ESI(-)FT-ICR MS for the determination of best conditions for producing extract abundant in phenolic compounds from leaves of E. uniflora and FTIR-PCA as a sample screening method.

E. uniflora leaves are a rich source of phenolic compounds with biological activities, including myricitrin. In this study, the chemical profile of nine extracts prepared with leaves collected in three regions (mountain, beach, and mangrove) and at three different times of the day (8 am, 1 pm, and 6 pm) was evaluated from spectra originating from ultra-high resolution mass spectrometry (Fourier transform ion cyclotron resonance, FT-ICR) coupled to electrospray ionisation (ESI). The best time of the day and location for collecting the leaves of E. uniflora used as raw materials for producing extracts and the best ethanol concentration for obtaining an extract more abundant in compounds of interest were verified. Several flavonoids and phenolic acids were detected in their deprotonated form in the regions from m/z 200 to 1200. Myricitrin ([C21H20O12-H]-, m/ztheo 463.08820), its chloride adduct ([C21H20O12+Cl]-, m/ztheo 499.06488), other myricitrin derivatives, and some tannins were the main compounds detected. Considering obtaining an extract rich in phenolic compounds, including myricitrin, the best place and time of the day to collect E. uniflora leaves is in the beach region at 1 pm. In contrast, the best ethanol concentration for extract production is 70 wt%. Therefore, extraction at 96 wt% ethanol is better for obtaining an extract more abundant in phenolic acids, although 70 wt% ethanol also extracted these compounds. FTIR-PCA models were used to check for possible similarities in the data according to collection time of the day and location. These models demonstrated an excellent solution for sample screening.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value.

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 µg/mL and >0.5 µg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Correlation analysis of modern analytical data - a chemometric dissection of spectral and chromatographic variables.

The Standard Practices for Infrared Multivariate Quantitative Analysis (ASTM E1655) provide a guide for determining physicochemical properties of materials using multivariate calibration techniques applied to chemical sources that have high multicollinearity and correlated information. Partial least squares (PLS) is the most widely used multivariate regression method due to its excellent prediction capabilities and easy optimization. Initially applied to chromatographic data, PLS has also shown great results in near-infrared (NIR) and mid-infrared (MIR) spectroscopies. However, complex chemical matrices with low correlation may not be efficiently modeled using PLS or other multivariate analyses limited by grouping similar information (such as latent variables or principal components). Therefore, this study aims to evaluate the multicollinearity of different analytical techniques, such as high-temperature gas chromatography (HTGC), NIR, MIR, hydrogen nuclear magnetic resonance (1H NMR), carbon-13 nuclear magnetic resonance (13C NMR), and Fourier transform ion cyclotron resonance mass spectrometry coupled to the electrospray source in positive and negative ionization modes (ESI(±)FT-ICR). Descriptive statistics (coefficient of determination, R2) and principal component analysis (PCA) were used to identify the distribution of correlated information. Results showed that NIR and MIR spectroscopies exhibited a higher percentage of correlated variables, while 13C NMR and ESI(±)FT-ICR MS had more discrete profiles. Therefore, PLS development may be more effectively applied to NIR, MIR, and 1H NMR data, while 13C NMR and mass spectra may require other algorithms or variable selection methods in combination with PLS.

MALDI(+) FT-ICR Mass Spectrometry (MS) Combined with Machine Learning toward Saliva-Based Diagnostic Screening for COVID-19.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

Portable Raman spectroscopy applied to the study of drugs of abuse.

The use of drugs of abuse has grown significantly in recent decades. In forensic chemistry, methods of identifying and characterizing illicit drugs contribute to the interests of researchers, experts, and public security authorities. Among existing methods, portable Raman spectroscopy is notable for performing rapid, non-destructive, and highly selective analysis in the laboratory or on-site. When the resulting spectral data are paired with chemometric tools, methods of exploratory analysis and multivariate calibration can be developed. Thus, this work describes the application of Raman spectroscopy associated with principal component analysis (PCA) and interval principal component analysis (iPCA) to assessing trends in samples of cocaine (n = 40), crack (n = 33), and their main adulterants (n = 5) and diluents (n = 5), tablets of ecstasy (n = 14), designer drugs papers (n = 27), and alcoholic solutions adulterated with benzodiazepines (alprazolam and diazepam). In addition, competitive adaptive reweighted sampling (CARS) combined with partial least squares (PLS) regression (CARSPLS) was used to quantify adulterants (benzocaine, lidocaine, and procaine) in binary mixtures with crack (n = 21) and solutions of cachaça adulterated with bromazepam (n = 11).

