Comprehensive analysis of chromatographic data by using PARAFAC2 and principal components analysis

A survey of testicular texture in canine ultrasound images

Introduction

Computer-based texture analysis provides objective data that can be extracted from medical images, including ultrasound images. One popular methodology involves the generation of a gray-level co-occurrence matrix (GLCM) from the image, and from that matrix, texture fractures can be extracted.

Methods

We performed texture analysis on 280 ultrasound testicular images obtained from 70 dogs and explored the resulting texture data, by means of principal component analysis (PCA).

Results

Various abnormal lesions were identified subjectively in 35 of the 280 cropped images. In 16 images, pinpoint-to-small, well-defined, hyperechoic foci were identified without acoustic shadowing. These latter images were classified as having "microliths." The remaining 19 images with other lesions and areas of non-homogeneous testicular parenchyma were classified as "other." In the PCA scores plot, most of the images with lesions were clustered. These clustered images represented by those scores had higher values for the texture features entropy, dissimilarity, and contrast, and lower values for the angular second moment and energy in the first principal component. Other data relating to the dogs, including age and history of treatment for prostatomegaly or chemical castration, did not show clustering on the PCA.

Discussion

This study illustrates that objective texture analysis in testicular ultrasound correlates to some of the visual features used in subjective interpretation and provides quantitative data for parameters that are highly subjective by human observer analysis. The study demonstrated a potential for texture analysis in prediction models in dogs with testicular abnormalities.

Exploring the Effect of Different Storage Conditions on the Aroma Profile of Bread by Using Arrow-SPME GC-MS and Chemometrics

In the present feasibility study, SPME Arrow-GC-MS method coupled with chemometric techniques, was used for investigating the impact of two different storage conditions, namely freezing and refrigeration, on volatile organic compounds (VOCs) of different commercial breads. The SPME Arrow technology was used as it is a novel extraction technique, able to address issues arising with traditional SPME fibers. Furthermore, the raw chromatographic signals were analysed by means of a PARAFAC2-based deconvolution and identification system (PARADISe approach). The use of PARADISe approach allowed for an efficient and rapid putative identification of 38 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, and aldehydes. Additionally, Principal Component Analysis, applied on the areas of the resolved compounds, was used to investigate the effects of storage conditions on the aroma profile of bread. The results revealed that the VOC profile of fresh bread is more similar to the one of bread stored in the fridge. Furthermore, there was a clear loss of aroma intensity in frozen samples, which could be explained by phenomena related to different starch retrogradation that occurs during freezing and refrigeration. However, considering the limited number of investigated samples, this study must be considered as a proof of concept; a more statistically representative sampling and further examinations of other properties, such as bread texture, need to be performed to better understand whether samples destined for eventual analysis should be frozen or refrigerated.

Investigation of micropollutants in household waste fractions processed by anaerobic digestion: target analysis, suspect- and non-target screening

Household waste represents a major source of energy, nutrients, and recyclable material. In order to exploit benefits and avoid hazards in the context of circular economy, the risk profile towards human and the environment should be assessed. Here, we investigated the presence of micropollutants by quantitative target analysis, suspect and non-target screening and evaluated changes in the chemical fingerprint upon anaerobic digestion. Extracts were analyzed by reversed phase liquid chromatography high-resolution mass spectrometry (LC-HRMS) and gas chromatography mass spectrometry (GC-MS). Thirty-one of 51 target micropollutants were detected in low ng/mL levels except for few detections at µg/mL levels. The micropollutants quantified in this study included the following: pharmaceuticals (salicylic acid, amitriptyline, carbamazepine); biocides (triclocarban, 2-phenylphenol); industrial compounds used in, e.g., paper industry (pentachlorphenol, PFOS, PFOA, bisphenol A); aromatics, polycyclic aromatics, and heteroaromatics, and their alkylated, hydroxylated, or carboxylated analogues. Fifty of 206 compounds from the suspect screening list were tentatively identified. These included phthalates, methylparaben, phenol, benzophenone, and pharmaceuticals, e.g., ibuprofen. Most compounds detected by GC-MS decreased more than twofold in peak height or remained unaffected by the anaerobic digestion, and very few increased more than twofold, e.g., p-cresol, menthol, and octadecanal. From the LC-HRMS non-target screening analysis, 250 chemical components were resolved using the multiway curve resolution technique PARAFAC2; of these, carbidopa was the only identified unknown.

