Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department Of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA.
Talanta. 2011 Jan 30;83(4):1260-8. doi: 10.1016/j.talanta.2010.05.063. Epub 2010 Jun 8.
A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography (GC) has been used with conventional mass spectrometry (MS) to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system (FuRES) classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.8 ± 0.5% accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares (o-PLS) regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution (PDR) method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes.
一种能够快速分类未知喷气燃料类型或检测喷气燃料物理性质可能发生变化的方法，对国防和航空公司都具有至关重要的意义。虽然快速气相色谱（GC）已经与传统的质谱（MS）一起用于研究喷气燃料，但快速 GC 首次与快速扫描 MS 结合使用，通过模糊规则生成专家系统（FuRES）分类器将喷气燃料分类为批次号或来源。在建立分类器的过程中，数据分别进行了小波变换和未进行小波变换预处理，并对性能进行了评估。主成分变换用于在分类之前压缩双向数据图像。Jet fuel 样品的分类准确率高达 99.8±0.5%，小波压缩前后均取得了成功。使用 10 个引导拉丁分区对广义预测精度进行验证。优化的偏最小二乘（o-PLS）回归结果用作比较 FuRES 预测结果的正偏参考。对两种方法得到的喷气燃料样品的预测结果进行了统计学比较。还使用投影差异分辨率（PDR）方法对快速 GC 和快速 MS 数据进行了评估。制备了两批 10 个新样品的等分试样，相隔 4 天独立运行，以评估方法的稳健性。分类参数的唯一变化是使用多项式保留时间对齐来校正在两次采集的 4 天跨度内发生的漂移。FuRES 对四个未压缩三向数据模型实现了完美的分类。这种快速 GC/快速 MS 方法具有速度快、精度高和稳健性强的特点。这种测量方式可能可用作监测工具，以跟踪燃料化学成分的变化，这也可能导致燃料性能发生变化。
