LC-MS analyses revealed significant metabolic changes associated with the docosahexaenoic acid supplementation in rats.

Evidences suggest that dietary docosahexaenoic acid (DHA) supplementation may have pleiotropic beneficial effects on health. However, the underlying mechanisms and crucial targets that are involved in achieving these benefits remain to be clarified. In this study, we employed biochemical analysis and liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics coupled with multivariate statistical analysis to identify potential metabolic targets of DHA in adult rats at 48 h post-feeding. Blood biochemical analysis showed a significant decrease in triglyceride level of DHA diet group, the untargeted metabolomic analysis revealed that some metabolites were significantly different between the DHA diet group and the basal diet group, including fatty acids (16:0, 18:1, 20:5n3, 22:2n6 and 24:0), diglyceride (20:0/18:2n6, 18:3n6/22:6n3, 20:4n3/20:4n3, and 22:0/24:0), PIP2 (18:2/20:3), phytol, lysoSM (d18:1), 12-hydroxyheptadecatrienoic acid, dihydrocorticosterone and N1-acetylspermine, which are mainly involved in fat mobilization and triglyceride hydrolysis, arachidonic acid, steroid hormone, and polyamine metabolism. To our knowledge, this is the first report that links the metabolic effects of DHA with arachidonic acid, steroid, and polyamine metabolism. Our finding suggests that the beneficial effects of DHA, may not directly require its own metabolic derivatives, but could be achieved by metabolic regulation.

Pipeline for High-Throughput Modeling of Marijuana and Hemp Extracts.

Authentication of Cannabis products is important for assuring the quality of manufacturing, with the increasing consumption and regulation. In this report, a two-stage pipeline was developed for high-throughput screening and chemotyping the spectra from two sets of botanical extracts from the Cannabis genus. The first set contains different marijuana samples with higher concentrations of tetrahydrocannabinol (THC). The other set includes samples from hemp, a variety of Cannabis sativa with the THC concentration below 0.3%. The first stage applies the technique of class modeling to determine whether spectra belong to marijuana or hemp and reject novel spectra that may be neither marijuana nor hemp. An automatic soft independent modeling of class analogy (aSIMCA) that self-optimizes the number of principal components and the decision threshold is utilized in the first pipeline process to achieve excellent efficiency and efficacy. Once these spectra are recognized by aSIMCA as marijuana or hemp, they are then routed to the appropriate classifiers in the second stage for chemotyping the spectra, i.e., identifying these spectra into different chemotypes so that the pharmacological properties and cultivars of the spectra can be recognized. Three multivariate classifiers, a fuzzy rule building expert system (FuRES), super partial least-squares-discriminant analysis (sPLS-DA), and support vector machine tree type entropy (SVMtreeH), are employed for chemotyping. The discriminant ability of the pipeline was evaluated with different spectral data sets of these two groups of botanical samples, including proton nuclear magnetic resonance, mass, and ultraviolet spectra. All evaluations gave good results with accuracies greater than 95%, which demonstrated promising application of the pipeline for automated high-throughput screening and chemotyping marijuana and hemp, as well as other botanical products.

Automatic soft independent modeling for class analogies.

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Characterization of Brazilian coffee based on isotope ratio mass spectrometry (δ13C, δ18O, δ2H, and δ15N) and supervised chemometrics.

Authentication of ground coffee has become an important issue because of fraudulent activities in the sector. In the current work, sixty-seven Brazilian coffees produced in different geographical origins using organic (ORG, n = 25) and conventional (CONV, n = 42) systems were analyzed for their stable isotope ratios (δ13C, δ18O, δ2H, and δ15N). Data were analyzed by inferential analysis to compare the factors whereas linear discriminant analysis (LDA), k-nearest neighbors (k-NN), and support vector machines (SVM) were used to classify the coffees based on their origin. ORG and CONV cultivated coffees could not be differentiated according to C stable isotope ratio (δ13C; p = 0.204), but ORG coffees presented higher values of the N stable isotope ratio (δ15N; p = 0.0006). k-NN presented the best classification results for both ORG and CONV coffees (87% and 67%, respectively). SVM correctly classified coffees produced in São Paulo (75% accuracy), while LDA correctly classified 71% of coffees produced in Minas Gerais.

Selected-ion flow-tube mass-spectrometry (SIFT-MS) fingerprinting versus chemical profiling for geographic traceability of Moroccan Argan oils.

This study investigated the effectiveness of SIFT-MS versus chemical profiling, both coupled to multivariate data analysis, to classify 95 Extra Virgin Argan Oils (EVAO), originating from five Moroccan Argan forest locations. The full scan option of SIFT-MS, is suitable to indicate the geographic origin of EVAO based on the fingerprints obtained using the three chemical ionization precursors (H3O+, NO+ and O2+). The chemical profiling (including acidity, peroxide value, spectrophotometric indices, fatty acids, tocopherols- and sterols composition) was also used for classification. Partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and support vector machines (SVM), were compared. The SIFT-MS data were therefore fed to variable-selection methods to find potential biomarkers for classification. The classification models based either on chemical profiling or SIFT-MS data were able to classify the samples with high accuracy. SIFT-MS was found to be advantageous for rapid geographic classification.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study.

