Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, USA.
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department of Chemistry and Biochemistry, Ohio University, Athens, OH 45701-2979, USA.
Talanta. 2018 Apr 1;180:229-238. doi: 10.1016/j.talanta.2017.12.032. Epub 2017 Dec 13.
High-resolution mass spectrometry (HRMS) combined with pattern recognition was used to discriminate among twenty-five Cannabis samples, twenty hemp samples, and eight liquor samples. The effects of preprocessing on multivariate data analysis were evaluated for Orbitrap high-resolution mass spectra. Different root transformations were evaluated with respect to the bin width and the average classification rates. In addition, linear binning and proportional binning with various resolving powers were studied with respect to the average classification rates. The proportional binning with the square root transformation gave the best overall performance for chemical profiling or spectral fingerprinting. Six classification methods, fuzzy rule-building expert system (FuRES), linear discriminant analysis (LDA), super partial least squares discriminant analysis (sPLS-DA), support vector machine (SVM), SVM classification tree type gap (SVMTreeG), and SVM classification tree type entropy (SVMTreeH) had similar trends in prediction rate with respect to the resolving power. The optimal proportional mass bin width may depend on the data set, i.e., for the Cannabis samples is resolving power 10, for the hemp samples and the liquor samples are resolving power 10. Hence, data preprocessing methods such as different transformations, binning strategies, and resolving powers are important factors to be optimized for HRMS direct infusion measurements combined with pattern recognition to be an authentication and characterization tool for various products.
高分辨率质谱（HRMS）结合模式识别技术，用于区分 25 个大麻样本、20 个汉麻样本和 8 个酒类样本。评估了不同预处理方法对 Orbitrap 高分辨质谱数据的多变量数据分析的影响。对于不同的根变换，研究了其对分类率的影响。此外，还研究了不同分辨率的线性分箱和比例分箱对平均分类率的影响。对于化学特征分析或光谱指纹分析，平方根变换的比例分箱效果最好。六种分类方法，模糊规则生成专家系统（FuRES）、线性判别分析（LDA）、超偏最小二乘判别分析（sPLS-DA）、支持向量机（SVM）、SVM 分类树型间隙（SVMTreeG）和 SVM 分类树型熵（SVMTreeH），在分辨率方面，其预测率的趋势相似。最佳比例质量分箱宽度可能取决于数据集，即大麻样本的分辨率为 10，汉麻样本和酒类样本的分辨率为 10。因此，数据预处理方法，如不同的变换、分箱策略和分辨率，是 HRMS 直接进样测量与模式识别相结合的重要因素，是各种产品的鉴别和特征分析工具。
