Mass Spectral Fingerprints Research Articles

Antioxidants Discovery for Differentiation of Monofloral Stingless Bee Honeys Using Ambient Mass Spectrometry and Metabolomics Approaches.

Stingless bee honey (SBH) is a natural, sweet product produced by stingless bees (Meliponini tribe) that has been used as a traditional medicine to treat various illnesses. It has been shown that SBH has high nutritional value and health-promoting properties due to the presence of plant bioactive compounds from different botanical flora of the foraged nectar. In this study, the antioxidant activities of seven monofloral honeys from acacia, agarwood, coconut, dwarf mountain pine (DMP), Mexican creeper (MC), rubber, and starfruit botanical origins were investigated. The antioxidant properties of SBH studied had a range from 19.7 to 31.4 mM TE/mg for DPPH assays, 16.1 to 29.9 mM TE/mg for ABTS assays, 69.0 to 167.6 mM TE/mg for ORAC assays, and 45.5 to 89.3 mM Fe2+/mg for FRAP assays. Acacia honey showed the highest level of antioxidant properties. The models built from mass spectral fingerprints from direct ambient mass spectrometry showed distinct clusters of SBH by botanical origin and correlated with the antioxidant properties. An untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach was undertaken to identify the antioxidant compounds that could explain the unique antioxidant and compositional profiles of the monofloral SBH by its botanical origin. The antioxidants that were identified predominantly consisted of alkaloids and flavonoids. Flavonoid derivatives, which are potent antioxidants, were found to be key markers of acacia honey. This work provides the fundamental basis for the identification of potential antioxidant markers in SBH associated with the botanical origin of the foraged nectar.

Metabolomics for Plant Health Biosecurity Diagnostics and Response

The increasing diversity of potential biosecurity threats makes their diagnosis a complicated and evolving area, requiring moving beyond traditional taxonomic species identification. New biosecurity diagnostic tools should provide a greater depth of information on threat biology to enable accurate risk assessment for the more efficient and effective deployment of biosecurity resources. Metabolomics is amongst the new approaches being explored for biosecurity diagnostics, where a broad spectrum of metabolites might signify relevant biological characteristics of an intercepted organism. Examples of these characteristics are physiological signatures of age, reproductive status, geographic origin, pathogen status of potential invertebrate vectors, and the distinction between diseases and abiotic plant stress symptoms. This broad-based approach is attractive, where several biological characteristics of an organism can be assessed with a single measurement. However, it can be impractical as several hundred biological replicates of the organism are needed to build a robust model of a species. New approaches such as mass spectral fingerprinting substantially reduce the time taken for metabolomics measurements, and more sophisticated modelling methods aid feasibility. Promising data are emerging for metabolomics and metabolite fingerprinting for potential biosecurity applications. We discuss the possibilities and potential uses for these new tools for post-detection decisions, diagnosis, and biosecurity.

Phytochemical Characterization of Malt Spent Grain by Tandem Mass Spectrometry also Coupled with Liquid Chromatography: Bioactive Compounds from Brewery By-Products.

