Chemometric Analysis Combined with GC × GC-FID and ESI HR-MS to Evaluate Ultralow-Sulfur Diesel Stability.

Diesel has been the most employed fuel in highway and nonhighway transportation systems. Many studies over the past years have attempted to classify diesel as a stable or unstable composition since this fuel can still degrade during storage or thermal oxidative processes. Products generated because of such degradation are the reason for the formation of soluble gums and insoluble organic particulates, which in turn cause a negative influence on engine performance. This work reports a detailed composition of nonpolar and polar compounds in many ultralow-sulfur diesel (ULSD) samples by comprehensive two-dimensional gas chromatography with a flame ionization detector (GC × GC-FID) and electrospray ionization high-resolution mass spectrometry (ESI HR-MS). In addition, chemometric approaches were applied for ULSD storage stability investigation. GC × GC-FID experiments achieved the nonpolar chemical characterization for the ULSD samples, including all main hydrocarbon classes: paraffins, mono- and dinaphthenics and olefins, and aromatics. The GC × GC-FID data combined with principal component analysis (PCA) described that the separation of the samples' concerning storage stability was mainly due to the contents of mono- and diaromatic compounds in the unstable ULSD samples. Moreover, PCA was also applied to the ESI (±) data set, and the results highlight the presence of compounds belonging to O class (natural antioxidants), which decrease the rate of oxygen consumption in the fuel, characterizing it as stable composition. The basic nitrogen compounds are mostly present in the stable ULSD samples indicating that they did not affect the stability of the fuel. On the other hand, the HC classes presented pronounced abundance among unstable ULSD samples suggesting that the fuel degradation may go through the oxidation of hydrocarbons and the formation of Ox compounds as byproducts. Furthermore, MS/MS experiments point to the formation of CcHhNnOo-like precursor species, which can react with each other and lead to the formation of gums and insoluble sediments in the fuel. In summary, the results express the potential of using the GC × GC-FID and ESI (±) Orbitrap MS techniques as valuable tools for diesel stability evaluations.

Exploring salivary lipid profile changes in COVID-19 patients: Insights from mass spectrometry analysis.

The most common COVID-19 testing relies on the use of nasopharyngeal swabs. However, this sampling step is very uncomfortable and is one of the biggest challenges regarding population testing. In the present study, the use of saliva as an alternative sample for COVID-19 diagnosis was investigated. Therefore, high-resolution mass spectrometry analysis and chemometric approaches were applied to salivary lipid extracts. Two data organizations were used: classical MS data and pseudo-MS image datasets. The latter transformed MS data into pseudo-images, simplifying data interpretation. Classification models achieved high accuracy, with pseudo-MS image data performing exceptionally well. PLS-DA with OPSDA successfully separated COVID-19 and healthy groups, serving as a potential diagnostic tool. The most important lipids for COVID-19 classification were elucidated and include sphingolipids, ceramides, phospholipids, and glycerolipids. These lipids play a crucial role in viral replication and the inflammatory response. While pseudo-MS image data excelled in classification, it lacked the ability to annotate important variables, which was performed using classical MS data. These findings have the potential to improve clinical diagnosis using rapid, non-invasive testing methods and accurate high-volume results.

Quantitative Evaluation of Non-basic Nitrogen-Containing Compounds in Petroleum-Derived Samples by Direct Injection ESI (-) Orbitrap MS.

The quantification of non-basic nitrogen-containing compounds (NCCs) in petroleum-derived samples has become a critical issue due to the undesirable effects of these compounds on the petroleum industry. In addition, there is a lack of analytical methods that allow the direct quantification of NCCs in these matrices. This paper provides strategies for obtaining quantitative information of NCCs in petroleum-derived samples using direct flow injection electrospray ionization (ESI) (-) Orbitrap mass spectrometry without fractionation steps. Benzocarbazole (BC) quantification was performed using the standard addition method. The method was validated, and all analytical parameters demonstrated satisfactory results in the matrix-mix. Paired Student's t-test exhibited the matrix effect (95% confidence level, p < 0.05). Limits of detection ranged from 2.94 to 14.91 µg L-1, and the limits of quantification ranged from 9.81 to 49.69 µg L-1. Intraday and interday accuracy and precision were not above 15%. Quantification of non-basic NCCs was carried out based on two approaches. In approach 1, the non-basic NCCs' total content in petroleum-derived samples was determined by the BC concentration and the total abundance correction. The method presented good performance with the average error of 21, 8.3, and 28% for crude oil, gas oil, and diesel samples, respectively. Approach 2 was based on the multiple linear regression model with regression significant at a 0.05 significance level within average relative errors of 16, 7.8, and 17% for the crude oil, gas oil, and diesel samples, respectively. Then, both approaches successfully predicted the quantification of non-basic NCCs by ESI direct flow injection.

LC-HRMS/MS-Based Metabolomics Approaches Applied to the Detection of Antifungal Compounds and a Metabolic Dynamic Assessment of Orchidaceae.

The liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach is a powerful technology for discovering novel biologically active molecules. In this study, we investigated the metabolic profiling of Orchidaceae species using LC-HRMS/MS data combined with chemometric methods and dereplication tools to discover antifungal compounds. We analyze twenty ethanolic plant extracts from Vanda and Cattleya (Orchidaceae) genera. Molecular networking and chemometric methods were used to discriminate ions that differentiate healthy and fungal-infected plant samples. Fifty-three metabolites were rapidly annotated through spectral library matching and in silico fragmentation tools. The metabolomic profiling showed a large production of polyphenols, including flavonoids, phenolic acids, chromones, stilbenoids, and tannins, which varied in relative abundance across species. Considering the presence and abundance of metabolites in both groups of samples, we can infer that these constituents are associated with biochemical responses to microbial attacks. In addition, we evaluated the metabolic dynamic through the synthesis of stilbenoids in fungal-infected plants. The tricin derivative flavonoid- and the loliolide terpenoidfound only in healthy plant samples, are promising antifungal metabolites. LC-HRMS/MS, combined with state-of-the-art tools, proved to be a rapid and reliable technique for fingerprinting medicinal plants and discovering new hits and leads.

Predicting oil content in ripe Macaw fruits (Acrocomia aculeata) from unripe ones by near infrared spectroscopy and PLS regression.

A method for early quantification of unripe macaw fruits oil content using near-infrared spectroscopy (NIR) and partial least squares (PLS) is presented. After harvest, the fruit takes about 30 days to reach its maximum oil accumulation. The oil content was quantified thirty days after harvest using Soxhlet extraction. PLS models were built using NIR spectra of shell obtained five days after harvest (Shell5). The Shell5 model was compared with models built using NIR spectra of the shell (Shell30) and mesocarp thirty days after harvest (Pulp30). Ordered predictors selection was used to select the most informative variables. The best models presented root mean square error of prediction and correlation coefficient of prediction of 4.87% and 0.89 for Shell5; 5.83% and 0.85 for Shell30; 4.76% and 0.92 for Pulp30. Thus, the anticipated prediction of oil content could reduce the time and costs of macaw palm quality control and storage.

Comprehensive new approaches for variable selection using ordered predictors selection.

New strategies of ordered predictors selection (OPS) were developed in this work, making this method more versatile and expanding its worldwide use and applicability. OPS is a recognized method to select variables in multivariate regression and is used by analytical chemists and chemometrists. It shows high ability to improve the prediction of models after the selection of a few and important variables. At the core of OPS is sorting variables from informative vectors and systematically investigating the regression models to identify the most relevant set of variables by comparing the cross-validation parameters of the models. Nevertheless, the first version of the OPS method performs variable selection using only one informative vector at a time and is limited to just one variable selection run. Then, three new strategies were proposed. First, an automatic method was developed to perform variable selection using several informative vectors and their combinations. Second, the feedback OPS is presented, in this new strategy the pre-selected variables would return to a new selection. Last, a method to apply OPS in full array subdivisions called OPS intervals was established. Initially, the new strategies were applied in the six datasets used in the original OPS paper to compare the prediction performance with the new OPS algorithms. After that, twelve new datasets were used to test and compare the new OPS approaches with other variable selection methods, genetic algorithm (GA), the interval successive projections algorithm for PLS (iSPA), and recursive weighted partial least squares (rPLS). The new OPS approaches outperformed the first OPS version and the other variable selection methods. Results showed that in addition to greater predictive capacity, the accuracy in the selection of expected variables is highly superior with the new OPS approaches. Overall, the new OPS provided the best set of selected variables to build more predictive and interpretative regression models, proving to be efficient for variable selection in different types of datasets.

Early prediction of sugarcane genotypes susceptible and resistant to Diatraea saccharalis using spectroscopies and classification techniques.

The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life.

Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods.

Near-infrared (NIR) spectroscopy and chemometric methods were used to predict the chemical properties of decomposing eucalyptus harvest residues to better understand the decomposition process of these materials. Leaves, twigs, branches, and bark from a decomposition experimental set up in commercial plantations were sampled for one year. The contents of carbon (C), nitrogen (N), extractives (EX), acid-soluble lignin (SL), Klason insoluble lignin (KL) and holocellulose (HC) were determined by the reference method in the collected samples. Principal component analysis (PCA) was employed to distinguish the types of harvest residues throughout the decomposition period. Multi-residue regression models were built from the NIR spectra using partial least squares regression (PLS). Two feature selection methods, i.e., ordered predictors selection (OPS) and genetic algorithm (GA), were applied and compared. The OPS and GA did not differ statistically; however, compared with the GA, OPS was more computationally efficient and selected fewer variables. Using the PLS-OPS models, the root mean square errors of prediction (RMSEP) for C, N, EX, SL, KL and HC were 19.70, 0.08, 0.74, 0.39, 28.13 and 33.99, respectively, and the prediction correlations (Rp) for these properties were 0.94, 0.99, 0.99, 0.99, 0.96 and 0.98, respectively. PLS-discriminant analysis (PLS-DA) was used to classify the samples over the decomposition time and provided a good separation. Some mismatches obtained in the modeled classes were explained by the differences in the decomposition rate and changes in the chemical composition of the different harvest residue components that were evaluated. The results showed the feasibility of NIR spectroscopy and chemometric methods to evaluate the chemistry of decomposing eucalyptus harvest residues, indicating that these methods can be used as rapid and inexpensive alternatives to conventional methods to help understand the decomposition process.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression.

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.