Detection of Sialic Acid to Differentiate Cervical Cancer Cell Lines Using a Sambucus nigra Lectin Biosensor.

Pap smear screening is a widespread technique used to detect premalignant lesions of cervical cancer (CC); however, it lacks sensitivity, leading to identifying biomarkers that improve early diagnosis sensitivity. A characteristic of cancer is the aberrant sialylation that involves the abnormal expression of α2,6 sialic acid, a specific carbohydrate linked to glycoproteins and glycolipids on the cell surface, which has been reported in premalignant CC lesions. This work aimed to develop a method to differentiate CC cell lines and primary fibroblasts using a novel lectin-based biosensor to detect α2,6 sialic acid based on attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and chemometric. The biosensor was developed by conjugating gold nanoparticles (AuNPs) with 5 µg of Sambucus nigra (SNA) lectin as the biorecognition element. Sialic acid detection was associated with the signal amplification in the 1500-1350 cm-1 region observed by the surface-enhanced infrared absorption spectroscopy (SEIRA) effect from ATR-FTIR results. This region was further analyzed for the clustering of samples by applying principal component analysis (PCA) and confidence ellipses at a 95% interval. This work demonstrates the feasibility of employing SNA biosensors to discriminate between tumoral and non-tumoral cells, that have the potential for the early detection of premalignant lesions of CC.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

Untargeted Metabolomics Analysis for Studying Differences in High-Quality Colombian Cocoa Beans.

Colombia is a producer of fine cocoa, according to the International Cocoa Organization; however, most of its exports are in the ordinary cocoa category. To remedy this situation, several national organizations are working to create technological platforms for small producers to certify the quality of their beans. The objective of this study was to identify differential chemical markers in 36 cocoa bean samples from five Colombian departments and associate them with cocoa quality properties. For this purpose, a non-targeted metabolomics approach was performed using UHPLC-HRMS, along with sensory and physicochemical analyses. The 36 samples did not differ in sensory quality, polyphenol content, and theobromine/caffeine ratio. However, the multivariate statistical analysis allowed us to differentiate the samples into four clusters. In addition, a similar grouping of the samples was also observed in the physical analyses. The metabolites responsible for such clustering were investigated with univariate statistical analysis and presumptively identified by comparison of experimental mass spectra with those reported in databases. Alkaloids, flavonoids, terpenoids, peptides, quinolines, and sulfur compounds were identified as discriminants between sample groups. Here, it was presented the metabolic profiles as an important chemical feature for further studies in quality control and more specific characterization of fine cocoa.

NMR-based metabolomics to determine the fluctuation of metabolites in hydroponic purslane crops at different harvesting times.

Purslane (Portulaca oleracea L.) has a high content of nutrients and medicinal effects that depend on the genotype, harvesting time, and production system. The objective of the present research work was to elucidate the NMR-based metabolomics profiling of three native purslane cultivars from Mexico (Xochimilco, Mixquic, and Cuautla) grown under hydroponic conditions and harvested in three different times (32, 39, and 46 days after emergence). Thirty-nine metabolites identified in the 1H NMR spectra of aerial parts of purslane, 5 sugars, 15 amino acids, 8 organic acids, 3 caffeoylquinic acids, as well as 2 alcohols and 3 nucleosides, choline, O-phosphocholine and trigonelline were also detected. A total of 37 compounds were detected in native purslane from Xochimilco and Cuautla, whereas 39 compounds were detected in purslane from Mixquic. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the cultivars into three clusters. Mixquic cultivar had the highest number of differential compounds (amino acids and carbohydrates), followed by Xochimilco and Cuautla cultivars, respectively. Changes in the metabolome were observed in latest times of harvest for all the cultivars studied. The differential compounds were glucose, fructose, galactose, pyruvate, choline, and 2-hydroxysobutyrate. The results obtained in this investigation may contribute to selecting the best cultivar of purslane and the best time in which the levels of nutrients are optimal.

Variation in phenolic, antioxidant and vitamin amounts among some medicinal plants and investigation by PCA analysis: Lamiaceae family / Variación en las cantidades de fenoles, antioxidantes y vitaminas entre algunas plantas medicinales e investigación poranálisis PCA: familia Lamiaceae

Aromatic and medicinal plants are of great importance to determine the contents of the active compounds of plant origin and to evaluate them depending on variety and climate factors in order to determine the phenolic, antioxidant enzyme activity, vitamin contents in species belonging to the Lamiaceae family. Examination of the characteristics of different species, the highest peroxidase (POD) enzyme activity, ascorbate peroxidase (AxPOD), total antioxidant (TA), malondialdehyte (MDA), caffeic acids (CA), vitamin C contents,and chloric acid (ChA) were obtained in the M. longifoliaspecies. The highest vitamin E and catalase (CAT) were determined in the S. hortensisspecies but the highest total phenolic (TP), superoxide dismutase (SOD) enzyme, hydrogen peroxide (H2O2) and chlorogenic acid (ChgA) were determined in the S. spicigeraspecies. As a result of PCA analysis, it can be said that Mentha longifolia(L.) Hudson and Satureja spicigeraspecies have significant value in terms of biochemical and phenolic content.