Brewer's spent grain (BSG) is one of the main by-products of beer industry, little used because of its high moisture making it difficult to transport and store. Mainly used as animal feed and for energy production, the agro-industrial waste have recently attracted attention as source of bioactive compounds, with potential applications in many sectors as food, nutraceutical, pharmaceutical, cosmetic, food packaging. The present work focuses on BSG as potential source of valuable small-size bioactive compounds. Laboratory-made BSG was obtained by using four base malts for mashing. After drying, BSG was eco-friendly extracted with water and the extracts analyzed by untargeted ElectroSpray Ionization (ESI)-Mass Spectrometry (MS)/Mass Spectrometry (MS) (ESI-MS/MS) infusion experiments and by targeted High Performance Liquid Chromatography-PhotoDiodeArray-ElectroSpray Ionization-Mass Spectrometry (HPLC-PDA-ESI-MS) in Selected Ion Recording (SIR) mode analysis, to investigate the metabolic profile, the phenolic profile, the individual phenolic content, and tryptophan content. Aqueous extracts of malts and wort samples were also analyzed for a comparison. Data were statistically analyzed by ANOVA test. An explorative analysis based on Principal Component Analysis (PCA) was also carried out on malts, wort and threshes, in order to study correlation among samples and between samples and variables. The untargeted ESI-MS/MS infusion experiments provided the mass spectral fingerprint of BSG, evidencing amino acids (γ-aminobutyric acid, proline, valine, threonine, leucine/isoleucine, lysine, histidine, phenylalanine and arginine) and organic and inorganic acids (pyruvic, lactic, phosphoric, valerianic, malonic, 2-furoic, malic, citric and gluconic acids), besides sugars. γ-Aminobutyric acid and lactic acid resulted predominant among the others. The targeted HPLC-PDA-ESI-MS in SIR mode analysis provided the phenolic profile of the polar fraction of BSG, evidenced tryptophan as the main residual metabolite in BSG (62.33-75.35 μg/g dry BSG), and catechin (1.13-4.24 μg/g dry BSG) as the representative phenolic antioxidant of not pre-treated BSG samples. The chemometric analysis of the individual compounds content in BSG, malt and wort evidenced similarities and differences among the samples. As main goal, the phytochemical characterization of BSG from base malts highlighted BSG as a potential source of small biomolecules, as tryptophan and catechin, besides γ-aminobutyric acid and lactic acid, opening to new perspectives of application for BSG. Strategies for their recovery are a future challenge. Moreover, ESI-MS/MS analysis was confirmed as a powerful tool for fast characterization of complex matrix. Last, results obtained by chemometric elaboration of data demonstrated the possibility to monitor a small number of molecules to ensure the quality of a final product.

Toward Depth-Resolved Analysis of Plant Metabolites by Nanospray Desorption Electrospray Ionization Mass Spectrometry.

Nanospray desorption electrospray ionization (nano-DESI) is one of the ambient desorption ionization methods for mass spectrometry (MS), and it utilizes a steady-state liquid junction formed between two microcapillaries to directly extract analytes from sample surfaces with minimal sample damage. In this study, we employed nano-DESI MS to perform a metabolite fingerprinting analysis directly from a Hypericum leaf surface. Moreover, we investigated whether changes in metabolite fingerprints with time can be related to metabolite distribution according to depth. From a raw Hypericum leaf, the mass spectral fingerprints of key metabolites, including flavonoids and prenylated phloroglucinols, were successfully obtained using ethanol as a nano-DESI solvent, and the changes in their intensities were observed with time via full mass scan experiments. In addition, the differential extraction patterns of the obtained mass spectral fingerprints were clearly visualized over time through selected ion monitoring and pseudo-selected reaction monitoring experiments. To examine the correlation between the time-dependent changes in the metabolite fingerprints and depth-wise metabolite distribution, we performed a nano-DESI MS analysis against leaves whose surface layers were removed multiple times by forming polymeric gum Arabic films on their surfaces, followed by detaching. The preliminary results showed that the changes in the metabolite fingerprints according to the number of peelings showed a similar pattern with those obtained from the raw leaves over time.

Volatile fingerprinting by solid-phase microextraction mass spectrometry for rapid classification of honey botanical source

