Multivariate Chemometric Analysis Research Articles

GC-MS and multivariate analysis reveal partial serum metabolome restoration by bevacizumab in a colon cancer rat model: An untargeted metabolomics investigation

Bevacizumab is an anti-angiogenic therapeutic agent that targets vascular endothelial growth factor (VEGF) and has been approved for the treatment of several types of cancer, including colon cancer. Herein, a GC-MS based metabolomics approach was employed to investigate the impact of bevacizumab on the serum metabolome of colon cancer rats. Multivariate chemometric analysis models such as PCA and PLS-DA showed a clear separation between the control, cancer and bevacizumab-treated groups, suggesting that bevacizumab administration induced significant metabolic alterations. Furthermore, pairwise comparisons between the studied groups using the OPLS-DA model in addition to univariate analysis identified several discriminatory metabolites belonged to various chemical classes including amino acids, organic acids and fatty acids that were perturbed between the studied groups. Interestingly, bevacizumab treatment was able to partially restore some of the cancer-induced metabolic disturbances, indicating its potential therapeutic efficacy via improving the tumor vasculature and nutrient delivery. Besides, pathway analysis of the differential metabolites identified key metabolic pathways affected by bevacizumab, which included valine, leucine and isoleucine metabolism, pyruvate metabolism and butanoate metabolism. However, little effects were observed on lipid metabolites such as palmitic acid and stearic acid and consequently their related metabolic pathways such as fatty acid biosynthesis metabolism suggesting that bevacizumab has more prominent effect on energy and amino acid metabolisms as compared to fatty acid metabolism in colon cancer rats. Overall, our study provided novel insights into the metabolic mechanisms underlying the therapeutic effects of bevacizumab in colon cancer rats via the use of a comprehensive GC-MS metabolomics approach.

Chemometric classification of Vietnamese green tea (Camellia sinensis) varieties and origins using elemental profiling and FTIR spectroscopy

SummaryThe objective of this study was to employ multi‐elemental analysis and chemometric multivariate data analysis for identifying the geographical origin of Camellia sinensis cultivated in Vietnam's northeastern region. Ninety‐two tea leaf samples encompassing four distinct varieties were sourced from Bac Can and Thai Nguyen provinces. These samples underwent analysis through Fourier transform infrared (FTIR) spectroscopy and chemometrics, while elemental quantification was achieved via inductively coupled plasma mass spectrometry (ICP‐MS), utilising mixed standards for 26 elements. The FTIR spectra exhibited characteristic peaks, denoting the functional groups of the potential compounds present. ICP/MS analysis identified the presence of 14 elements in the tea samples, revealing significant variations in content across the four varieties. principal component analysis (PCA) of the FTIR data illustrated that the initial two principal components accounted for 91.26% of the total variance, effectively segregating the four green tea varieties into distinct clusters. Conversely, PCA of the ICP‐MS spectra also distinguished the four varieties, albeit Trung Du and Hybrid F1 exhibited a proximate distribution. This closeness may be incidental and not indicative of correlation, considering the limited variable set. Our findings ascertain that ICP/MS and FTIR, augmented by chemometric software, serve as efficacious tools for ascertaining the geographical origins of tea.

Phytochemical profiling of three Amaranthus species using LC-MS/MS metabolomic approach and chemometric tools