Las plantas aromáticas y medicinales son de gran importancia para determinar el contenido de los compuestos activos de origen vegetal y evaluarlos en función de la variedad y factores climáticos con el fin de determinar la actividad enzimática fenólica, antioxidante, contenido vitamínico en especies pertenecientes a la familia Lamiaceae. El examen de las características de diferentes especies, la mayor actividad enzimática de peroxidasa (POD), ascorbato peroxidasa (AxPOD), antioxidante total (TA), malondialdehído (MDA), ácidos cafeicos (CA), contenido de vitamina C y ácido clorhídrico (ChA) se obtuvieron en la especie M. longifolia. La mayor cantidad de vitamina E y catalasa (CAT) se determinó en la especie S. hortensis, pero la mayor cantidad total de enzima fenólica (TP), superóxido dismutasa (SOD), peróxido de hidrógeno (H2O2) y ácido clorogénico (ChgA) se determinó en la especie S. spicigera. Como resultado del análisis de PCA, se puede decir que las especies Mentha longifolia(L.) Hudson y Satureja spicigeratienen un valor significativo en términos de contenido bioquímico y fenólico.

Machine learning and analysis of genomic diversity of "Candidatus Liberibacter asiaticus" strains from 20 citrus production states in Mexico.

Background: Huanglongbing (HLB, yellow shoot disease) is a highly destructive citrus disease associated with a nonculturable bacterium, "Candidatus Liberibacter asiaticus" (CLas), which is transmitted by Asian citrus psyllid (ACP, Diaphorina citri). In Mexico, HLB was first reported in Tizimin, Yucatán, in 2009 and is now endemic in 351 municipalities of 25 states. Understanding the population diversity of CLas is critical for HLB management. Current CLas diversity research is exclusively based on analysis of the bacterial genome, which composed two regions, chromosome (> 1,000 genes) and prophage (about 40 genes). Methods and results: In this study, 40 CLas-infected ACP samples from 20 states in Mexico were collected. CLas was detected and confirmed by PCR assays. A prophage gene(terL)-based typing system (TTS) divided the Mexican CLas strains into two groups: Term-G including four strains from Yucatán and Chiapas, as well as strain psy62 from Florida, USA, and Term-A included all other 36 Mexican strains, as well as strain AHCA1 from California, USA. CLas diversity was further evaluated to include all chromosomal and prophage genes assisted by using machine learning (ML) tools to resolve multidimensional data handling issues. A Term-G strain (YTMX) and a Term-A strain (BCSMX) were sequenced and analyzed. The two Mexican genome sequences along with the CLas genome sequences available in GenBank were studied. An unsupervised ML was implemented through principal component analysis (PCA) on average nucleotide identities (ANIs) of CLas whole genome sequences; And a supervised ML was implemented through sparse partial least squares discriminant analysis (sPLS-DA) on single nucleotide polymorphisms (SNPs) of coding genes of CLas guided by the TTS. Two CLas Geno-groups, Geno-group 1 that extended Term-A and Geno-group 2 that extended Term-G, were established. Conclusions: This study concluded that: 1) there were at least two different introductions of CLas into Mexico; 2) CLas strains between Mexico and USA are closely related; and 3) The two Geno-groups provide the basis for future CLas subspecies research.

Comparison between Aliivibrio fischeri and activated sludge microorganisms in the evaluation of the toxic pollutants of leachates from Brazilian landfills.

Ecotoxicological assessment of landfill leachate has become a priority to determine its impacts on the ecosystem. Toxicity assays with microorganisms stand out due to their quick response, low cost and ease of testing. In this context, the present study evaluated the acute toxic effects of leachates from two landfills of different ages and modes of operation to bacterium Aliivibrio fischeri and activated sludge microorganisms and the ammonia nitrogen and humic substances (HS) sensitivity to these organisms. Reductions greater than 30% in leachate toxicity were observed after ammonia removal for A. fischeri and activated sludge microorganisms. After 97% removal of HS, the greater reductions in toxicity (44.28 to 79.82%) were verified for microbial species studied, indicating that the organic compounds (measured as chemical oxygen demand, total organic carbon and humic substances) were the primary pollutants responsible for the toxicity of the leachates. Concerning the organisms studied, A. fischeri showed greater sensitivity to the leachates' pollutants compared to the activated sludge microorganisms. Nevertheless, a strong correlation was observed between A. fischeri and activated sludge microorganisms' toxicity responses, suggesting that respirometry assay can be used to determine leachate toxicity.

Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition.

Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.

Evaluation of the concentrations of elements at trace level in the Serinhaem River estuary, Bahia, Brazil, using chemometric tools.

The Serinhaém River estuary is located in the Environmental Protection Area (EPA) of Pratigi, in the State of Bahia, Brazil, which is recognized for being a relatively well-preserved environment. In this work, the levels of aluminum (Al), arsenic (As), barium (Ba), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), vanadium (V), and zinc (Zn) were determined to evaluate the behavior of these chemical elements through geochemical parameters. Eighty-one sediment samples were collected in five sediment cores along the estuarine region. The results of the composition of the Serinhaém river basin showed high levels of Fe, Al, and Mn in the sediment samples. By using Principal Component Analysis, it was observed that 55.8% of the elements have a significant correlation with Fe, Al, and Mn, which may have the same origin or be associated with Fe and Mn oxy-hydroxides, and aluminosilicates. Although Cr, As, and V are correlated with Fe, Mn, and Al, their concentrations are above those established by NOAA, suggesting adverse effects on biota. Barium concentrations increased toward the outfall, where it meets the Camamu Bay, which is naturally enriched with this element. It was also possible to observe that along with the vertical profile, there were no variations in the concentrations of the elements, while along the estuary, it was possible to verify that the cores differ from each other. The estuary of the Serinhaém River can be considered to be influenced relatively little by human activities, and its concentrations can be considered as a base level for this coastal region.

Improvement of the bioaccessibility of bioactive compounds from Amazon fruits treated using high energy ultrasound.

The aim of this paper was to evaluate the effect of high energy ultrasound on the bioaccessibility of bioactive compounds from açaí (Euterpe precatoria) and buriti (Mauritia flexuosa) juices. Five levels of energy density (0, 0.9, 1.8, 2.7 and 3.6 J.cm-3), as well as their effects on the bioactive compounds were evaluated. Ultrasound did not significantly influence pH, titratable acidity and soluble solids. However, it affected the color attributes of juices by increasing brightness and color variation. The concentration of bioactive compounds (anthocyanins and carotenoids) and antioxidants increased with increasing ultrasound energy density, which was confirmed by Principal Component Analysis (PCA). Fatty acids increased up to 2.7 J.cm-3 and were reduced when higher energy was employed on the ultrasound process. Ultrasound allowed the release of new aromatic substances. For this reason, the ultrasound technology can be considered an alternative pre-treatment for fruit juices, improving the bioaccessibility and concentration of bioactive compounds.

Monitoring of the process of waste landfill leachate diffusion in clay and sandy soil.

The objective of this research was to evaluate the interaction of landfill leachate of urban solid waste in clayey (CL) and sandy soils (SL) in order to determine physical and chemical parameters that can be used as indicators of soil contamination when there are faults in the landfill waterproofing. In the diffusion tests, compacted soil samples were placed in contact with leachate (methanogenic phase). The temporal analysis (200 days) considered the parameters pH, electrical conductivity (EC), alkalinity, nitrogen series, chemical oxygen demand (COD), solids and color for the leachate and pH, ΔpH, EC, total nitrogen (TN), chemical elements, and cation exchange capacity (CEC) for the soils. Correlation analysis and principal component analysis (PCA) were performed to results. It was observed that the studied soils have potential to attenuate chemicals present in the leachate; this indicates the possibility of using them as base in landfills. Correlation analysis and PCA carried out to CL showed that in a process of CL monitoring the pH would be the key parameter to indicate contamination of this soil, due to the high correlation of this parameter with the others analyzed. For the SL, the parameters pH, alkalinity, apparent color, and COD (total and filtered) could be used as indicators of contamination. In both soils, monitoring of concentrations of Ca, Mg, K, SB, V, and CTC can be used to indicate possible faults in the waterproofing system of the landfill.

Estimating CO2 and VOCs production of Colletotrichum fragariae and Rhizopus stolonifer grown in cold stored strawberry fruit.