Volatile fingerprinting by Headspace-Solid-Phase Microextraction followed by Direct Injection Mass Spectrometry (HS-SPME DIMS) has been assessed for the first time as an alternative approach to gas chromatographic methods (HS-SPME GC-MS) for the rapid classification of honey botanical origin. In order to fully evaluate the potential of SPME for honey authentication, samples from different botanical sources (eucalyptus, citrus, acacia, rosemary and honeydew) were analyzed by both approaches using either carboxen/polydimethylsiloxane (C/PDMS) or polyacrylate SPME fibers. These datasets were further subjected to different supervised and unsupervised chemometric techniques, including powerful machine learning methods such as FreeViz, not previously applied for this purpose. The best overall classification rate (99% success), outperforming that provided by GC-MS data (98.25% success), was obtained when partial least squares-linear discriminant analysis was applied to mass spectral fingerprints gathered by C/PDMS fiber. The HS-SPME DIMS approach here optimized is shown as an advantageous alternative in terms of analysis time over chromatographic methods (2 vs 50 min), as well as a reliable and cost-efficient approach for honey source authentication issues both in research and food industry fields.

Comparison of Plasma Ionization- and Secondary Electrospray Ionization- High-resolution Mass Spectrometry for Real-time Breath Analysis

Real-time breath analysis by high-resolution mass spectrometry (HRMS) is a promising method to noninvasively retrieve relevant biochemical information. In this work, we conducted a head-to-head comparison of two ionization techniques: Secondary electrospray ionization (SESI) and plasma ionization (PI), for the analysis of exhaled breath. Two commercially available SESI and PI sources were coupled to the same HRMS device to analyze breath of two healthy individuals in a longitudinal study. We analyzed 58 breath specimens in both platforms, leading to 2,209 and 2,296 features detected by SESI-HRMS and by PI-HRMS, respectively. 60% of all the mass spectral features were detected in both platforms. However, remarkable differences were noted in terms of the signal-to-noise ratio (S/N), whereby the median (interquartile range, IQR) S/N ratio for SESI-HRMS was 115 (IQR = 408), whereas for PI-HRMS it was 5 (IQR = 5). Differences in the mass spectral profiles for the same samples make the inter-comparability of both techniques problematic. Overall, we conclude that both techniques are excellent for real-time breath analysis because of the very rich mass spectral fingerprints. However, further work is needed to fully understand the exact metabolic insights one can gather using each of these platforms.

Characterization of Eocene flint

Eocene flint 48–56.0 million years old (mya) from the Negev desert (Israel) was characterized using a suite of analytical techniques. High-resolution transmission electron microscopy (HR-TEM) and selected area electron diffraction (SAED) of the inorganic component showed the texture, morphology, size, and distribution of two silica polymorphs: α–quartz and moganite. While euhedral forms were attributed to α-quartz, moganite crystals were comprised of spherulitic grains. An electron less-dense amorphous material (no scattering under SAED) was found between the siliceous crystallites. Energy dispersive X-rays (EDS) and electron energy loss spectroscopy (EELS) demonstrated that this electron less-dense amorphous material is composed solely of carbon. Low vacuum, low energy backscattered environmental scanning electron microscopy (BSE-eSEM) imaging of flint surfaces showed the presence of micrometer-sized organic inclusions randomly distributed throughout the siliceous matrix. Energy-dispersive X-ray studies (EDS) demonstrated that these organic micro-inclusions were composed of carbon, sulfur, and nitrogen with a C/N ratio attributed to marine sources. These micro-inclusions were not directly associated with hard-shell fossils. BSE-eSEM imaging conditions allowed the identification of entrapped carbon-rich organic material, which is not possible when applying commonly used electron microscopy conditions that require carbon coating and high acceleration voltages, rendering carbon-rich features electron-transparent. Phase contrast-enhanced micro-computed tomography (PC-μCT) showed that these organic micro-inclusions were randomly distributed throughout the siliceous matrix.Time-of-flight secondary ion mass spectrometry (ToF-SIMS), nano-Fourier transform infrared spectroscopy (nano-FTIR), and scanning probe microscopy (SPM) were used to further characterize these organic micro-inclusions. These three in situ analytical techniques with nanometer resolution provided complementary information on the chemical composition and structure of the organic material. Specifically, ToF-SIMS analysis revealed amino acid and hydrocarbon mass spectra fingerprints inside the organic micro-inclusions. While the former were exclusively found in the organic micro-inclusions, the mass spectral fingerprints for hydrocarbons were also found in the siliceous matrix in agreement with the HR-TEM/EDS/EELS results, where pure carbon was found between the siliceous nanocrystals. While ToF-SIMS provides chemical information, it does not provide structural information. Nano-FTIR analysis showed the presence of amide I and II infrared vibrations exclusively on the organic micro-inclusions. The scanning probe microscopy (SPM) techniques Peak Force Quantitative Nanomechanics (PF-QNM) and Contact Resonance Atomic Force Microscopy (CR-AFM) were used to assess the mechanical properties. PF-QNM measurements on the organic micro-inclusions, under dry and liquid conditions, demonstrated that the organic micro-inclusions swell upon hydration and soften, pointing toward the presence of hydrophilic molecules in agreement with nano-FTIR and ToF-SIMS results. CR-AFM allows in situ determination of the mechanical properties of materials with high stiffness at nanometer resolution. This technique, rarely used in a geological context, revealed that the organic micro-inclusions had an unusually high stiffness atypical for modern organic material, which was attributed to molecular cross-linking promoted by diagenesis.This work provided a comprehensive view of the inorganic and organic components of Eocene flint from the Negev desert with implications for paleontology and archaeology. It offers a roadmap of novel complementary techniques that can be used in the exploration of entrapped organic material in flint.