Several Amaranthus vegetables (Amaranthaceae) have been recognized as valuable sources of minerals, vitamins, proteins, and phytonutrients, with health-promoting characteristics. In this study, three edible Amaranthus species, namely A. hybridus (AH), A. blitum (AB), and A. caudatus (AC), were chemically characterized using non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) technique. Further, multivariate chemometric analyses were conducted, including principal component analysis (PCA) and correlation-covariance plot (C-C plot). As a result, forty-one diverse compounds were identified, which varied in distribution and abundance across the investigated species. Amino acids and flavonoid glycosides were the most prevalent metabolites. Other identified compounds comprised nucleoside, chlorogenic acids, hydroxy cinnamoyl amides, and triterpenoid saponins. The most discriminant metabolites were flavonoid glycosides and hydroxy cinnamoyl amides, giving each species a chemotaxonomic identity. Advancing the chemotaxonomy of Amaranthaceae, adenosine nucleoside and N-coumaroyl-ʟ-tryptophan were first reported from this family. Isorhamnetin and tricin glycosides were uniquely identified in AC, offering useful chemotaxonomic markers for this species. Notably, AB and AH profiles shared most metabolites, yet with varying abundance. These include adenosine, nicotiflorin, dicaffeoylquinic acids, and N-trans-feruloyl-4-O-methyldopamine. However, N-coumaroyl-ʟ-tryptophan and kaempferol dirhamnoside were exclusively found in AB, separating it from AH. In conclusion, the applied analytical techniques established molecular fingerprints for the included species, identified specific biomarkers, and investigated their interconnections.

Innovative Tools for the Nitrogen Fertilization Traceability of Organic Farming Products

In the last decades, consumers have become increasingly interested in organic products, and they strongly demand reliability in the traceability of the organic products they buy and eat. Several research methods have been developed in the last decades to study inexperienced and reliable systems and to assess the authenticity of products obtained using organic cultivation practices. The monitoring of some chemical compounds, originating from primary and/or secondary metabolism, in horticultural organic and conventional products has shown the diversity generated using the two production approaches. The difference in fertilization practices has been also shown to have an effect on the isotopic distribution of some elements, with particular reference to nitrogen. An integrated system is proposed to evaluate the validity of organic goods using collected isotopic data and other chemical and biological parameters. This approach is intended to be coupled to the application of chemometric multivariate analysis on quality and nutraceutical parameters combined with isotopic data. Indeed, this will give the opportunity to discriminate organic from conventional products based on different isotopic signatures, due to the different nitrogenous sources, combined with the qualitative profile of the crops, which are significantly affected by the different agronomic treatments. The main perspectives of the presented integrated approach, based on the combined use of chemometric and analytical tools, are linked to the feasibility of applying a reliable system for traceability. This will authenticate productions obtained using organic fertilizers (organic agriculture) with respect to those obtained with the use of synthetic fertilizers (conventional agriculture), protect and valorize virtuous farmers and support political stakeholders and decision-makers to counteract food fraud.

Identification of Turkish extra virgin olive oils produced in different regions by using NMR (1H and 13C) and IRMS (13C/12C)

AbstractIsotope ratio mass spectroscopy (IRMS) and nuclear magnetic resonance (NMR) spectroscopy techniques are two of the analytical methods that are used to characterize food products. The aim of this study is to classify extra virgin olive oil (EVOO) samples collected from different regions of Turkey based on 1H and 13C NMR spectra along with IRMS δ13C carbon isotope ratio data by using chemometrics multivariate data analysis methods. A total of 175 EVOO samples were analyzed in 2014/15 and 2015/16 harvest seasons. Multivariate classification and clustering models were used to identify geographical and botanical origins of the EVOOs. IRMS results showed that there was no significant difference in terms of δ13C values between the years in terms of harvest year (p > 0.05), only extraction phase and variety were statistically significant factors (p < 0.05). The interactions of the factors showed that the harvest year × variety interaction is important. The outcomes of this research clearly indicated that considering the partial least squares discriminant analysis result with NMR spectra, the percent success of the model in the South Marmara, North Aegean, and South Aegean region samples were 95%, 95.7%, and 96.4% in the model set, respectively. The results showed that by using classification and clustering models, geographic marking and labeling of these oils can be carried out regardless of differences in year and production systems (2 and 3 phase extraction system) according the NMR analysis.

