NMR-based metabolomics for investigating urinary profiles of metal carpentry workers exposed to welding fumes and volatile organic compounds.

Introduction: Metal carpentry includes a wide range of work activities such as welding and cutting metallic components, use of solvents and paints. Therefore, the employees in these types of activities are mainly exposed to welding fumes and volatile organic solvents. Here, we present an NMR-based metabolomic approach for assessing urinary profiles of workers in the same company that are exposed to two different risk factors. Methods: The study enrolled 40 male subjects exposed to welding fumes, 13 male subjects exposed to volatile organic compounds of a metal carpentry company, and 24 healthy volunteers. All samples were collected, in the middle of the working week at fast. Thirty-five urinary metabolites belonging to different chemical classes such as amino acids, organic acids and amines were identified and quantified. Results were processed by multivariate statistical analysis for identifying significant metabolites for each working group examined, compared to controls. Results: Workers exposed to welding fumes displayed urinary increase in glutamine, tyrosine, taurine, creatine, methylguanidine and pseudouridine associated to oxidative impairment, while workers exposed to volatile organic compounds showed higher urinary levels of branched chain aminoacids. Conclusion: Our work identified specific urinary profile related to each occupational exposure, even if it is below the threshold limit values.

Development of partial least squares regression with discriminant analysis for software bug prediction.

Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both. However, there is still no universal best solution to this problem, as most suitable features and models vary from dataset to dataset and depend on the context in which we use them. Therefore, novel approaches and further studies on this topic are highly necessary. In this paper, we employ a chemometric approach - Partial Least Squares with Discriminant Analysis (PLS-DA) - for predicting bug prone Classes in Java programs using static source code metrics. PLS-DA is successfully applied within the field of chemometrics, but to our best knowledge, it has never been used before in the software maintenance domain for predicting bugs. In addition, we have used rigorous statistical treatments and evaluation for representing the software engineering results. We show that our PLS-DA based prediction model achieves superior performances compared to the state-of-the-art approaches (i.e. F-measure of 0.44-0.47 at 90% confidence level) when no data re-sampling applied and comparable to others when applying up-sampling on the largest open bug dataset, while training the model is significantly faster, thus finding optimal parameters is much easier. In terms of completeness, which measures the amount of bugs contained in the Java Classes predicted to be defective, PLS-DA outperforms every other algorithm: it found 69.3% and 79.4% of the total bugs with no re-sampling and up-sampling, respectively.

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm.

Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Elemental analysis and metabolic profiling of medicinally potent members of Zingiberaceae family using FT-IR and LIBS coupled with PLS-DA.

The role of organic and inorganic elemental profiles in the growth, development, and secondary metabolite synthesis of plants is crucial, particularly concerning their medicinal value. However, comprehensive studies addressing both aspects are scarce. Hence, the present manuscript aims to investigate the potential use of Fourier transform infrared spectroscopy (FT-IR) and laser-induced breakdown spectroscopy (LIBS) techniques to obtain the functional groups and organic and inorganic elemental profiles of significant medicinal plants belonging to the Zingiberaceae family collected from two different geographic regions in India. The FT-IR analysis of the methanolic extracts shows the presence of aliphatic and aromatic alcohols, esters, ethers, carboxyl compounds, and their derivatives. In LIBS analysis, the spectral characteristics of atomic and molecular species present in the samples were observed, encompassing both organic and inorganic elements. The presence of heavy metals and trace elements have also been observed in the LIBS spectra of the samples. Furthermore, partial least squares discriminant analysis (PLS-DA) has been used to obtain classification pattern of the samples based on their spectral fingerprints. This study not only helps in reflecting the significance of micronutrients in aiding secondary metabolism thus enhancing the medicinal properties of plants, but also enables the identification of trace elements within plants. This facilitates the determination of the suitable usage and dosage of particular plant components, contributing to the research goal of establishing pharmacological and nutraceutical significance. This study is imperative as it fills a critical gap in research, although further work in this direction is warranted.

Characterization of the flavor profile of UHT milk during shelf-life via volatile metabolomics fingerprinting combined with chemometrics.