Validation of breath biomarkers for obstructive sleep apnea

Background and objectivesObstructive sleep apnea (OSA) is an underdiagnosed respiratory disease with negative metabolic and cardiovascular effects. The current gold standard for diagnosing OSA is in-hospital polysomnography, a time-consuming and costly procedure, often inconvenient for the patient. Recent studies revealed evidence for the potential of breath analysis for the diagnosis of OSA based on a disease-specific metabolic pattern. However, none of these findings were validated in a larger and broader cohort, an essential step for its application in clinics. MethodsIn the present study, we validated a panel of breath biomarkers in a cohort of patients with possible OSA (N = 149). These markers were previously identified in our group by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). ResultsHere, we could confirm significant differences between metabolic patterns in exhaled breath from OSA patients compared to control subjects without OSA as well as the association of breath biomarker levels with disease severity. Our prediction of the diagnosis for the patients from this completely independent validation study using a classification model trained on the data from the previous study resulted in an area under the receiver operating characteristic curve of 0.66, which is comparable to questionnaire-based OSA screenings. ConclusionsThus, our results suggest that breath analysis by SESI-HRMS might be useful to screen for OSA as an objective measure. However, its true predictive power should be tested in combination with OSA screening questionnaires. Clinical trial“Mass Spectral Fingerprinting in Obstructive Sleep Apnoea”, NCT02810158, www.ClinicalTrials.gov.

Surface Micro Discharge-Cold Atmospheric Pressure Plasma Processing of Common House Cricket Acheta domesticus Powder: Antimicrobial Potential and Lipid-Quality Preservation.

The growing world population and the need to reduce the environmental impact of food production drive the exploration of novel protein sources. Insects are being cultivated, harvested, and processed to be applied in animal and human nutrition. The inherent microbial contamination of insect matrices requires risk management and decontamination strategies. Thermal sterilization results in unfavorable cooking effects and oxidation of fatty acids. The present study demonstrates the risk management in Acheta domesticus (home cricket) powder with a low-energy (8.7–22.0 mW/cm2, 5 min) semi-direct surface micro discharge (SMD)–cold atmospheric pressure plasma (CAPP). At a plasma power density lower than 22 mW/cm2, no degradation of triglycerides (TG) or increased free fatty acids (FFA) content was detected. For mesophilic bacteria, 1.6 ± 0.1 log10 reductions were achieved, and for Enterobacteriaceae, there were close to 1.9 ± 0.2 log10 reductions in a layer of powder. Colonies of Bacillus cereus, Bacillus subtilis, and Bacillus megaterium were identified via the mass spectral fingerprint analyzed with matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry (MS). The spores of these Bacillus strains resisted to a plasma power density of 22 mW/cm2. Additional inactivation effects at non-thermal, practically non-oxidative conditions are supposed for low-intensity plasma treatments combined with the powder’s fluidization.