LC–MS/MS Coupled with Chemometric Analysis as an Approach for the Differentiation of Fritillariae cirrhosae Bulbus and Fritillariae pallidiflorae Bulbus

Fritillariae cirrhosae bulbus (FCB) is one of the most important traditional Chinese medicines (TCM) for the treatment of cough and phlegm. Due to increasing demand and the complexity of FCB’s botanical origin, various substitutes have appeared in the market, resulting in a major challenge to distinguish FCB and its substitutes (F. pallidiflorae bulbus, FPB). Therefore, discriminating FCB from FPB has becoming an urgent necessity. In this study, an ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) method was developed for the simultaneous quantification of nine steroidal alkaloids (imperialine-3-β-D-glucoside, imperialine, verticine, verticinone, peimisine, yibeinoside A, delavine, delavinone, ebeidinone) within 8 min. According to the composition and content of the above nine compounds, multivariate chemometric analyses were applied for the classification of FCB and FPB. The quantitative results showed that there were both similarities and differences in the content of nine steroidal alkaloids between FCB and FPB, and it was difficult to directly distinguish these two species. Fortunately, with the aid of chemometric analyses, FCB and FPB were successfully differentiated by partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA) models based on the nine alkaloids’ content. Moreover, four compounds (yibeinoside A, ebeiedinone, delavinone and imperialine) were discovered as potential markers for the identification and differentiation of FCB and FPB. Additionally, compared to other studies, this work collected a large number of samples (49 batches of FCB and 17 batches of FPB) to ensure the reliability of the results. In conclusion, this work established a new approach for the authentication of FCB based on its active components, which provides a good reference for the quality control of FCB and will help us to understand the chemical composition differences between FCB and its adulterants further.

Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods

The development of novel platforms for non-invasive continuous glucose monitoring applied in the screening and monitoring of diabetes is crucial to improve diabetes surveillance systems. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy of urine can be an alternative as a sustainable, label-free, fast, non-invasive, and highly sensitive analysis to detect changes in urine promoted by diabetes and insulin treatment. In this study, we used ATR-FTIR to evaluate the urinary components of non-diabetic (ND), diabetic (D), and diabetic insulin-treated (D + I) rats. As expected, insulin treatment was capable to revert changes in glycemia, 24-h urine collection volume, urine creatinine, urea, and glucose excretion promoted by diabetes. Several differences in the urine spectra of ND, D, and D + I were observed, with urea, creatinine, and glucose analytes being related to these changes. Principal components analysis (PCA) scores plots allowed for the discrimination of ND and D + I from D with an accuracy of ∼ 99 %. The PCA loadings associated with PC1 confirmed the importance of urea and glucose vibrational modes for this discrimination. Univariate analysis of second derivative spectra showed a high correlation (r: 0.865, p < 0.0001) between the height of 1074 cm-1 vibrational mode with urinary glucose concentration. In order to estimate the amount of glucose present in the infrared spectra from urine, multivariate curve resolution-alternating least square (MCR-ALS) was applied and a higher predicted concentration of glucose in the urine was observed with a correlation of 78.9 % compared to urinary glucose concentration assessed using enzyme assays. In summary, ATR-FTIR combined with univariate and multivariate chemometric analyses provides an innovative, non-invasive, and sustainable approach to diabetes surveillance.

GC-MS-based untargeted metabolomics reveals the key volatile organic compounds for discriminating grades of Yichang big-leaf green tea

In this work, headspace gas chromatography coupled to mass spectrometry (HS-GC-MS) combined with multivariate statistical analysis was applied to reveal volatile markers from different grades of Yichang big-leaf green tea (YBGT). A total of 94 volatile organic compounds (VOCs) were detected and identified, which can be categorized as alkanes, terpene, aromatics, ketone, ester, alcohol, heterocyclic compounds, aldehyde, olefin, acid, amine, and nitrogen compounds. The differences between low-grade and high-grade YBGT were demonstrated by principal component analysis (PCA) and hierarchical cluster analysis (HCA). Based on orthogonal partial least squares discriminant analysis (OPLS-DA), 19 VOCs were screened as markers for the discrimination of first-grade and second-grade YBGT, and 25 VOCs were screened as markers to distinguish first-grade from third-grade YBGT. Among them, 16 VOCs are common, which can be used as characteristic markers to distinguish low-grade from high-grade YBGT. Overall, our findings indicated that there are significant differences in VOCs among different grades of YBGT, and HS-GC-MS in combination with chemometric multivariate statistical analysis can be extended as a reliable strategy for discriminating grades of other Chinese green teas.

