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

曼陀罗(Datura Spp)富含托烷生物碱(Tas)，是植物在应对环境胁迫时产生的次生化合物。整个植物都是有毒的，现在入侵的作物包括玉米、小米、苋菜、荞麦、亚麻/亚麻籽、向日葵、高粱和大豆。最近，乌干达许多人因食用被Tas污染的超级谷物而遭受严重的食物中毒疾病和死亡。食品中TA的检测和定量的黄金标准方法费时、费力、昂贵。它们需要高水平的专业知识--这意味着利益相关者，如作物生产者，不能将这些用于其商品的质量控制。我们的建议旨在回答这样一个问题：使用振动光谱分析和数据分析相结合的方法，能否检测到可能毒害消费者的TA污染，从而防止其被检测到并提供准确和实时的测量？为了回答这个问题，我们的目标是开发和验证一种新的方法，以黄金标准方法定量食品中的TA，从而警告毒性。这些方法将基于经过验证的振动光谱(红外)技术。具有广泛TA浓度范围的食品将从欧盟参考实验室获得。他们将使用台式和便携式红外仪器进行扫描。他们将使用人工智能(机器学习)来分析大量的光谱数据，从而建立预测模型，检测扫描食品中的TA浓度。该模型将被导入到台式和便携式红外仪器中，从而在某种程度上实现TA分析的自动化，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。我们将把这一新方法的结果与金标准方法(气相色谱与高分辨率质谱联用或MS/MS)进行比较。

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).