Identification, Quantification, and Imaging of the Biodistribution of Soot Particles by Mass Spectral Fingerprinting.

Soot is ubiquitous and has large detrimental effects on climate, air quality, and human health. However, identification of soot in carbonaceous media is very challenging due to its nanoscale carbon nature and complex sources. Due to the shortage in the methodology, until now, the fate and health effect of soot particles after inhalation are still poorly understood. Here, we report a new method for label-free identification, quantification, and imaging of soot particles in complex media based on laser desorption/ionization mass spectrometry fingerprinting. We found that soot particles from different origins and with different morphologies showed highly consistent mass spectral fingerprints deriving from peak ratios of small carbon cluster anions (C2--C10-), which enabled both accurate quantification of soot in fine particulate matter (PM2.5) samples and label-free imaging of soot particles in biological media. By using this technique, we tracked and imaged the suborgan distribution of soot particles in mice after exposure to PM2.5. The results showed that the lung is the main target organ for short-term inhalation exposure to soot particles. This study helps to better understand the inhalation toxicology of soot and also provides a practical novel methodological platform for identification, tracing, and toxicological studies of elemental carbon-based nanomaterials.

Making complex measurements of meat composition fast: Application of rapid evaporative ionisation mass spectrometry to measuring meat quality and fraud

Increasing demands are being placed on meat producers to verify more about their product with regards to safety, quality and authenticity. There are many methods that can detect aspects of these parameters in meat, yet most are too slow to keep up with the demands of modern meat processing plants and supply chains. A new technology, Rapid Evaporative Ionisation Mass Spectrometry (REIMS), has the potential to bridge the gap between advanced laboratory measurements and technology that can screen for quality, safety and authenticity parameters in a single measurement. Analysis with REIMS generates a detailed mass spectral fingerprint representative of a meat sample without the need for sample processing. REIMS has successfully been used to detect species fraud, detect use of hormones in meat animals, monitor meat processing and to detect off flavours such as boar taint. The aim of this review is to summarize these and other applications to highlight the potential of REIMS for meat analysis. Sampling methods and important considerations for data analysis are discussed as well as limitations of the technology and remaining challenges for practical adoption.

Convolutional neural network for preprocessing‐free bacterial Spectra identification

AbstractIdentifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is a tedious and labor‐intensive process. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real‐time identification without human experts, it still requires data preprocessing. To address this issue, we proposed a unified solution for the identification of bacterial species with a convolutional neural network. The neural network automatically determined species according to their mass spectra without the preprocessing steps. The convolutional and pooling layers in the neural network could replace the binning, baseline correction, and scaling procedures. Moreover, because of the explainable structure, the model could identify important regions of spectra to discriminate each bacterial species. We used spectral samples obtained from the fatty acid methyl esters of 10 samples from 16 bacterial species (a total of 16) to demonstrate the usefulness of the proposed method by comparing it with existing classification methods preceded by preprocessing. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. Moreover, the classification results of the proposed method are interpretable.

Evaluation of the Deterioration State of Archaeological Wooden Artifacts: A Nondestructive Protocol based on Direct Analysis in Real Time - Mass Spectrometry (DART-MS) Coupled to Chemometrics.