The Influence of Microwave Treatments on Bioactive Compounds and Antioxidant Capacity of Mentha piperita L.

Microwave extraction is becoming a popular option in many fields, especially for bioactive compounds from medicinal plants. This paper addresses the application of microwaves in the process of extracting bioactive compounds (phenols, flavonoids, chlorophyll) from peppermint with antioxidant capacity in order to highlight the influence of the microwave field on the quality of the final product in comparison with the control samples. The Mentha piperita L. is a rich source of phenols. The total phenol content after applying the MW treatments significant increased and varied between 25.000 ± 1.992 and 391.687 ± 20.537 mg GAE/100 g dw compared to the untreated sample (8.089 ± 2.745 mg GAE/100 g dw). The same trend was also recorded in the case of the flavonoid and pigment content in peppermint leaves following the application of microwave treatments. The obtained results were investigated using chemometric multivariate analysis. The main purpose of our research was to compare the possibilities of total or partial substitution of conventional extraction technologies with the microwave extraction technology, and also to highlight the existing differences in the amount of total phenols and flavonoids extracted from peppermint plants in different processing conditions. Through microwave processing, a significant increase in polyphenolic compounds is obtained.

Optimization of enzyme‐assisted extraction of albedo extract obtained from watermelon rind (Citrullus lanatus) and its use in the enrichment of nutrients and bioactive compounds in watermelon juices

AbstractBackgroundAlbedo residue represents approximately 54% of the total solid residue generated during watermelon juice production, this material is an excellent source of bioactive compounds and polysaccharides. This study aimed at optimizing the enzyme‐assisted extraction (EAE) to obtain a higher yield of albedo extract using response surface methodology (RSM), having in view the enrichment of watermelon juice with this material.ResultsThe complete three‐level factorial design allowed the optimization of enzyme concentration, extraction time and temperature, leading to an optimal extraction of albedo extract at an enzyme concentration of 1.5 ml/Kg (w/w), extraction time of 45 min and temperature of 50°C. The watermelon juice enriched with 10% of albedo extract (WJ10) showed the highest polyphenolic potential and antioxidant capacity. The 13C NMR spectra confirmed the presence of homogalacturonans, xylogalacturonans and rhamnogalacturonans in the albedo extract. The watermelon juice enriched with 30% albedo extract (WJ30) obtained an increase of 47% and 216% in the content of galactose and uronic acids. HPLC‐RID analysis revealed that the addition of albedo extract in watermelon juice decreased the contents of sucrose, glucose and fructose. The multivariate chemometric analysis revealed the grouping of samples according to the bioactive composition and monosaccharide profile.ConclusionThere is a global demand for reducing organic residues in food industries, that way, the elaboration of products from residues obtained from food processing, rich in polyphenols and polysaccharides, such as albedo extract, represents a promising opportunity to develop innovative products and ingredients with high added value.