Discovery of markers for discriminating the age of cultivated ginseng by using UHPLC-QTOF/MS coupled with OPLS-DA.

BACKGROUND: Ginseng (Ginseng Radix et Rhizoma, Panax ginseng C.A. Meyer) is gaining more publicity in modern society due to its health benefit and huge value in market. In the practice of grading and pricing of ginseng, the age is one of the major factor influencing the price and grade of ginseng. Therefore, the age discrimination is an important task for the quality control of ginseng. However, the traditional morphological methods are too subjective to be reproductive in discrimination. PURPOSE: To establish a method that can discriminate the ginseng samples with different cultivation years. STUDY DESIGN: To analyze the correlation between chemical compositions and cultivation years of cultivated ginseng samples of different age and thus discover potential quality marker (Q-marker) for discriminating the age of cultivated ginseng. METHODS: In the present study, the ultra-high performance liquid chromatography coupled with the quadrupole-time of flight mass spectrometry (UHPLC-QTOF/MS) were utilized for the age discrimination and marker discovery. A statistical data processing procedure was established to screen markers and reduce the false positive rate. RESULTS: The results showed that the ginseng samples from 2- to 6-year-old could be well separated in the orthogonal projections on the latent structure - discrimination analysis (OPLS-DA) using the markers screened by the established statistical procedure, which could reduce approximately 20% of the insignificant markers and false positive discoveries. Ultimately, more than 50 compounds contributing to the age discrimination were identified including one new compound (malonylginsenoside). One negative marker (1038.4825@8.98) was discovered for the 2-year-old ginseng, and an equation was established to effectively predict the age of 3- to 6-year-old of ginseng. CONCLUSION: The constructed method can discriminate the ginseng samples with different cultivation years and is a complement to the traditional discrimination method of ginseng age.

Heat-Treatment-Responsive Proteins in Different Developmental Stages of Tomato Pollen Detected by Targeted Mass Accuracy Precursor Alignment (tMAPA).

Recently, we have developed a quantitative shotgun proteomics strategy called mass accuracy precursor alignment (MAPA). The MAPA algorithm uses high mass accuracy to bin mass-to-charge (m/z) ratios of precursor ions from LC-MS analyses, determines their intensities, and extracts a quantitative sample versus m/z ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm that allows the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to indistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages, post-meiotic and mature. Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role in fruit setting and yield. By LC-MS-based shotgun proteomics, we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data-processing strategy, 51 unique proteins were identified as heat-treatment-responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.

Determination of 234U/238U, 235U/238U and 236U/238U isotope ratios in urine using sector field inductively coupled plasma mass spectrometry.

Quantification of the isotopic composition of uranium in urine at low levels of concentration is important for assessing both military and civilian populations' exposures to uranium. However, until now there has been no convenient, precise method established for rapid determination of multiple uranium isotope ratios. Here, the authors report a new method to measure (234)U/(238)U, (235)U/(238)U and (236)U/(238)U. It uses solid-phase chelation extraction (via TRU columns) of actinides from the urine matrix, followed by measurement using a magnetic sector field inductively coupled plasma mass spectrometer (SF-ICP-MS-Thermo Element XR) equipped with a high-efficiency nebulizer (Apex PFA microflow) and coupled with a membrane desolvating nebulizer system (Aridus II™). This method provides rapid and reliable results and has been used successfully to analyse Certified Reference Materials.

Rapid characterization of caged xanthones in the resin of Garcinia hanburyi using multiple mass spectrometric scanning modes: the importance of biosynthetic knowledge based prediction.

Although the anticancer activities of the resin of Garcinia hanburyi have been well demonstrated, the chemical composition of this medicinal plant is still not fully understood. In this study, a highly effective qualitative method was developed for rapidly profiling the target and non-target caged xanthones in the resin of G. hanburyi. This method mainly involves three steps as follows: (1) prediction of the possible unknown caged xanthones in the resin of G. hanburyi according to the structure characters of the known ones and some well established biosynthetic knowledge; (2) structure classification according to the diagnostic fragment ions (DFIs) of the known caged Garcinia xanthones; (3) detection and characterization of the target and non-target caged xanthones in the resin of G. hanburyi using multiple mass spectrometric (MS) scanning modes. By use of such procedures, mass spectrometric data can be used for confirming the rationally predicted chemical structure rather than sophisticated and time-consumed de novo structure elucidation of a completely unknown component. Finally, a total of 34 caged xanthones including 18 likely new ones from the resin of G. hanburyi were rapidly detected and characterized within one working day.

Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review.