Evaluating the deterioration state of archeological wood is obligatory before the preservation of archeological wooden artifacts. Herein, a nondestructive, accurate, and rapid methodology is first developed via direct analysis in real time-mass spectrometry (DART-MS) with chemometrics to classify archeological wood and recent wood into 3 groups according to their deterioration states. As water in wooden artifacts probably affected the ion fragmentation process during DART-MS, ions responsible for evaluating the deterioration state were separately screened toward waterlogged archeological wood and dried archeological wood by partial least-squares discriminant analysis (PLS-DA). The well-defined separation of severely decayed archeological wood, moderately decayed archeological wood and recent wood was revealed in PLS-DA models. Twenty and 27 wood fragment ions were further screened as key variables to evaluate the deterioration state of waterlogged archeological wood and dried archeological wood, respectively. They were tentatively identified as ions of lignin monomeric compositions, lignin dimers, lignin trimers, and oligosaccharides. Results strongly suggested that differences in the structure and relative abundances of wood cell wall components accounts for the evaluation of deterioration state by DART-MS coupled to chemometrics. PLS-DA models provided R2Y = 0.836, Q2 = 0.817, and R2Y = 0.754, Q2 = 0.682 were then established separately using mass spectral fingerprints of respective potential predictive wood fragment ions. Furthermore, archeological woods, consisting of Castanopsis, Quercus, Idesia, Populus, and Cunninghamia species and with an average MWC range of 103-465%, were used as an external validation set and evaluated with the methodology developed herein and the MWC criteria. Results showed that DART-MS coupled to chemometrics could accurately predict the inhomogeneous deterioration states of archeological wooden artifacts and avoid the interference of inorganic deposits, in comparison with the MWC criteria.

Characterization of Maca (Lepidium meyenii/Lepidium peruvianum) Using a Mass Spectral Fingerprinting, Metabolomic Analysis, and Genetic Sequencing Approach.

Maca (Lepidium meyenii, synonym L.peruvianum) was analyzed using a systematic approach employing principal component analysis of flow injection mass spectrometry fingerprints (no chromatographic separation) to guide the selection of samples for metabolite profiling and DNA next generation sequencing. Samples consisted of 39 commercial maca supplements from 11 manufacturers, 31 unprocessed maca tubers grown in Peru and China, and a historic non-tuber maca sample from Peru. Principal component analysis of flow injection mass spectrometry fingerprints initially placed all the maca samples in three classes with similar chemical composition: commercial maca samples, tubers grown in Peru, and tubers grown in China. Metabolite profiling identified 67 compounds in the negative mode and 51 compounds in the positive mode. Compounds identified by metabolite profiling (macamides, glucosinolates, amino acids, fatty acids, polyunsaturated fatty acids, saccharides, imidazoles) were then used to identify ions in the flow injection mass spectrometry fingerprints. The tuber fingerprints were analyzed by factorial multivariate analysis of variance revealing that black, red, and yellow maca from Peru and black and yellow maca from China were compositionally different with respect to color and country. Critical ions were identified that allowed for the differentiation of maca between colors from the same country or between two countries with the same color. Genetically, all samples were confirmed to be L.meyenii based on next generation sequencing at three gene regions (ITS2, psbA, and trnL) and comparison to recorded sequences of vouchered standards.

Integration of mass spectral fingerprinting analysis with precursor ion (MS1) quantification for the characterisation of botanical extracts: application to extracts of Centella asiatica (L.) Urban.