Ultra-high temperature (UHT) milk is popular among consumers. However, its flavor and texture change in its shelf life. Flavor is highly determinative for the success of dairy products and for consumers' willingness to buy. It is important to milk producers to ensure the optimal flavor of their products in the shelf life. In order to be able to control and predict the flavor quality of UHT milk during the shelf life, this study compared the variations in sensory quality, volatile aroma release and backbone flavor factors and developed a discriminant model to assess flavor quality based on flavouromics data of five competing milk sample during storage. Using partial least squares discriminant analysis (PLS-DA) with Electronic-nose (E-nose) data excellent classification sensitivity and specificity were achieved compared to models based on gas chromatography-mass spectrometry (GC-MS) data. The PLS-DA model using E-nose data exhibited a 100% correct classification rate for the storage period, and a 92% correct rate based on the eight variable importance in the projection (VIP) elements screened for volatile components from different groups. The discriminative model developed herein based on E-nose combined with chemometrics demonstrated advantages such as speed, efficiency, and environmental friendliness. This method shows promise as a precise tool for analyzing aroma changes in UHT milk during its shelf life, and provide support for controlling the flavor substances and milk product development.

Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds.

Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400-1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.

Metabolism changes caused by glucose in normal and cancer human brain cell lines by Raman imaging and chemometric methods.

Glucose is the main source of energy for the human brain. This paper presents a non-invasive technique to study metabolic changes caused by glucose in human brain cell lines. In this paper we present the spectroscopic characterization of human normal brain (NHA; astrocytes) and human cancer brain (CRL-1718; astrocytoma and U-87 MG; glioblastoma) control cell lines and cell lines upon supplementation with glucose. Based on Raman techniques we have identified biomarkers that can monitor metabolic changes in lipid droplets, mitochondria and nucleus caused by glucose. We have studied the vibrations at 750 cm-1, 1444 cm-1, 1584 cm-1 and 1656 cm-1 as a function of malignancy grade. We have compared the concentration of cytochrome, lipids and proteins in the grade of cancer aggressiveness in normal and cancer human brain cell lines. Chemometric analysis has shown that control normal, control cancer brain cell lines and normal and cancer cell lines after supplementation with glucose can be distinguished based on their unique vibrational properties. PLSDA (Partial Least Squares Discriminant Analysis) and ANOVA tests have confirmed the main role of cytochromes, proteins and lipids in differentiation of control human brain cells and cells upon supplementation with glucose. We have shown that Raman techniques combined with chemometric analysis provide additional insight to monitor the biology of astrocytes, astrocytoma and glioblastoma after glucose supplementation.

Correlation between physicochemical properties, flavor characteristics and microbial community structure in Dushan shrimp sour paste.

Dushan shrimp sour paste (DSSP), a traditional Guizhou condiment, and its unique flavor is determined by the fermentation microbiota. However, the relationship between the microbiota structure and its flavor remains unclear. This study identified 116 volatile flavor compounds using electronic nose and headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GC-MS) techniques, of which 19 were considered as key flavor compounds, mainly consisting of 13 esters and 1 alcohol. High-throughput sequencing technique, the bacterial community structure of nine groups of DSSPs was determined. Further analysis revealed Vagococcus, Lactococcus, and Tepidimicrobium as key bacteria involved in flavor formation. This study contributes to our understanding of the relationship between bacterial communities and the flavor formation, and provides guidance for screening starter culture that enhance the flavor of DSSP in industrial production.

Comparative Analysis of Texture Characteristics, Sensory Properties, and Volatile Components in Four Types of Marinated Tofu.

In this study, three different brands of commercially available marinated tofu were analyzed and compared with homemade products to explore the effect of key flavor substances on their sensory quality, sensory properties, texture characteristics, and volatile components. The texture characteristics and flavor substances of the three brands of commercially available marinated tofu were significantly different from those of homemade products. A total of 64 volatile components were identified by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), mainly including 11 hydrocarbons, 11 alcohols, 10 ketones, 15 aldehydes, 4 esters, 1 acid, and 12 other volatile substances. Among these, nine key flavor compounds (ROAV > 1, VIP > 1) were identified using the relative odor activity value (ROAV) combined with a partial least squares discriminant analysis (PLS-DA) and variable importance in projection, including α-Pinene, ß-Myrcene, α-Phellandrene, 1-Penten-3-one, Butanal, 3-Methyl butanal, acetic acid ethyl ester, 1,8-Cineol, and 2-Pentyl furan. The correlation heatmap showed that sensory evaluation was positively correlated with hardness, gumminess, chewiness, and springiness while negatively correlated with 2-Pentyl furan, α-Pinene, resilience, α-Phellandrene, 1-Penten-3-one, acetic acid ethyl ester, and 1,8-Cineol. Overall, this study provides a theoretical reference for developing new instant marinated tofu snacks.