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.

Exploring the chemical profile of designer drugs by ESI(+) and PSI(+) mass spectrometry-An approach on the fragmentation mechanisms and chemometric analysis.

The consumption of design drugs, frequently known as new psychoactive substances (NPS), has increased considerably worldwide, becoming a severe issue for the responsible governmental agencies. These illicit substances can be defined as synthetic compounds produced in clandestine laboratories in order to act as analogs of schedule drugs mimetizing its chemical structure and improving its pharmacological effects while hampering the control and making regulation more complicated. In this way, the development of new methodologies for chemical analysis of NPS drugs is indispensable to determine a novel class of drugs arising from the underground market. Therefore, this work shows the use of high-resolution mass spectrometry Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) applying different ionization sources such as paper spray ionization (PSI) and electrospray ionization (ESI) in the evaluation of miscellaneous of seized drugs samples as blotter paper (n = 79) and tablet (n = 100). Also, an elucidative analysis was performed by ESI(+)MS/MS experiments, and fragmentation mechanisms were proposed to confirm the chemical structure of compounds identified. Besides, the results of ESI(+) and PSI(+)-FT-ICR MS were compared with those of GC-MS, revealing that ESI(+)MS showed greater detection efficiency among the methodologies employed in this study. Moreover, this study stands out as a guide for the chemical analysis of NPS drugs, highlighting the differences between the techniques of ESI(+)-FT-ICR MS, PSI(+)-FT-ICR MS, and GC-MS.

DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry.

We have built an online tool with a user-friendly and browser-based interface to facilitate the processing of high resolution and precision oil mass spectrometry data. DropMS does not require software installations. Mass spectra are sent and processed by the server using various algorithms reported in the literature, such as S/N ratio filters, recalibrations, chemical formula assimilations, and data visualization using graphs and diagrams popularly known in mass spectrometry as Van Krevelen and Kendrick diagrams and DBE vs C#. To validate the algorithms used and the processing results, the same mass spectrum of a typical Brazilian oil sample was analyzed by ESI(+)-FT-ICR/MS and processed using Sierra Analytics DropMS and Composer to obtain good agreement between the heteroatomic classes found and the number of compounds assigned. The MS has chemical information spread over the entire spectrum. The PLS multivariate regression has the main objective of decomposing the most important information into latent variables in order to quantify the most evaluated properties. Finally, 12 processed petroleum FT-ICR MS spectra were used for a partial least-squares regression with seven latent variables for R2 = 0.971 and RMSEC of 0.997 for API density property with a reference value range of 21-42.

Estimating the intermediate precision in petroleum analysis by (±)electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry.

RATIONALE: Electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS) is an important analytical technique used for the elucidation of crude oil polar compounds at the molecular level, providing thousands of heteroatom compounds in a single analysis. Due to the high resolution, the complexity of data produced, and steps involved in spectra acquisition and processing, it is necessary to estimate its intermediate precision. METHODS: Intermediate precision was estimated for positive- and negative-ion ionization modes (ESI(±)) using Composer® software for two Brazilian crude oil samples. The analytical parameters evaluated were the class distribution histogram, the double bond equivalent (DBE) distribution, and the DBE versus carbon number. The statistical parameters used to study the intermediate precision were calculated from the average, standard deviation, confidence interval (significance level at 5%), coefficient of variation (CV), intermediate precision limit (ISO 5725), and principal component analysis (PCA). RESULTS: Two crude oil samples (A and B) were analyzed, in triplicate, for seven consecutive days by ESI(±) FT-ICR MS. The assigned class limit by ESI(+) for crude oil A was 0.42% (O2 S[H] class) and for crude oil B was 0.04% (N2 O2 S[H] class). The assigned DBE intensity limits for the two crude oils were 0.04% for ESI(+) and 0.013% for ESI(-). The PCA for ESI(-) and ESI(+) modes presented better precision for crude oils B and A, respectively. CONCLUSIONS: The most abundant classes and DBE of the majority class (i.e., with the highest intensity) are the parameters produced from the Composer® software that had the highest precision and can be used to estimate crude oil properties. The DBE values presented lower intermediate precision limit values (0.04%) than the assigned class values (0.4%). According to CV and PCA, ESI(+) was more precise for crude oil A (83% precision) and ESI(-) for crude oil B (84% precision).

Extraction and isolation of cannabinoids from marijuana seizures and characterization by 1H NMR allied to chemometric tools.

