College of Food Science and Nutritional Engineering, China Agriculture University, Beijing 10083, China.
Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Clayton, VIC 3800, Australia.
Toxins (Basel). 2018 Feb 6;10(2):71. doi: 10.3390/toxins10020071.
Ochratoxin A (OTA) contamination in grape production is an important problem worldwide. Microbial volatile organic compounds (MVOCs) have been demonstrated as useful tools to identify different toxigenic strains. In this study, strains were classified into two groups, moderate toxigenic strains (MT) and high toxigenic strains (HT), according to OTA-forming ability. The MVOCs were analyzed by GC-MS and the data processing was based on untargeted profiling using XCMS Online software. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed using extract ion chromatogram GC-MS datasets. For contrast, quantitative analysis was also performed. Results demonstrated that the performance of the OPLS-DA model of untargeted profiling was better than the quantitative method. Potential markers were successfully discovered by variable importance on projection (VIP) and -test. ()-2-octen-1-ol, octanal, 1-octen-3-one, styrene, limonene, methyl-2-phenylacetate and 3 unknown compounds were selected as potential markers for the MT group. Cuparene, ()-thujopsene, methyl octanoate and 1 unknown compound were identified as potential markers for the HT groups. Finally, the selected markers were used to construct a supported vector machine classification (SVM-C) model to check classification ability. The models showed good performance with the accuracy of cross-validation and test prediction of 87.93% and 92.00%, respectively.
OTA 在葡萄生产中的污染是一个全球性的重要问题。微生物挥发性有机化合物(MVOCs)已被证明是识别不同产毒菌株的有用工具。在这项研究中,根据 OTA 形成能力将菌株分为中度产毒菌株(MT)和高度产毒菌株(HT)两组。使用 GC-MS 分析 MVOCs,并基于 XCMS Online 软件的无靶标分析进行数据处理。使用提取离子色谱 GC-MS 数据集进行正交投影判别分析(OPLS-DA)。作为对比,还进行了定量分析。结果表明,无靶标分析的 OPLS-DA 模型的性能优于定量方法。通过变量重要性投影(VIP)和 t 检验成功发现了潜在的标志物。()-2-辛烯-1-醇、辛醛、1-辛烯-3-酮、苯乙烯、柠檬烯、甲基-2-苯基乙酸酯和 3 种未知化合物被选为 MT 组的潜在标志物。 Cuparene、()-thujopsene、甲基辛酸酯和 1 种未知化合物被鉴定为 HT 组的潜在标志物。最后,选择的标志物用于构建支持向量机分类(SVM-C)模型以检查分类能力。模型显示出良好的性能,交叉验证的准确率和测试预测准确率分别为 87.93%和 92.00%。