Automatic and non-targeted analysis of the volatile profile of natural and alkalized cocoa powders using SBSE-GC-MS and chemometrics

A total of 56 key volatile compounds present in natural and alkalized cocoa powders have been rapidly evaluated using a non-target approach using stir bar sorptive extraction gas chromatography mass spectrometry (SBSE-GC-MS) coupled to Parallel Factor Analysis 2 (PARAFAC2) automated in PARADISe. Principal component analysis (PCA) explained 80% of the variability of the concentration, in four PCs, which revealed specific groups of volatile characteristics. Partial least squares discriminant analysis (PLS-DA) helped to identify volatile compounds that were correlated to the different degrees of alkalization. Dynamics between compounds such as the acetophenone increasing and toluene and furfural decreasing in medium and strongly alkalized cocoas allowed its differentiation from natural cocoa samples. Thus, the proposed comprehensive analysis is a useful tool for understanding volatiles, e.g., for the quality control of cocoa powders with significant time and costs savings.

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring

The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term "annotated peak" is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations.

Signal Processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of -18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as by products of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as non-Aroclor 3, 3', dichlorobiphenyl (PCB 11) were found in many of the samples as unique source signals that describe PCB mixtures in air samples collected from Chicago, IL.

Mathematical chromatography deciphers the molecular fingerprints of dissolved organic matter

Mathematical chromatography offers information reduction and feature extraction in complex liquid chromatography—mass spectrometry datasets.

Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares-discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.

Emission of volatile organic compounds from yellow onion (Allium cepa L.) bulbs during storage

Fresh onions (Allium cepa L.) emit volatile organic compounds (VOCs) naturally in very low concentrations. The aim of the present study was to determine the emission rate of low-boiling VOCs from healthy and naturally infected onion bulbs at 4, 15, and 25 °C and to evaluate the applicability of the VOC method to monitor quality changes during 12 weeks of storage of two cultivars ('Hystand' and 'Hoza') of yellow onions. VOCs were extracted from the headspace of bulbs by solid phase micro-extraction (SPME) up to 5 times during storage and analyzed by gas chromatography-mass spectrometry (GC-MS). A total of twenty-nine compounds were measured and twenty-seven of these were identified while thirteen were reported for the first time from yellow onion bulbs. Acetone (0.10-18.0 nmol kg^-1 day^-1), dimethyl disulfide (0.12-18.9 nmol kg^-1 day^-1) and hexanal (0.05-4.40 nmol kg^-1 day^-1) were among the most abundant volatiles emitted from healthy bulbs. The concentration of these compounds as well as the total volatiles decreased with time in storage. However, microbial infection resulted in higher emission of propene, carbon disulfide, isoprene, pentane, 2-methylfuran, 3-methylfuran, 1-propenethiol, hexane, and methyl propyl sulfide, indicating that VOC emission may be used as an indicator to monitor natural senescence and decay of stored onion bulbs.

An optimized band-target entropy minimization for mass spectral reconstruction of severely co-eluting and trace-level components

Gas chromatography-mass spectrometry (GC-MS) is a versatile analytical method but its data is usually complicated by the presence of severely co-eluting and trace-level components. In this work, we introduce an optimized band-target entropy minimization approach for the analysis of complex mass spectral data. This new approach enables an automated mass spectral analysis which does not require any user-dependent inputs. Moreover, the approach provides improved sensitivity and accuracy for mass spectral reconstruction of severely co-eluting and trace-level components. The accuracy of our approach is compared to the automatic mass spectral deconvolution and identification system (AMDIS) with two controlled mixtures and a sample of Eucalyptus essential oil. Our approach was able to putatively identify 130 compounds in Eucalyptus essential oil, which was 46% in excess of that identified by AMDIS. This new approach is expected to benefit GC-MS analysis of complex mixtures such as biological samples and essential oils, in which the data are often complicated by co-eluting and trace-level components. Graphical abstract ᅟ.