Metabolic profiling and biomarkers identification in cluster bean under drought stress using GC-MS technique.

INTRODUCTION: The Cluster bean is an economically significant annual legume, widely known as guar. Plant productivity is frequently constrained by drought conditions. OBJECTIVE: In this work, we have identified the untargeted drought stress-responsive metabolites in mature leaves of cluster beans under drought and control condition. METHODS: To analyse the untargeted metabolites, gas chromatography-mass spectrometry (GC-MS) technique was used. Supervised partial least-squares discriminate analysis and heat map were used to identify the most significant metabolites for drought tolerance. RESULTS: The mature leaves of drought-treated C. tetragonoloba cv. 'HG-365' which is a drought-tolerant cultivar, showed various types of amino acids, fatty acids, sugar alcohols and sugars as the major classes of metabolites recognized by GC-MS metabolome analysis. Metabolite profiling of guar leaves showed 23 altered metabolites. Eight metabolites (proline, valine, D-pinitol, palmitic acid, dodecanoic acid, threonine, glucose, and glycerol monostearate) with VIP score greater than one were considered as biomarkers and three metabolite biomarkers (D-pinitol, valine, and glycerol monostearate) were found for the first time in guar under drought stress. In this work, four amino acids (alanine, valine, serine and aspartic acid) were also studied, which played a significant role in drought-tolerant pathway in guar. CONCLUSION: This study provides information on the first-ever GC-MS metabolic profiling of guar. This work gives in-depth details on guar's untargeted drought-responsive metabolites and biomarkers, which can plausibly be used for further identification of biochemical pathways, enzymes, and the location of various genes under drought stress.

Different stages of flavor variations among canned Antarctic krill (Euphausia superba): Based on GC-IMS and PLS-DA.

The aim of the present study was to explore changes in the profile of volatile compounds (VCs) in canned Antarctic krill (Euphausia superba) at different processing stages using partial least squares discriminant analysis (PLS-DA) and gas chromatography-mass spectrometry (GC-IMS). A total of 43 VCs were detected using GC-IMS in all krill meat samples, which included mainly alcohols, aldehydes, ketones, esters, and furans. Considering the different processing stages, the highest variation in VCs and the highest VC content were observed in krill meat which underwent both blanching and salt addition. PLS-DA further revealed flavor differences in canned Antarctic krill meat at different processing stages, with octanal, 2-hexanol, 2-octane, 2,3,5-trimethyl pyrazine, and cis-3-hexanol as the main contributors to observed differences in VC profiles. These findings contribute to the production of high-quality canned krill meat, enhancing its flavor quality and providing a feasible theoretical basis for future krill meat pretreatment and industry development.

The difference analysis of physicochemical indexes and volatile flavor compounds of chili oil prepared from different varieties of chili pepper.

Because of its peculiar flavor, chili oil is widely used in all kinds of food and is welcomed by people. Chili pepper is an important raw material affecting its quality, and commercial chili oil needs to meet various production needs, so it needs to be made with different chili peppers. However, the current compounding method mainly relies on the experience of professionals and lacks the basis of objective numerical analysis. In this study, the chroma and capsaicinoids of different chili oils were analyzed, and then the volatile components were determined by gas chromatography-mass spectrometry (GC-MS) and gas chromatography-ion migration spectrometer (GC-IMS) and electronic nose (E-nose). The results showed that Zidantou chili oil had the highest L*, b*, and color intensity (ΔE) (52.76 ± 0.52, 88.72 ± 0.89, and 118.84 ± 1.14), but the color was tended to be greenyellow. Xinyidai chili oil had the highest a* (65.04 ± 0.2). But its b* and L* were relatively low (76.17 ± 0.29 and 45.41 ± 0.16), and the oil was dark red. For capsaicinoids, Xiaomila chili oil had the highest content of capsaicinoids was 2.68 ± 0.07 g/kg, Tianjiao chili oil had the lowest content of capsaicinoids was 0.0044 ± 0.0044 g/kg. Besides, 96 and 54 volatile flavor substances were identified by GC-MS and GC-IMS respectively. And the main volatile flavor substances of chili oil were aldehydes, alcohols, ketones, and esters. A total of 11 key flavor compounds were screened by the relative odor activity value (ROAV). Moguijiao chili oil and Zidantou chili oil had a prominent grass aroma because of hexanal, while Shizhuhong chili oil, Denglongjiao chili oil, Erjingtiao chili oil, and Zhoujiao chili oil had a prominent floral aroma because of 2, 3-butanediol. Chili oils could be well divided into 3 groups by the partial least squares discriminant analysis (PLS-DA). According to the above results, the 10 kinds of chili oil had their own characteristics in color, capsaicinoids and flavor. Based on quantitative physicochemical indicators and flavor substances, the theoretical basis for the compounding of chili oil could be provided to meet the production demand more scientifically and accurately.