Marijuana, a drug derived from the Cannabis sativa L. plant, is the world's most consumed illicit drug. In this paper, a total of 156 marijuana samples seized in the state of Espírito Santo (ES), Brazil were studied and analysed by proton nuclear magnetic resonance (1H NMR) spectroscopy to identify the major cannabinoids present. A crude extract of all samples was purified using high performance liquid chromatography so that these compounds could serve as reference substances. Nine fractions were obtained and analysed by 1H NMR and gas chromatography-mass spectrometry (GC-MS), with five presented cannabinoids. ∆9-THC (Δ9-trans-tetrahydrocannabinol), ∆9-THCA (∆9-tetrahydrocannabinolic acid), ∆8-THC (∆8-tetrahydrocannabinol), 11-hydroxycannabinol, CBV (cannabivarin), and CBN (cannabinol) were found, and their chemical structures were confirmed by GC-MS. The latter compound was obtained with high purity (≈100%), while the others were obtained as less complex mixtures with purity higher than 75% (except for Δ8-THC). Principal component analysis (PCA) was used on the 1H NMR spectra of the 156 samples, and it was found that the samples were grouped according to the months, differentiating into two groups (from July 2014 to January 2015 and from February 2015 to July 2015), where non-grouping was observed from four macro-regions of the ES state (North, Central, Metropolitan, and South). The chemical profile of the seized samples was correlated to the 1H NMR spectrum of an isolated CBN sub-fraction, in which the group formed by samples seized in the year 2015 presented lower CBN content in the chemical composition. From the PCA score plot, two groups of samples were confirmed using the partial least squares discriminant analysis and orthogonal projections to latent structures classification methods.

Portable near infrared spectroscopy applied to fuel quality control.

Fuel quality control has gained interest in many countries owing to the potential damage of low-quality fuel to engines, the environment, and economy. Thus, the application of analytical techniques to verify quality control of fuels has become crucial. The portable micro-spectrometer in the near infrared region (microNIR) has gained credibility as a successful analytical technique in several quality control sectors. The possibility of real-time analysis using a nondestructive and reliable method is the main advantage of this methodology. In this work, chemometric models (PLS) were developed using microNIR data to determine the amount of biodiesel in diesel (LODBio = 0.5wt%; LOQBio = 1.8wt%; and RMSEPBio = 1.8wt%); sulfur in diesel (LODS = 2.4mgL-1; LOQS = 8.0mgL-1; and RMSEPS = 13.2mgL-1); gasoline, ethanol, and methanol in C-type gasoline (LODgas = 0.55wt%; LOQgas = 1.84wt%; and RMSEPgas = 0.81wt%; LODeth = 0.75wt%; LOQeth = 2.5wt%; and RMSEPeth = 3.81wt%; and LODmet = 0.85wt%; LOQmet = 2.84wt%; and RMSEPmet = 1.80wt%); and water, methanol, and ethanol in ethanol-hydrated fuel (EHF) (LODH2O = 0.04wt%; LOQH2O = 1.29wt%; and RMSEPH2O = 1.05wt%; LODmet = 0.52wt%; LOQmet = 1.73wt%; and RMSEPmet = 2.78wt%; and LODeth = 1.22wt%; LOQeth = 4.07wt%; and RMSEPeth = 4.41wt%). A total of 181 blends were prepared, with biodiesel and sulfur contents ranging from 0 to 100wt% and 10-500mgL-1, respectively. For gasoline blends, the gasoline, ethanol, and methanol contents ranged from 0.0 to 75.0wt%, 25.0-75.0wt%, and 0.0-50.0wt%, respectively. In the EHF control, the ethanol, water, and methanol contents ranged from 0.0 to 100.0wt%, 0.0-50.0wt%, and 0.0-50.0wt%, respectively. The proposed method presented high precision and accuracy in all cases, and the results showed that the microNIR technique had excellent performance in fuel quality control.

Portable near infrared spectroscopy applied to quality control of Brazilian coffee.

The use of portable micro-spectrometers such as a micro near infrared region (microNIR) spectrometer is a promising technique for solving analytical problems in several areas of science. This work evaluated the potential of microNIR in quality control of Arabica coffee. Arabica coffee has a high commercial value product, motivating the development of analytical methods with high sensitivity and accuracy for detection of its adulteration. Herein, microNIR was successfully used to determine the quality of Arabica coffee by identification and quantification of adulterations such as Robusta coffee (in different roasting levels), as well as corn, peels, and sticks. MicroNIR was combined with multivariate calibration by partial least squares (PLS) and principal component analysis (PCA). A total of 125 blends were produced, containing thirteen different concentrations of the adulterants (corn and peels/sticks, and the Robusta coffee) ranging from 1 to 100wt%. Developed PCA and PLS models were also applied to monitor the quality of sixteen commercial coffee samples. The results obtained using microNIR proved the ability of the method to be efficient and capable in the prediction of adulterations with minimum quantification levels (LOQs of 5-8wt%), being able to be applied to quality control of commercial coffee samples. Therefore, microNIR can reduce and simplify the time of analysis and sample preparation step, as well as to guarantee the efficiency of real-time data acquisition owing to its portability.

