Data-Driven Soft Independent Modeling of Class Analogy in Paper Spray Ionization Mass Spectrometry-Based Metabolomics for Rapid Detection of Prostate Cancer.

Sensitive, rapid, and meaningful diagnostic tools for prostate cancer (PC) screening are urgently needed. Paper spray ionization mass spectrometry (PSI-MS) is an emerging rapid technology for detecting biomarker and disease diagnoses. Due to lack of chromatography and difficulties in employing tandem MS, PSI-MS-based untargeted metabolomics often suffers from increased ion suppression and subsequent feature detection, affecting chemometric methods for disease classification. This study first evaluated the data-driven soft independent modeling of class analogy (DD-SIMCA) model to analyze PSI-MS-based global metabolomics of a urine data matrix to classify PC. The efficiency of DD-SIMCA was analyzed based on the sensitivity and specificity parameters that showed 100% correct classification of the training set, based on only PC and test set samples, based on normal and PC. This analytical methodology is easy to interpret and efficient and does not require any prior information from the healthy individual. This new application of DD-SIMCA in PSI-MS-based metabolomics for PC disease classification could also be extended to other diseases and opens a rapid strategy to discriminate against health problems.

Analysis of 1H NMR spectra of diesel and crambe biodiesel mixtures using chemometrics tools to evaluate the authenticity of a Brazilian standard biodiesel blend.

A methodology was developed to monitor the content of crambe biodiesel in mixtures with conventional diesel using hydrogen nuclear magnetic resonance (1H NMR) spectroscopy combined with the orthogonal projections on the latent structure-discrimination analysis (OPLS-DA). The efficiency of the developed OPLS-DA model was analyzed based on the criteria of true response statistics: false positive and false negative rate, sensitivity, specificity, efficiency and Matthew's correlation coefficient, where the sensitivity (true positive rate) and specificity (true negative rate) were both equal to 1 and the false positive and false negative rates were both equal to 0, which means that all samples to be predicted as belonging to the diesel class of interest, B10 (containing 10% biodiesel and 90% pure diesel), were predicted in class 1, and all samples to be considered as belonging to the diesel class, not of interest, BX (biodiesel content less and greater than in B10), were predicted in class 0. These results showed 100% correct classification of the training and test set samples for B10 and BX, demonstrating a high efficiency of the OPLS-DA model in the monitoring of crambe methyl biodiesel content when mixed with diesel in various proportions. The excellent results in the application of this model suggest that this analytical methodology is feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.