Elemental Profiling for Discrimination of Geographical Origin of Tea (Camellia sinensis) in north-east region of India by ICP-MS coupled with Chemometric techniques

The study aimed to discriminate the teas originated from eight north-east region of India using 67 elemental profiling through the use of ICP-MS and process the large data with chemometric software. Among the 67 elements, concentrations of 18 elements were significantly different in the eight regions (Darjeeling, Tripura, Upper Assam, South Bank, Terai, North Bank, Dooars and Cachar). Selected 18 elements belongs to alkali metal (Rb, Cs), alkaline earth metal (Mg, Sr, Ba), transition metal (V, Cr, Mn, Co, Ni, Zn, Mo), basic metal (Al), metalloid (B), non-metal (Se) and lanthanide (La, Ce, Nd). Multivariate chemometric analysis with PCA, LDA, SVM & OPLS DA were performed on the concentration of 18 elements on 307 authentic sample set. The correct classification rate for the training set (307) and test set (24) tea samples were 100% for SVM, LDA and OPLS DA model and confidence measure for SVM and LDA model were 99.4% and 88.97%, respectively. Variable importance in projection (VIP) values ranged between 1.04-1.51 for five elements (Cr, Mo, Rb, Mn and Ni), which can be considered as significant parameter for discrimination. The results demonstrated that ICP-MS with chemometric software could be used to discriminate the geographical origin of tea.

Metabolic Fingerprint Analysis of Cytochrome b 5-producing E. coli N4830-1 Using FT-IR Spectroscopy.

Optimization of recombinant protein expression in bacteria is an important task in order to increase protein yield while maintaining the structural fidelity of the product. In this study, we employ Fourier transform infrared (FT-IR) spectroscopy as a high throughput metabolic fingerprinting approach to optimize and monitor cytochrome b5 (CYT b5) production in Escherichia coli N4830-1, as the heterologous host. Cyt b5 was introduced as a plasmid with between 0 and 6 copies under a strong promoter. The FT-IR spectroscopy results combined with multivariate chemometric analysis illustrated discriminations among culture conditions as well as revealing features that correlated to the different cytb5 gene copy numbers. The second derivative of the FT-IR spectral data allowed for the quantitative detection of Cyt b5 directly inside the intact cells without the need for extraction, and highlighted changes in protein secondary structure that was directly correlated to the cytb5 gene copy number and protein content, and was in complete agreement with quantitative findings of standard traditional techniques such as SDS–PAGE and western blot analysis.

Comparative analysis of in vivo and in silico toxicity evaluation of the organoiodine compounds towards D.magna using multivariate chemometric approach: A study on the example of amiodarone phototransformation products

In the present study the photochemical fate of organoiodine compound – amiodarone was performed. The drug turned out to be highly susceptible to UV–Vis irradiation, especially in the presence of humic substances and organic matrix. Qualitative LC-MS analysis revealed formation of twelve – mainly previously unreported – transformation products (TPs). Four major TPs were submitted to the toxicity analysis with the use of D. magna. All of the tested TPs presented higher toxic potential than the parent compound. The phenolic TPs were approximately 100 times more toxic than amiodarone. Toxic properties of the major TPs resulted in steadily increasing toxic potential of the photo-generated mixture over the time of irradiation. Moreover, the experimental toxicity data, concerning the TPs, were compared with results estimated by 6 in silico models with the use of a multivariate chemometric analysis. The results showed that the applied computational methods were able neither to correctly predict toxic properties of the studied compounds, nor the trends in change of their toxic parameters. Additional validation of in silico models ability to predict toxicity of iodinated organic compounds showed that the studied computational methods do not present sufficient prediction ability. Therefore their estimations concerning organoiodines should be verified using experimental tests.

Forensic examination of textile fibres using Raman imaging and multivariate analysis

