concentrations of 11.4 \u03bcg\/kg for AOH, 50.3 \u03bcg\/kg for AME, and 445.6 \u03bcg\/kg for TeA. The deterministic dietary exposure assessment revealed that children were at a greater risk than adults, primarily due to their lower body weight, with AME contributing the most to total exposure. TeA and TEN did not pose a health risk to either adults or children under assessed dietary conditions.\n\nChapter 4 described the development of a predictive model based on the Random Forest algorithm, integrating weather conditions, agronomic practices, and outputs from a fungal infection simulation model to forecast the Alternaria mycotoxin contamination in open-field tomato production. The model demonstrated strong predictive performance for AOH, AME, and TeA (internal validation accuracy > 97%; external validation accuracy > 70%). Sensitivity analysis indicated that prediction accuracy depends on the tomato growth stage, identifying the flowering stage as an optimal period for effective fungicide application. Feature importance analysis identified the fungal infection simulation model output as the top predictor, followed by wind speed and temperature. This represented the first predictive model explicitly developed for Alternaria mycotoxins in tomato production, linking environmental drivers to fungal biology and informing risk-based control approaches.\n\nChapter 5 focused on the cost-effectiveness of three pre-harvest control approaches for reducing Alternaria mycotoxin contamination in open-field tomato production, based on a simulated case study in Guangdong Province, China: (i) fixed-schedule fungicide application (S1), (ii) fixed-schedule fungicide application supplemented with harvest-stage predictions (S2), and (iii) a flowering-stage prediction model integrated with risk-based fungicide use (S3). Our analysis revealed that S3 consistently outperformed S1 and S2. S3 provided the highest net benefit, the lowest costs, and a large reduction in health burdens, resulting in a decrease of over 75% in disability-adjusted life years (DALYs) compared to S1. These findings highlight the potential of predictive models to improve mycotoxin risk management in a cost-effective, health-protective, and environmentally sustainable manner, delivering valuable insights for stakeholders across the food production chain.","auteur":"Yimin Zhang","auteur_slug":"yimin-zhang","publicatiedatum":"27 mei 2026","taal":"EN","url_flipbook":"https:\/\/ebook.proefschriftmaken.nl\/ebook\/yiminzhang?iframe=true","url_download_pdf":"https:\/\/ebook.proefschriftmaken.nl\/download\/d7603dbf-9076-42e4-8a04-344cde0529bb\/optimized","url_epub":"","ordernummer":"18789","isbn":"","doi_nummer":"","naam_universiteit":"Wageningen University","afbeeldingen":11828,"naam_student:":"","binnenwerk":"","universiteit":"Wageningen University","cover":"","afwerking":"","cover_afwerking":"","design":""},"_links":{"self":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/11826","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio"}],"about":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/types\/us_portfolio"}],"author":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/comments?post=11826"}],"version-history":[{"count":1,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/11826\/revisions"}],"predecessor-version":[{"id":11829,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/11826\/revisions\/11829"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media\/11827"}],"wp:attachment":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media?parent=11826"}],"wp:term":[{"taxonomy":"us_portfolio_category","embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio_category?post=11826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}