Visible/near-infrared hyperspectral imaging combined with machine learning for identification of ten Dalbergia species.

Introduction: This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade. Methods: We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance. Results: With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power. Discussion: These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

Characterization of Near Infrared-Dye Colored Fabrics Using Hyperspectral Imaging.

Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications.

Near-Infrared Spectroscopy for Rapid Differentiation of Fresh and Frozen-Thawed Common Carp (Cyprinus carpio).

This study aimed to investigate near-infrared spectroscopy (NIRS) in combination with classification methods for the discrimination of fresh and once- or twice-freeze-thawed fish. An experiment was carried out with common carp (Cyprinus carpio). From each fish, test pieces were cut from the dorsal and ventral regions and measured from the skin side as fresh, after single freezing at minus 18 °C for 15 ÷ 28 days and 15 ÷ 21 days for the second freezing after the freeze-thawing cycle. NIRS measurements were performed via a NIRQuest 512 spectrometer at the region of 900-1700 nm in Reflection mode. The Pirouette 4.5 software was used for data processing. SIMCA and PLS-DA models were developed for classification, and their performance was estimated using the F1 score and total accuracy. The predictive power of each model was evaluated for fish samples in the fresh, single-freezing, and second-freezing classes. Additionally, aquagrams were calculated. Differences in the spectra between fresh and frozen samples were observed. They might be assigned mainly to the O-H and N-H bands. The aquagrams confirmed changes in water organization in the fish samples due to freezing-thawing. The total accuracy of the SIMCA models for the dorsal samples was 98.23% for the calibration set and 90.55% for the validation set. For the ventral samples, respective values were 99.28 and 79.70%. Similar accuracy was found for the PLS-PA models. The NIR spectroscopy and tested classification methods have a potential for nondestructively discriminating fresh from frozen-thawed fish in as methods to protect against fish meat food fraud.

Investigating the Effects of Distillation System, Geographical Origin, and Aging Time on Aroma Characteristics in Brandy Using an Untargeted Metabonomic Approach.

In this study, the influence of the distillation system, geographical origin, and aging time on the volatiles of brandy was investigated. An untargeted metabolomics approach was used to classify the volatile profiles of brandies based on the presence of different distillation systems and geographical origins. Through the predictive ability of PLS-DA models, it was found that higher alcohols, C13-norisopenoids, and furans could serve as key markers to discriminate between continuous stills and pot stills, and the contents of C6/C9 compounds, C13-norisoprenoids, and sesquiterpenoids were significantly affected by brandy origin. A network analysis illustrated that straight-chain fatty acid ethyl esters gradually accumulated during aging, and several higher alcohols, furfural, 5-methylfurfural, 4-ethylphenol, TDN, ß-damascenone, naphthalene, styrene, and decanal were also positively correlated with aging time. This study provides effective methods for distinguishing brandies collected from different distillation systems and geographical origins and summarizes an overview of the changes in volatile compounds during the aging process.

Comparative profiling of serum biomarkers and ATR-FTIR spectroscopy for differential diagnosis of patients with rheumatoid and psoriatic arthritis - a pilot study.

BACKGROUND: Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic inflammatory diseases in which innate and adaptive responses of the immune system are induced. RA and PsA have complex signaling pathways. Despite the differences in their clinical presentation, there is a great demand for fast and accurate diagnosis of diseases to implement treatment and plan an individual therapeutic strategy quickly. In this report, we present the results of differential diagnosis of patients with RA and PsA and healthy subjects (C, control group), allowing for reliable differentiation of groups of rheumatoid patients based on biochemical parameters, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, and combined data sets. MATERIALS AND METHODS: Biochemical analyses, ELISA (enzyme-linked immunosorbent assays), and multiplex assays were conducted for blood sera from patients with RA (n = 32), patients with PsA (n = 28), and the control group (n = 18). ATR-FTIR spectra were collected for lyophilized sera. RESULTS: The combination of six biochemical parameters (WBC, ESR, RF, CRP, HCC-4/CCL16, and HMGB1/HMGB) allowed the development of the partial least squares discriminant analysis (PLS-DA) model with an overall accuracy (OA) of 80% for test samples. The best separation between RA, PsA, and the control group was obtained utilizing spectral data. Using the interval PLS algorithm (iPLS) specific spectral ranges were selected and a classifier characterized by OA value for test set equal to 88% was obtained. This parameter, for the hybrid PLS-DA model constructed using selected biochemical parameters and a significantly reduced number of spectral variables, reached the level of 84%. CONCLUSIONS: PLS-DA models developed on the basis of spectral data enable effective differentiation of patients with RA, patients with PsA, and healthy subjects. They appeared to be insensitive to existing inflammation processes which opens interesting perspectives for new diagnostic tests and algorithms for identification of patients with RA and PsA.

