School of Food and Biological Engineering, Jiangsu University, No. 301 Xuefu Road, Jiangsu 212013, PR China.
School of Food and Biological Engineering, Jiangsu University, No. 301 Xuefu Road, Jiangsu 212013, PR China.
Food Chem. 2024 Jan 1;430:137048. doi: 10.1016/j.foodchem.2023.137048. Epub 2023 Aug 1.
A novel method was developed for the early detection of wheat infected with Aspergillus flavus (A. flavus) using a nanocomposite colorimetric sensors array (CSA). LC-MS analysis revealed the presence of Aflatoxin B1 (AFB1) and Aflatoxin B2 (AFB2) on day seven, demonstrating mycotoxin variabilities in infected wheat. HS-SPME-GC-MS analysis detected 2-methylbutyral, a gas exclusively associated with toxigenic A. flavus. The CSA was modified using three nanoparticles of MOF and successfully used to detect the wheat infected with A. flavus. Discrimination of different types of infected wheat samples was achieved using the RGB difference map and Principal Component Analysis (PCA) model. Additionally, the Linear Discriminant Analysis (LDA) model accurately predicted the presence of toxigenic A. flavus at various stages of infection. These findings highlight the promising capabilities of nanocomposite CSA for early-stage detection of A. flavus infection in wheat.
一种使用纳米复合比色传感器阵列(CSA)对感染黄曲霉(A. flavus)的小麦进行早期检测的新方法已经开发出来。LC-MS 分析表明,在第 7 天存在黄曲霉毒素 B1(AFB1)和黄曲霉毒素 B2(AFB2),证明了感染小麦中霉菌毒素的可变性。HS-SPME-GC-MS 分析检测到 2-甲基丁酸,这是一种仅与产毒黄曲霉(A. flavus)相关的气体。CSA 经过三种 MOF 纳米粒子的修饰,并成功用于检测感染黄曲霉的小麦。使用 RGB 差异图和主成分分析(PCA)模型实现了对不同类型感染小麦样品的区分。此外,线性判别分析(LDA)模型准确预测了在不同感染阶段产毒黄曲霉(A. flavus)的存在。这些发现突出了纳米复合 CSA 对小麦中黄曲霉(A. flavus)感染早期检测的有前景的能力。