Sampling methods for the study of volatile profile of PDO wine vinegars. A comparison using multivariate data analysis

High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography-mass spectrometry (GC-MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction "HS-SPME", Headspace stir bar sorptive extraction "HSSE" and Dynamic headspace extraction "DHS") were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization.

A comprehensive and comparative GC-MS metabolomics study of non-volatiles in Tanzanian grown mango, pineapple, jackfruit, baobab and tamarind fruits

Tropical fruits contribute significantly to the total fruit intake worldwide. However, their metabolomes have not yet been investigated comprehensively, as most previous studies revealed only volatile and bulk compositions. This study compares non-volatile metabolites of five fruits grown in Tanzania. A new methodology is developed for broad-spectrum GC-MS metabolomics in fruits using a new derivatization and a two dimensional peak deconvolution techniques. A total of 92 peaks were detected from fruits of which 45 were identified. Jackfruits contained the highest amount of carbohydrates, while baobab contained the highest amount of fatty acids. The highest content of organic acids was detected in tamarind. Principal component analysis revealed insights into metabolic differences and similarities, while hierarchical cluster analysis correctly grouped the fruits according to their relationships in plants' phylogenetic tree. The developed methodology could potentially be applied in large-scale studies on fruit quality, authenticity/variety, optimization of post-harvest processing and storage.

GC-MS Metabolite Profiling of Extreme Southern Pinot noir Wines: Effects of Vintage, Barrel Maturation, and Fermentation Dominate over Vineyard Site and Clone Selection

Wine is an extremely complex beverage that contains a multitude of volatile and nonvolatile compounds. This study investiged the effect of vineyard site and grapevine clone on the volatile profiles of commercially produced Pinot noir wines from central Otago, New Zealand. Volatile metabolites in Pinot noir wines produced from five grapevine clones grown on six vineyard sites in close proximity, over two consecutive vintages, were surveyed using gas chromatography-mass spectrometry (GC-MS). The raw GC-MS data were processed using parallel factor analysis (PARAFAC2), and final metabolite data were analyzed by principal component analysis (PCA). Winemaking conditions, vintage, and barrel maturation were found to be the most dominant factors. The effects of vineyard site and clone were mostly vintage dependent. Although four compounds including β-citronellol, homovanillyl alcohol, N-(3-methylbutyl)acetamide, and N-(2-phenylethyl)acetamide discriminated the vineyard sites independent of vintage, Pinot noir wines from different clones were only partially discriminated by PCA, and marker compound selection remained challenging.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Antioxidant Activity/Capacity Measurement. 3. Reactive Oxygen and Nitrogen Species (ROS/RNS) Scavenging Assays, Oxidative Stress Biomarkers, and Chromatographic/Chemometric Assays

There are many studies in which the antioxidant potential of different foods have been analyzed. However, there are still conflicting results and lack of information as a result of unstandardized assay techniques and differences between the principles of the methods applied. The measurement of antioxidant activity, especially in the case of mixtures, multifunctional or complex multiphase systems, cannot be evaluated satisfactorily using a simple antioxidant test due to the many variables influencing the results. In the literature, there are many antioxidant assays that are used to measure the total antioxidant activity/capacity of food materials. In this review, reactive oxygen and nitrogen species (ROS/RNS) scavenging assays are evaluated with respect to their mechanism, advantages, disadvantages, and potential use in food systems. On the other hand, in vivo antioxidant activity (AOA) assays including oxidative stress biomarkers and cellular-based assays are covered within the scope of this review. Finally, chromatographic and chemometric assays are reviewed, focusing on their benefits especially with respect to their time saving, cost-effective, and sensitive nature.

Feature extraction from resolution perspective for gas chromatography-mass spectrometry datasets

Automatic feature extraction from large-scale datasets is one of the major challenges when analyzing complex samples with gas chromatography-mass spectrometry (GC-MS).