Organic species are an important but poorly characterized constituent of airborne particulate matter. A quantitative understanding of the organic fraction of particles (organic aerosol, OA) is necessary to reduce some of the largest uncertainties that confound the assessment of the radiative forcing of climate and air quality management policies. In recent years, aerosol mass spectrometry has been increasingly relied upon for highly time-resolved characterization of OA chemistry and for elucidation of aerosol sources and lifecycle processes. Aerodyne aerosol mass spectrometers (AMS) are particularly widely used, because of their ability to quantitatively characterize the size-resolved composition of submicron particles (PM(1)). AMS report the bulk composition and temporal variations of OA in the form of ensemble mass spectra (MS) acquired over short time intervals. Because each MS represents the linear superposition of the spectra of individual components weighed by their concentrations, multivariate factor analysis of the MS matrix has proved effective at retrieving OA factors that offer a quantitative and simplified description of the thousands of individual organic species. The sum of the factors accounts for nearly 100% of the OA mass and each individual factor typically corresponds to a large group of OA constituents with similar chemical composition and temporal behavior that are characteristic of different sources and/or atmospheric processes. The application of this technique in aerosol mass spectrometry has grown rapidly in the last six years. Here we review multivariate factor analysis techniques applied to AMS and other aerosol mass spectrometers, and summarize key findings from field observations. Results that provide valuable information about aerosol sources and, in particular, secondary OA evolution on regional and global scales are highlighted. Advanced methods, for example a-priori constraints on factor mass spectra and the application of factor analysis to combined aerosol and gas phase data are discussed. Integrated analysis of worldwide OA factors is used to present a holistic regional and global description of OA. Finally, different ways in which OA factors can constrain global and regional models are discussed.

Confident identification of 3-nitrotyrosine modifications in mass spectral data across multiple mass spectrometry platforms.

3-nitrotyrosine (3NT) is an oxidative posttranslational modification associated with many diseases. Determining the specific sites of this modification remains a challenge due to the low stoichiometry of 3NT modifications in biological samples. Mass spectrometry-based proteomics is a powerful tool for identifying 3NT modifications, however several reports identifying 3NT sites were later demonstrated to be incorrect, highlighting that both the accuracy and efficiency of these workflows need improvement. To advance our understanding of the chromatographic and spectral properties of 3NT-containing peptides we have adapted a straightforward, reproducible procedure to generate a large set of 3NT peptides by chemical nitration of a defined, commercially available 48 protein mixture. Using two complementary LC-MS/MS platforms, a QTOF (QSTAR Elite) and dual pressure ion trap mass spectrometer (LTQ Velos), we detected over 200 validated 3NT-containing peptides with significant overlap in the peptides detected by both systems. We investigated the LC-MS/MS properties for each peptide manually using defined criteria and then assessed their utility to confirm that the peptide was 3NT modified. This broad set of validated 3NT-containing peptides can be utilized to optimize mass spectrometric instrumentation and data mining strategies or further develop 3NT peptide enrichment strategies for this biologically important, oxidative posttranslational modification.

Simultaneous determination of cholecalciferol (vitamin D3) and ergocalciferol (vitamin D2) in foods by selected reaction monitoring.

Cholecalciferol (vitamin D3) and ergocalciferol (vitamin D2) were determined simultaneously by selected reaction monitoring (SRM) mass spectrometry for different food matrixes. A small amount of starting sample was saponified and extracted before injection into a linear ion trap mass spectrometer equipped with an atmospheric pressure chemical ionization source. Dihydrotachysterol, which is absent from food and has a structure similar to that of vitamins D3 and D2, was used as an internal standard. Calibration curves for the 2 vitamins showed linearity with R2 values of 0.9999 and 0.9989 for vitamins D3 and D2, respectively. Limits of detection for vitamins D3 and D2 were 0.5 ng/g (1.3 pmol/g) and 1.75 ng/g (4.4 pmol/g) and limits of quantitation were 1.25 ng/g (3.24 pmol/g), and 3.75 ng/g (9.45 pmol/g), respectively. Accuracy and precision of the method were tested with the infant formula reference standard of the National Institute of Standards and Technology, which showed a relative standard deviation of 6%. Recoveries ranged from 95 to 105%. Several food products were tested with AOAC Method 982.29, which is currently in use for vitamins D3 and D2, and results were comparable within 6%.

Isotope dilution mass spectrometry as a candidate definitive method for determining total glycerides and triglycerides in serum.

A new isotope dilution mass spectrometric method for total glycerides and triglycerides in human serum is described. Total glycerides are defined as the sum of tri-, di-, and monoglycerides plus free glycerol; triglycerides are defined as the pure triglyceride species. In both determinations, serum samples are supplemented by addition of [13C3]tripalmitin, processed, derivatized, and the abundance ratios of selected ions are determined. Bias is investigated by quantifying the analyte in the same samples under different chromatographic and ionization conditions. The analytes were determined in two human serum pools. The CV for a single measurement ranged from 0.35% to 0.72%, and the relative SEM ranged from 0.10% to 0.34%; there was no significant bias in the measurements. The combination of high precision and absence of significant bias in the results qualify this method for consideration as a Definitive Method as defined by the National Committee for Clinical Laboratory Standards.