IntroductionThe phytochemical composition of plant material governs the bioactivity and potential health benefits as well as the outcomes and reproducibility of laboratory studies and clinical trials.ObjectiveThe objective of this work was to develop an efficient method for the in‐depth characterisation of plant extracts and quantification of marker compounds that can be potentially used for subsequent product integrity studies. Centella asiatica (L.) Urb., an Ayurvedic herb with potential applications in enhancing mental health and cognitive function, was used as a case study.MethodsA quadrupole time‐of‐flight analyser in conjunction with an optimised high‐performance liquid chromatography (HPLC) separation was used for in‐depth untargeted fingerprinting and post‐acquisition precursor ion quantification to determine levels of distinct phytochemicals in various C. asiatica extracts.ResultsWe demonstrate the utility of this workflow for the characterisation of extracts of C. asiatica. This integrated workflow allowed the identification or tentative identification of 117 compounds, chemically interconnected based on Tanimoto chemical similarity, and the accurate quantification of 24 phytochemicals commonly found in C. asiatica extracts.ConclusionWe report a phytochemical analysis method combining liquid chromatography, high resolution mass spectral data acquisition, and post‐acquisition interrogation that allows chemical fingerprints of botanicals to be obtained in conjunction with accurate quantification of distinct phytochemicals. The variability in the composition of specialised metabolites across different C. asiatica accessions was substantial, demonstrating that detailed characterisation of plant extracts is a prerequisite for reproducible use in laboratory studies, clinical trials and safe consumption. The methodological approach is generally applicable to other botanical products.

Fast Confirmation of Antibody Identity by MALDI-TOF MS Fingerprints.

Thousands of antibodies for diagnostic and other analytical purposes are on the market. However, it is often difficult to identify duplicates, reagent changes, and to assign the correct original publications to an antibody. This slows down scientific progress and might even be a cause of irreproducible research and a waste of resources. Recently, activities were started to suggest the sole use of recombinant antibodies in combination with the open communication of their sequence. In this case, such uncertainties should be eliminated. Unfortunately, this approach seems to be rather a long-term vision since the development and manufacturing of recombinant antibodies remain quite expensive in the foreseeable future. Nearly all commercial antibody suppliers also may be reluctant to publish the sequence of their antibodies, since they fear counterfeiting. De novo sequencing of antibodies is also not feasible today for a reagent user without access to the hybridoma clone. Nevertheless, it seems to be crucial for any scientist to have the opportunity to identify an antibody undoubtedly to guarantee the traceability of any research activity using antibodies from a third party as a tool. For this purpose, we developed a method for the identification of antibodies based on a MALDI-TOF MS fingerprint. To circumvent lengthy denaturation, reduction, alkylation, and enzymatic digestion steps, the fragmentation was performed with a simple formic acid hydrolysis step. Eighty-nine unknown monoclonal antibodies were used for this study to examine the feasibility of this approach. Although the molecular assignment of peaks was rarely possible, antibodies could be easily recognized in a blinded test, simply from their mass-spectral fingerprint. A general protocol is given, which could be used without any optimization to generate fingerprints for a database. We want to propose that, in most scientific projects relying critically on antibody reagents, such a fingerprint should be established to prove and document the identity of the used antibodies, as well as to assign a specific reagent to a datasheet of a commercial supplier, public database record, or antibody ID.

Detection of milk powder in liquid whole milk using hydrolyzed peptide and intact protein mass spectral fingerprints coupled with data fusion technologies.

Detection of the presence of milk powder in liquid whole milk is challenging due to their similar chemical components. In this study, a sensitive and robust approach has been developed and tested for potential utilization in discriminating adulterated milk from liquid whole milk by analyzing the intact protein and hydrolyzed peptide using ultra‐performance liquid chromatography with quadrupole time‐of‐flight mass spectrometer (UPLC‐QTOF‐MS) fingerprints combined with data fusion. Two different datasets from intact protein and peptide fingerprints were fused to improve the discriminating ability of principle component analysis (PCA). Furthermore, the midlevel data fusion coupled with PCA could completely distinguish liquid whole milk from the milk. The limit of detection of milk powder in liquid whole milk was 0.5% (based on the total protein equivalence). These results suggested that fused data from intact protein and peptide fingerprints created greater synergic effect in detecting milk quality, and the combination of data fusion and PCA analysis could be used for the detection of adulterated milk.

HS-SPME-MS-Enose Coupled with Chemometrics as an Analytical Decision Maker to Predict In-Cup Coffee Sensory Quality in Routine Controls: Possibilities and Limits.