Thin layer chromatography coupled to paper spray ionization mass spectrometry for cocaine and its adulterants analysis.

Thin layer chromatography (TLC) is a simple and inexpensive type of chromatography that is extensively used in forensic laboratories for drugs of abuse analysis. In this work, TLC is optimized to analyze cocaine and its adulterants (caffeine, benzocaine, lidocaine and phenacetin) in which the sensitivity (visual determination of LOD from 0.5 to 14mgmL(-1)) and the selectivity (from the study of three different eluents: CHCl3:CH3OH:HCOOHglacial (75:20:5v%), (C2H5)2O:CHCl3 (50:50v%) and CH3OH:NH4OH (100:1.5v%)) were evaluated. Aiming to improve these figures of merit, the TLC spots were identified and quantified (linearity with R(2)>0.98) by the paper spray ionization mass spectrometry (PS-MS), reaching now lower LOD values (>1.0µgmL(-1)). The method developed in this work open up perspective of enhancing the reliability of traditional and routine TLC analysis employed in the criminal expertise units. Higher sensitivity, selectivity and rapidity can be provided in forensic reports, besides the possibility of quantitative analysis. Due to the great simplicity, the PS(+)-MS technique can also be coupled directly to other separation techniques such as the paper chromatography and can still be used in analyses of LSD blotter, documents and synthetic drugs.

A survey of adulterants used to cut cocaine in samples seized in the Espírito Santo State by GC-MS allied to chemometric tools.

Cocaine is a stimulant drug of the central nervous system (CNS) extracted from the leaves of Erytroxylum coca. It is defined as a tropane alkaloid containing 1R-(exo,exo)-3-(benzoyloxy)-8-methyl-8-azabicyclo[3.2.1]octane-2-carboxylic acid methyl esther. However, despite its defined composition, a wide variety of chemical additives are present in cocaine found in the illicit market, such as benzocaine, lidocaine, caffeine, procaine and phenacetin. In this work, 512 cocaine samples seized by the Civil Police of Espirito Santo state (PC-ES, Brazil) were analyzed by gas chromatography mass spectrometry (GC-MS) allied to principal component analysis (PCA) in order to classify the samples as a function of seizure year (2008, 2009, 2010, 2011 and 2012) and location (metropolitan, north, south and central). The cocaine content (wt.%) and its adulterants were also estimated. Analyzing the samples seized between 2008 and 2011, three sample sets are clearly grouped according to the degree of adulteration with caffeine and lidocaine: 100-50 wt.% of cocaine; 50-20 wt.% of cocaine; and 20-80 wt.% of lidocaine and 60-80 wt.% of caffeine, simultaneously. The last group is formed by samples seized between 2008 and 2009, which proves the higher degree of adulteration during this period. In 2012, higher cocaine content was observed for the 191 analyzed samples than in samples from previous years. The PCA data also suggests that the metropolitan region samples had a higher degree of adulteration than the state countryside samples.

Integrative analysis to select cancer candidate biomarkers to targeted validation.

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

Prediction of the distillation temperatures of crude oils using ¹H NMR and support vector regression with estimated confidence intervals.

This paper aims to estimate the temperature equivalent to 10% (T10%), 50% (T50%) and 90% (T90%) of distilled volume in crude oils using (1)H NMR and support vector regression (SVR). Confidence intervals for the predicted values were calculated using a boosting-type ensemble method in a procedure called ensemble support vector regression (eSVR). The estimated confidence intervals obtained by eSVR were compared with previously accepted calculations from partial least squares (PLS) models and a boosting-type ensemble applied in the PLS method (ePLS). By using the proposed boosting strategy, it was possible to identify outliers in the T10% property dataset. The eSVR procedure improved the accuracy of the distillation temperature predictions in relation to standard PLS, ePLS and SVR. For T10%, a root mean square error of prediction (RMSEP) of 11.6°C was obtained in comparison with 15.6°C for PLS, 15.1°C for ePLS and 28.4°C for SVR. The RMSEPs for T50% were 24.2°C, 23.4°C, 22.8°C and 14.4°C for PLS, ePLS, SVR and eSVR, respectively. For T90%, the values of RMSEP were 39.0°C, 39.9°C and 39.9°C for PLS, ePLS, SVR and eSVR, respectively. The confidence intervals calculated by the proposed boosting methodology presented acceptable values for the three properties analyzed; however, they were lower than those calculated by the standard methodology for PLS.