Vibrational spectroscopic techniques have shown to be highly suitable for the identification and comparison of textile fibres and clothing fabrics. On the other hand, new chemical imaging modes based on these spectroscopic techniques are becoming useful in multiple fields. This is particularly important to, for instance, chemically visualize and screen different samples including forensic evidence (crime scene investigation), chemical and food products (quality control), biological tissues and living beings (medical imaging), among others. This study explores the forensic examination and selective chemical visualization of textile fibres and clothing fabrics using Raman imaging. Four experiments were performed, which were focused on the screening of (i) white different materials made of 100 % cotton (gauze, cotton wool, t-shirt, and swab), (ii) polyester and cotton fabrics evidence of the same colour, (iii) five different coloured cotton fabrics, and (iv) textile fibres of different materials (acrylic, cotton, nylon, polyester, and silk). Several methods of multivariate chemometric analysis including principal component analysis (PCA), multivariate analysis of variance (MANOVA), and multivariate curve resolution (MCR) were applied to enhance the limited visual comparison of the spectra accomplished with the unaided eye. The results evidenced the suitability of Raman imaging to statistically discriminate textile fibres and fabrics due to the chemical composition of both the clothing material and the dyestuff.

Interpretable machine learning with an eye for the physics: Hyperspectral Vis/NIR “video” of drying wood analyzed by hybrid subspace modeling

Chemometric multivariate analysis based on low-dimensional linear and bilinear data modelling is presented as a fast and interpretable alternative to more fancy “AI” for practical use of Big Data streams from hyperspectral “video” cameras. The purpose of the present illustration is to find, quantify and understand the various known and unknown factors affecting the process of drying moist wood. It involves an “interpretable machine learning” that analyses more than 350 million absorbance spectra, requiring 418 GB of data storage, without the use of black box operations. The 159-channel high-resolution hyperspectral wood “video” in the 500–1005 nm range was reduced to five known and four unknown variation components of physical and chemical nature, each with its spectral, spatial and temporal parameters quantified. Together, this 9-dimensional linear model explained more than 99.98% of the total input variance.

Ketoprofen Solubility in Pure Organic Solvents Using In Situ FTIR and UV–Vis and Analysis of Solution Thermodynamics

This work investigates the use of in situ process analytical technologies, namely, attenuated total reflectance Fourier transform infrared spectroscopy (ATR FTIR) and ATR UV–vis, coupled with a robust chemometric multivariate analysis approach for the determination of the solubility of ketoprofen in organic solvents within the temperature range of 273.15–303.15 K. Principal component analysis of the raw spectral data is applied along with multiple regression analysis and a stepwise regression procedure as the chemometric approach to build inferential models able to predict solubility data. For each solvent, the constructed models are used for solubility determinations and the results are compared with those previously determined by a gravimetric method. Both in situ FTIR and UV–vis render an accurate prediction of solubility data, with a notably high number of data points enabling the subsequent thermodynamic analysis. However, the accuracy of the solubility rendered from the spectroscopic analysis is not as accurate as by the gravimetric method at low temperatures. In addition, the experimental enthalpies of solution are determined by solution calorimetry and are found to be in a range of 18.6–32.4 kJ/mol in the different solvents. The order of solubility data obtained in the solvents studied is discussed in terms of the strength of the interactions with the ketoprofen carbonyl groups analyzed by the C═O frequency of the FTIR data and based on the solution thermodynamic data obtained. All solutions of the work are relatively nonideal, with activity coefficients deviating from unity and enthalpies of mixing deviating from zero. The thermodynamic analysis and results clearly show that calorimetric enthalpies of solution are theoretically and quantitatively different from the corresponding van’t Hoff enthalpies of solution, and a detailed explanation is provided.

Determination and Quantification of Heavy Metals in Sediments through Laser-Induced Breakdown Spectroscopy and Partial Least Squares Regression

Conventional analysis techniques and sample preprocessing methods for identifying trace metals in soil and sediment samples are costly and time-consuming. This study investigated the determination and quantification of heavy metals in sediments by using a Laser-Induced Breakdown Spectroscopy (LIBS) system and multivariate chemometric analysis. Principle Component Analysis (PCA) was conducted on the LIBS spectra at the emission lines of 11 selected elements (Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si). The results showed apparent clustering of four types of sediment samples, suggesting the possibility of application of the LIBS technique for distinguishing different types of sediments. Mainly, the Cd, Cr, and Pb concentrations in the sediments were analyzed. A data-smoothing method—namely, the Savitzky–Golay (SG) derivative—was used to enhance the performance of the Partial Least Squares Regression (PLSR) model. The performance of the PLSR model was evaluated in terms of the coefficient of determination (R2), Root Mean Square Error of Calibration (RMSEC), and Root Mean Square Error of Cross Validation (RMSECV). The results obtained using the PLSR with the SG derivative were improved in terms of the R2 and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrated that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples and can play a significant role in controlling and managing the water quality of rivers.