Using the liquid-chromatographic-fingerprint of sterols fraction to discriminate virgin olive from other edible oils

A method to discriminate virgin olive oil from other edible vegetable oils such as, sunflower, pomace olive, rapeseed, canola, corn and soybean, applying chemometric techniques to the liquid chromatographic representative fingerprint of sterols fraction, is proposed. After a pre-treatment of the LC chromatogram data - including baseline correction, smoothing signal and mean centering - different unsupervised and supervised pattern recognition procedures, such as principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discriminant analysis (PLSDA), have been applied. From the information obtained from PCA and HCA, two groups can be clearly distinguished (virgin olive and the rest of vegetable oils tested) which have been used to discriminate between two defined classes by means of a PLSDA model. Five latent variables (LVs) explained 76.88% of X-block variance and 95.47% of the defined classes block (γ-block) variance. A root mean square error for calibration and cross validation of 0.10 and 0.22 respectively, confirmed these results and a root mean square error for prediction of 0.15 evidences that the classification model proposed presents an adequate prediction capability. The contingency table also shows the good performance of the model, proving the capability of the LC-R-FpM, to discriminate virgin olive from other vegetable edible oils.

Non-methane volatile organic compounds predict odor emitted from five tunnel ventilated broiler sheds

Non-methane volatile organic compounds (NMVOCs) emitted from mechanically ventilated poultry sheds in similar stages (32-36 d) of broiler production were measured by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS), then identified using parallel factor analysis (PARAFAC2) and the NIST11 database. Calibration models predicting odor measured by dilution olfactometry from NMVOC concentrations via orthogonal projection to latent structures (O-PLS) made good predictions (Rp(2)=0.83-0.87, RMSEp=137-175OU) using one to eight NMVOCs with either one or two latent variables representing odor concentration and character, respectively. Similar changes in odorant composition were observed in each sampling campaign, with samples collected early in the day more odorous and more sulfurous than samples collected later in the day. High litter moisture favored sulfur-containing odorants over alcohols, aldehydes and ketones but had little bearing on perceived odor, whereas high bird density favored alcohols, aldehydes and ketones over sulfur-containing odorants. Eight VOCs that were important predictors of odor across all sheds in order of decreasing importance were dimethyl sulfide (DMS), dimethyl trisulfide (DMTS), 2-3 butanedione, 3-methyl-butanal, 1-butanol, 3-methyl-1-butanol, acetoin, and 2-butanone. Four additional NMVOCs also influenced perceived odor although less predictably; these were n-hexane, 2-butanol, dimethyl disulfide (DMDS), and 1-octen-3-ol. All of the odorants are associated with microbial or fungal activity in the litter and manure, except n-hexane, which may originate from hexane-extracted soybean meal in the chicken feed. The organosulfides measured in this study may have arisen from the field sites as well as from the degradation of thiols captured on sorbent tubes during analysis by TD-GC/MS.

The use of trimethylsilyl cyanide derivatization for robust and broad-spectrum high-throughput gas chromatography-mass spectrometry based metabolomics

Reproducible and quantitative gas chromatography-mass spectrometry (GC-MS)-based metabolomics analysis of complex biological mixtures requires robust and broad-spectrum derivatization. We have evaluated derivatization of complex metabolite mixtures using trimethylsilyl cyanide (TMSCN) and the most commonly used silylation reagent N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). For the comparative analysis, two metabolite mixtures, a standard complex mixture of 35 metabolites covering a range of amino acids, carbohydrates, small organic acids, phenolic acids, flavonoids and triterpenoids, and a phenolic extract of blueberry fruits were used. Four different derivatization methods, (1) direct silylation using TMSCN, (2) methoximation followed by TMSCN (M-TMSCN), (3) direct silylation using MSTFA, and (4) methoximation followed by MSTFA (M-MSTFA) were compared in terms of method sensitivity, repeatability, and derivatization reaction time. The derivatization methods were observed at 13 different derivatization times, 5 min to 60 h, for both metabolite mixtures. Fully automated sample derivatization and injection enabled excellent repeatability and precise method comparisons. At the optimal silylation times, peak intensities of 34 out of 35 metabolites of the standard mixture were up to five times higher using M-TMSCN compared with M-MSTFA. For direct silylation of the complex standard mixture, the TMSCN method was up to 54 times more sensitive than MSTFA. Similarly, all the metabolites detected from the blueberry extract showed up to 8.8 times higher intensities when derivatized using TMSCN than with MSTFA. Moreover, TMSCN-based silylation showed fewer artifact peaks, robust profiles, and higher reaction speed as compared with MSTFA. A method repeatability test revealed the following robustness of the four methods: TMSCN > M-TMSCN > M-MSTFA > MSTFA.