TRALSPEC-AI |Development and validation of a novel method for the determination of Tropane Alkaloids in Food and Food Products

TRALSPEC-AI |食品和食品中托烷生物碱测定新方法的开发和验证

• 批准号: EP/X021610/1
• 负责人: RUFIELYN GRAVADOR
• 金额: $ 24.26万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX021610%2F1
• 关键词: TRALSPEC; AI; Development; validation; novel

Datura spp. is rich in tropane alkaloids (TAs), plant secondary compounds produced in response to environmental stressors. The entire plant is toxic and now invade crops such as maise, millet, amaranth, buckwheat, flax/linseed, sunflowers, sorghum, and soybeans. Recently, many people in Uganda suffered from severe food poisoning illness and fatality due to the consumption of Super Cereal contaminated by TAs. The gold standard methods for detecting and quantifying TAs in foods are time-consuming, laborious, expensive. They require a high level of expertise-- implying that stakeholders, such as the crop producers, cannot use these in quality control of their commodities. Our proposal aims to answer the question: can TA contamination that can poison the consumers be detected and thus prevented using vibrational spectroscopy coupled with data analytics to give accurate and real-time measurements? To answer this question, we aim to develop and validate a novel approach to gold standard methods in quantifying TAs in foods, therefore alerting for toxicity. The methods will be based on validated vibrational spectroscopic (infrared) techniques. Foods with a wide range of TA concentrations will be obtained from the European Union Reference Laboratories. They will be scanned using a benchtop and portable IR instrument. They will use Artificial intelligence (Machine Learning) to analyse the large volume of spectral data, resulting in predictive modelling that will detect concentrations of TAs in scanned foods. The model will be imported into the benchtop and portable IR instruments, thus in a way, automating the TA analysis to give real-time measurements. This is user-friendly and can be used at any stage along the food supply chain. We will compare the results obtained from this novel approach with the gold standard methods (Gas & Liquid chromatography coupled with high-resolution mass spectrometry (MS) or MS/MS).

曼陀罗属富含托烷生物碱 (TA)，这是植物响应环境压力而产生的次级化合物。整个植物有毒，现已侵入玉米、小米、苋菜、荞麦、亚麻/亚麻籽、向日葵、高粱和大豆等作物。最近，乌干达许多人因食用被TA污染的超级谷物而出现严重食物中毒并死亡。检测和定量食品中 TA 的金标准方法耗时、费力且昂贵。它们需要高水平的专业知识——这意味着作物生产者等利益相关者无法利用这些知识来控制其商品的质量。我们的提案旨在回答这样一个问题：使用振动光谱与数据分析相结合来提供准确和实时的测量，是否可以检测到可能毒害消费者的 TA 污染，从而防止这种污染？为了回答这个问题，我们的目标是开发和验证一种新的黄金标准方法，用于量化食品中的 TA，从而发出毒性警报。这些方法将基于经过验证的振动光谱（红外）技术。 TA 浓度范围广泛的食品将从欧盟参考实验室获得。将使用台式和便携式红外仪器对它们进行扫描。他们将使用人工智能（机器学习）来分析大量光谱数据，从而建立预测模型来检测扫描食品中 TA 的浓度。该模型将被导入台式和便携式红外仪器中，从而在某种程度上自动化 TA 分析以提供实时测量。这是用户友好的，可以在食品供应链的任何阶段使用。我们会将这种新颖方法获得的结果与金标准方法（气相色谱和液相色谱结合高分辨率质谱 (MS) 或 MS/MS）进行比较。