Chemical Characteristics of Croatian Traditional Istarski pršut (PDO) Produced from Two Different Pig Genotypes

Chemical characteristics of raw and processed Istarski pršut (PDO) produced from two different pig genotypes were studied with special emphasis on amino and fatty acid composition and factors of lipid stability. Raw hams of Large White (LW)xLandrace (L), and (LWxL)xDuroc (D) pig genotypes were used in the study (20 hams of each genotype). All left raw hams from each carcass were processed in accordance with the PDO specification of Istarski pršut, and other half (the right ones) of LWxL)xD genotype were used for analyses of raw hams (fresh muscles). Istarski pršut was evaluated on the basis of the chemical parameters of the raw and matured lean ham. The process of dry curing significantly influenced the chemical properties of Istarski pršut. Despite the higher content of intramuscular fat and polyunsaturated fatty acids, the fat of (LWxL)xD ham was much more resistant to hydrolysis and oxidation, suggesting that fatty acid profile and other factors, also play a significant role. Significant differences between pig genotypes in the amino acid and fatty acid profiles were found. The analyzed Istarski pršut may be distinguished by prints of multivariate chemometric statistical analysis, based on their amino acid and fatty acid compositions.

Multivariate chemometric analysis for the evaluation of 22 Citrus fruits growing in Brazil’s semi-arid region

This work provides the first screening of 22 Citrus cultivars grown in a semi-arid tropical region based on their most relevant phytochemical and polyphenolic features through a multivariate approach. Multivariate chemometric analysis through PCA analysis were used combined to reach grouping of the Citrus fruits and unveil the main characteristics associated with the fruits identity and origin region. Citric acid was the dominant organic acid (4.43–140.54 g L−1), representing 93.9 % of total organic acids quantified in the samples. Glucose and fructose corresponded, respectively, to 51.2 and 48.8 % of the total sugars quantified in samples. Hesperidin was the predominant flavonoid identified in all Citrus, particularly in oranges, while in pomelos, naringin was the major polyphenol. The results showed that most phenolics are related to Citrus identity, as PCA revealed the grouping of samples according to the species or variety. The associations found for the Citrus obtained from the same semi-arid tropical location could be used as basis for authentication of these Citrus samples, as judged by the distinctions found between the phenolic profiles, sugars and organic acids. This could be used for other fruits upon verification of their polyphenolic association with location and climate.

MSroi: A pre-processing tool for mass spectrometry-based studies

Mass spectrometry has become one of the methods of choice in the analytical field due to its high sensitivity and selectivity to retrieve structural information allowing the univocal identification of compounds. From a chemometric point of view, mass spectrometry generates challenging datasets because of their large size with thousands of mass-to-charge values and with a high inherent complexity associated with the multicomponent mixtures analysed. These unresolved challenges have brought about the development of various computational approaches to enable the pre-processing of these mass spectrometry datasets. Here, we present the MSroi software in an attempt to provide a one-for-all solution in the compression and pre-processing of mass spectrometry datasets. MSroi allows the analysis of a variety of mass spectrometry datasets obtained using different acquisition approaches such as direct infusion, hyphenated to a separation technique or imaging. In all these cases, MSroi produces a highly compressed data table containing the measured intensities for relevant mass to charge values at each considered retention time or pixel. This output can be used as input for the feature detection step in, for instance, the ROIMCR approach, or used independently for subsequent multivariate chemometric analysis.