The quality assessment of the green coffee that you will go to buy cannot be disregarded from a sensory evaluation, although this practice is time consuming and requires a trained professional panel. This study aims to investigate both the potential and the limits of the direct headspace solid phase microextraction, mass spectrometry electronic nose technique (HS-SPME-MS or MS-EN) combined with chemometrics for use as an objective, diagnostic and high-throughput technique to be used as an analytical decision maker to predict the in-cup coffee sensory quality of incoming raw beans. The challenge of this study lies in the ability of the analytical approach to predict the sensory qualities of very different coffee types, as is usual in industry for the qualification and selection of incoming coffees. Coffees have been analysed using HS-SPME-MS and sensory analyses. The mass spectral fingerprints (MS-EN data) obtained were elaborated using: (i) unsupervised principal component analysis (PCA); (ii) supervised partial least square discriminant analysis (PLS-DA) to select the ions that are most related to the sensory notes investigated; and (iii) cross-validated partial least square regression (PLS), to predict the sensory attribute in new samples. The regression models were built with a training set of 150 coffee samples and an external test set of 34. The most reliable results were obtained with acid, bitter, spicy and aromatic intensity attributes. The mean error in the sensory-score predictions on the test set with the available data always fell within a limit of ±2. The results show that the combination of HS-SPME-MS fingerprints and chemometrics is an effective approach that can be used as a Total Analysis System (TAS) for the high-throughput definition of in-cup coffee sensory quality. Limitations in the method are found in the compromises that are accepted when applying a screening method, as opposed to human evaluation, in the sensory assessment of incoming raw material. The cost-benefit relationship of this and other screening instrumental approaches must be considered and weighed against the advantages of the potency of human response which could thus be better exploited in modulating blends for sensory experiences outside routine.

Timber species identification from chemical fingerprints using direct analysis in real time (DART) coupled to Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS): comparison of wood samples subjected to different treatments

Abstract Timber genus identification based on the anatomical features of wood is well established in botany. However, species-level wood identification is not always possible based on traditional wood morphology techniques alone. To compensate for the deficiencies of traditional methods, direct analysis in real time coupled to Fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS) was used to obtain the mass spectral fingerprints of different timber species. Using heartwood samples of two morphologically similar species, Pterocarpus santalinus and Pterocarpus tinctorius, subjected to different treatments, i.e. solvent extractions and powdered samples as well as air-dried samples and samples dried at low and high temperatures, we observed distinct chemical signatures for the wood samples from the two species, enabling rapid species-level identification when multivariate statistical analysis was adopted. The supervised orthogonal partial least squares discriminant analysis (OPLS-DA) models for samples subjected to different treatments all exhibited accurate differentiation performance of the explained fraction of variance of classes (R2Y = 0.936–0.987) and the cross-validated fraction of variance of classes (Q2 = 0.857–0.949). Compared with solvent types and the physical form of the sample, the drying treatment method had a greater impact on the chemical fingerprint from DART-FTICR-MS. Air-dried wood chips were the optimal samples for the DART-FTICR-MS method coupled with statistical analysis.

Toward a rapid method for the study of biodiversity in cold environments: the characterization of psychrophilic yeasts by MALDI-TOF mass spectrometry.

To investigate the potential of matrix-assisted laser desorption ionization mass spectrometry (MALDI-TOF/MS) as a platform to support biodiversity and phylogenetic studies of psychrophilic yeasts in cold environments, the technique was employed to rapidly characterize and distinguish three psychrophilic yeasts (Rhodotorula mucilaginosa, Naganishia vishniacii, and Dioszegia cryoxerica) from three mesophilic counterparts (Saccharomyces cerevisiae Cry Havoc, S. cerevisiae California V Ale, and S. pastorianus). A detailed workflow for providing reproducible mass spectral fingerprints of low molecular weight protein/peptide features specific to the organisms studied is presented. The potential of this approach as a tool in the study of biodiversity, systematics, and phylogeny of psychrophilic microorganisms is highlighted.