Stafstrom William, Ngure Francis, Mshanga John, Wells Henry, Nelson Rebecca J, Mischler John
School of Integrative Plant Science, Cornell University, Ithaca, NY, United States of America.
Independent Research Consultant, Mycotoxins Mitigation and Child Stunting Research Trial, Arusha Tanzania & Nairobi, Limuru, Kenya.
PLoS One. 2025 Jan 13;20(1):e0316457. doi: 10.1371/journal.pone.0316457. eCollection 2025.
Human exposure to mycotoxins is common and often severe in underregulated maize-based food systems. This study explored how monitoring of these systems could help to identify when and where outbreaks occur and inform potential mitigation efforts. Within a maize smallholder system in Kongwa District, Tanzania, we performed two food surveys of mycotoxin contamination at local grain mills, documenting high levels of aflatoxins and fumonisins in maize destined for human consumption. A farmer questionnaire documented diverse pre-harvest and post-harvest practices among smallholder farmers. We modeled maize aflatoxins and fumonisins as a function of diverse indicators of mycotoxin risk based on survey data, high-resolution geospatial environmental data (normalized difference vegetation index and soil quality), and proximal near-infrared spectroscopy. Interestingly, mixed linear models revealed that all data types explained some portion of variance in aflatoxin and fumonisin concentrations. Including all covariates, 2015 models explained 27.6% and 20.6% of variation in aflatoxin and fumonisin, and 2019 models explained 39.4% and 40.0% of variation in aflatoxin and fumonisin. This study demonstrates the value of using low-cost risk factors to model mycotoxins and provides a framework for designing and implementing mycotoxin monitoring within smallholder settings.
在监管不力的以玉米为基础的食品体系中，人类接触霉菌毒素的情况很常见，而且往往很严重。本研究探讨了对这些体系进行监测如何有助于确定疫情发生的时间和地点，并为潜在的缓解措施提供信息。在坦桑尼亚孔瓜区的一个玉米小农户体系中，我们在当地谷物磨坊对霉菌毒素污染进行了两次食品调查，记录了供人类食用的玉米中黄曲霉毒素和伏马毒素的高含量。一份农民调查问卷记录了小农户在收获前和收获后的各种做法。我们根据调查数据、高分辨率地理空间环境数据（归一化植被指数和土壤质量）以及近红外光谱，将玉米黄曲霉毒素和伏马毒素建模为霉菌毒素风险的各种指标的函数。有趣的是，混合线性模型显示，所有数据类型都解释了黄曲霉毒素和伏马毒素浓度变化的一部分。纳入所有协变量后，2015年的模型解释了黄曲霉毒素和伏马毒素变化的27.6%和20.6%，2019年的模型解释了黄曲霉毒素和伏马毒素变化的39.4%和40.0%。本研究证明了使用低成本风险因素对霉菌毒素进行建模的价值，并为在小农户环境中设计和实施霉菌毒素监测提供了一个框架。