Membrane lipid profile monitored by mass spectrometry detected differences between fresh and vitrified in vitro-produced bovine embryos.

This study aimed to evaluate the impact of vitrification on membrane lipid profile obtained by mass spectrometry (MS) of in vitro-produced bovine embryos. Matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) has been used to obtain individual embryo membrane lipid profiles. Due to conditions of analysis, mainly membrane lipids, most favorably phosphatidylcholines (PCs) and sphingomyelins (SMs) have been detected. The following ions described by their mass-to-charge ratio (m/z) and respective attribution presented increased relative abundance (1.2-20×) in the vitrified group: 703.5 [SM (16:0) + H]+; 722.5 [PC (40:3) + Na]+; 758.5 [PC (34:2) + H]+; 762.5 [PC (34:0) + H]+; 790.5 [PC (36:0) + H]+ and 810.5 [PC (38:4) + H]+ and/or [PC (36:1) + Na]+. The ion with a m/z 744.5 [PCp (34:1) and/or PCe (34:2)] was 3.4-fold more abundant in the fresh group. Interestingly, ions with m/z 722.5 or 744.5 indicate the presence of lipid species, which are more resistant to enzymatic degradation as they contain fatty acyl residues linked through ether type bonds (alkyl ether or plasmalogens, indicated by the lowercase 'e' and 'p', respectively) to the glycerol structure. The results indicate that cryopreservation impacts the membrane lipid profile, and that these alterations can be properly monitored by MALDI-MS. Membrane lipids can therefore be evaluated by MALDI-MS to monitor the effect of cryopreservation on membrane lipids, and to investigate changes in lipid profile that may reflect the metabolic response to the cryopreservation stress or changes in the environmental conditions.

Assessment of robustness on analysis using headspace solid-phase microextraction and comprehensive two-dimensional gas chromatography through experimental designs.

Plackett-Burman experimental design was applied for the robustness assessment of GC×GC-qMS (Comprehensive Two-Dimensional Gas Chromatography with Fast Quadrupolar Mass Spectrometric Detection) in quantitative and qualitative analysis of volatiles compounds from chocolate samples isolated by headspace solid-phase microextraction (HS-SPME). The influence of small changes around the nominal level of six factors deemed as important on peak areas (carrier gas flow rate, modulation period, temperature of ionic source, MS photomultiplier power, injector temperature and interface temperature) and of four factors considered as potentially influential on spectral quality (minimum and maximum limits of the scanned mass ranges, ions source temperature and photomultiplier power). The analytes selected for the study were 2,3,5-trimethylpyrazine, 2-octanone, octanal, 2-pentyl-furan, 2,3,5,6-tetramethylpyrazine, and 2-nonanone e nonanal. The factors pointed out as important on the robustness of the system were photomultiplier power for quantitative analysis and lower limit of mass scanning range for qualitative analysis.

Petroleomics by electrospray ionization FT-ICR mass spectrometry coupled to partial least squares with variable selection methods: prediction of the total acid number of crude oils.

Negative-ion mode electrospray ionization, ESI(-), with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was coupled to a Partial Least Squares (PLS) regression and variable selection methods to estimate the total acid number (TAN) of Brazilian crude oil samples. Generally, ESI(-)-FT-ICR mass spectra present a power of resolution of ca. 500,000 and a mass accuracy less than 1 ppm, producing a data matrix containing over 5700 variables per sample. These variables correspond to heteroatom-containing species detected as deprotonated molecules, [M - H](-) ions, which are identified primarily as naphthenic acids, phenols and carbazole analog species. The TAN values for all samples ranged from 0.06 to 3.61 mg of KOH g(-1). To facilitate the spectral interpretation, three methods of variable selection were studied: variable importance in the projection (VIP), interval partial least squares (iPLS) and elimination of uninformative variables (UVE). The UVE method seems to be more appropriate for selecting important variables, reducing the dimension of the variables to 183 and producing a root mean square error of prediction of 0.32 mg of KOH g(-1). By reducing the size of the data, it was possible to relate the selected variables with their corresponding molecular formulas, thus identifying the main chemical species responsible for the TAN values.