An untargeted analytical workflow based on Kendrick mass defect filtering reveals dysregulations in acylcarnitines in prostate cancer tissue.

BACKGROUND: Metabolomics is nowadays considered one the most powerful analytical for the discovery of metabolic dysregulations associated with the insurgence of cancer, given the reprogramming of the cell metabolism to meet the bioenergetic and biosynthetic demands of the malignant cell. Notwithstanding, several challenges still exist regarding quality control, method standardization, data processing, and compound identification. Therefore, there is a need for effective and straightforward approaches for the untargeted analysis of structurally related classes of compounds, such as acylcarnitines, that have been widely investigated in prostate cancer research for their role in energy metabolism and transport and ß-oxidation of fatty acids. RESULTS: In the present study, an innovative analytical platform was developed for the straightforward albeit comprehensive characterization of acylcarnitines based on high-resolution mass spectrometry, Kendrick mass defect filtering, and confirmation by prediction of their retention time in reversed-phase chromatography. In particular, a customized data processing workflow was set up on Compound Discoverer software to enable the Kendrick mass defect filtering, which allowed filtering out more than 90 % of the initial features resulting from the processing of 25 tumoral and adjacent non-malignant prostate tissues collected from patients undergoing radical prostatectomy. Later, a partial least square-discriminant analysis model validated by repeated double cross-validation was built on the dataset of 74 annotated acylcarnitines, with classification rates higher than 93 % for both groups, and univariate statistical analysis helped elucidate the individual role of the annotated metabolites. SIGNIFICANCE: Hydroxylation of short- and medium-chain minor acylcarnitines appeared to be a significant variable in describing tissue differences, suggesting the hypothesis that the neoplastic growth is linked to oxidation phenomena on selected metabolites and reinforcing the need for effective methods for the annotation of minor metabolites.

Evaluation of Dynamic Changes of Volatile Organic Components for Fishmeal during Storage by HS-SPME-GC-MS with PLS-DA.

Headspace solid-phase microextraction, combined with gas chromatography-mass spectrometry and partial least squares discriminant analysis, was adopted to study the rule of change in volatile organic compounds (VOCs) for domestic and imported fishmeal during storage with different freshness grades. The results showed that 318 kinds of VOCs were detected in domestic fishmeal, while 194 VOCs were detected in imported fishmeal. The total relative content of VOCs increased with storage time, among which acids and nitrogen-containing compounds increased significantly, esters and ketones increased slightly, and phenolic and ether compounds were detected only in domestic fishmeal. Regarding the volatile base nitrogen, acid value, pH value, and mold counts as freshness indexes, the freshness indexes were significantly correlated with nine kinds of VOCs (p < 0.05) through the correlation analysis. Among them, volatile base nitrogen had a significant correlation with VOCs containing nitrogen, acid value with VOCs containing carboxyl group and hydrocarbons, pH value with acids which could be used to adjust pH value, and mold counts with part of acids adjusting pH value and VOCs containing nitrogen. Due to the fact that the value of all freshness indexes increased with freshness degradation during storage, based on volatile base nitrogen and acid value, the fishmeal was divided into three freshness grades, superior freshness, corrupting, and completely corrupted. By using partial least squares discriminant analysis, this study revealed the differences in flavor of the domestic and imported fishmeal during storage with different freshness grades, and it identified four common characteristic VOCs, namely ethoxyquinoline, 6,7,8,9-tetrahydro-3H-benzo[e]indole-1,2-dione, hexadecanoic acid, and heptadecane, produced by the fishmeal samples during storage, as well as the characteristic VOCs of fishmeal at each freshness grade.