The aim of this work was to investigate the early detection of anthracnose and soft rot diseases in cold stored strawberry fruit by evaluating the CO2 and volatile organic compounds (VOCs) released by the fungi Colletotrichum fragariae and Rhizopus stolonifer. Strawberries were stored at 5, 10 and 21 °C (control group) and the VOCs and CO2 production of inoculated and non-inoculated strawberries were followed by gas chromatography. To evaluate and estimate the growth of both fungi, the CO2 data were fitted to the Gompertz model. Data of the VOCs released at the end of the fungal growth were analyzed using principal components analysis (PCA) to discriminate between infected and non-infected strawberries. The results showed that fungal growth was affected by temperature and C. fragariae had a maximum growth after 14.6 h at 5 °C and R. stolonifer at 21 °C after 45.2 h. On the other hand, through VOCs released by C. fragariae and R. stolonifer and PCA, four groups were obtained: a) strawberry infected with C. fragariae, stored at 10 °C, b) strawberry infected with R. stolonifer, stored at 21 °C, c) control group kept at 10 °C and, d) strawberry infected with C. fragariae and control group (5 and 21 °C), and strawberry infected with R. stolonifer at 5 and 10 °C. In conclusion, CO2 and VOCs released by C. fragariae and R. stolonifer on strawberries could infer the presence of anthracnose and soft rot during storage of the fruit at low temperature.

1H NMR-based fingerprinting of eleven Mexican Capsicum annuum cultivars.

Approximately 90% of the chili peppers consumed in the world are harvested in Mexico. The present article describes the untargeted 1H NMR-based metabolomic profiling of 11 cultivars of Capsicum annuum species which are routinely consumed worldwide. The metabolomic fingerprinting detected via 1H NMR contained 44 metabolites including sugars, amino acids, organic acids, polyphenolic acids and alcohols which were identified by comparison with the literature data, with Chenomx database and by 2D NMR. Statistical approaches based on principal component analysis (PCA) and linear discriminant analysis (LDA) were used to classify the Capsicum annuum cultivars according to their metabolite profile. LDA revealed metabolomic differences and similarities among Capsicum annuum cultivars, whereas hierarchical cluster analysis (HCA) significantly separated the cultivars according to the phylogenetic trees obtained. Substantial endogenous levels of free amino acids and carbohydrates were detected in all the studied cultivars but interestingly, Capsicum annuum cv. mirasol and C. annuum cv. chilaca contained almost three-fold more endogenous levels of vitamin C than the other cultivars. Considering that this antioxidant was found in crude aqueous extracts, its abundance could be directly proportional to its bioavailability for human nutrition. The results suggest that 1H NMR is an effective method to determine differences among cultivars of the Capsicum annuum species.

1H NMR-based metabolomics profiling of ten new races from Capsicum annuum cv. serrano produced in Mexico.

Herein we report on the 1H NMR-based metabolomics profiling of ten new races of Capsicum annuum cv. serrano, cultivated in Mexico. Forty eight metabolites (including sugars, amino acids, organic acids, polyphenolic acids and alcohols) were identified and quantified by 2D NMR and qNMR, respectively. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) separated the ten races into two clusters, from which citric acid, formic acid, fumaric acid, malic acid, glucose, fructose, sucrose and galactose were found as differential metabolites. This is the first study describing the chemical profiling of ten new races of Capsicum annuum cv. serrano and the spectrometric method used presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of different races of Capsicum annuum cv. serrano.

1H NMR-based metabolomic fingerprinting to determine metabolite levels in serrano peppers (Capsicum annum L.) grown in two different regions.

Chili pepper (Capsicum annuum) is the most important and emblematic condiment in Mexican food. Serrano pepper is a variety of C. annuum that is traditionally cultivated in Mexico and commercialized in local markets. The aim of this study was to describe the 1H NMR metabolomic profiling of the aqueous phase of serrano peppers harvested from two distinct regions, in the states of Veracruz and Oaxaca, Mexico. According to the current results, aspartate citrate, lactate, leucine and sucrose were found at higher amount in the serrano peppers from Veracruz. On the other hand, acetate, formate, fumarate, malonate, phosphocholine, pyruvate and succinate showed the highest abundance in this product from Oaxaca. These are the main metabolites that distinguish one group from the other. The spectrometric method reported presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of serrano peppers grown under different environmental conditions.

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts. METHODS: The proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs. RESULTS: This method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively. CONCLUSIONS: The SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

Combining contamination indexes, sediment quality guidelines and multivariate data analysis for metal pollution assessment in marine sediments of Cienfuegos Bay, Cuba.

The purpose of the present work was to combine several tools for assessing metal pollution in marine sediments from Cienfuegos Bay. Fourteen surface sediments collected in 2013 were evaluated. Concentrations of As, Cu, Ni, Zn and V decreased respect to those previous reported. The metal contamination was spatially distributed in the north and south parts of the bay. According to the contamination factor (CF) enrichment factor (EF) and index of geoaccumulation (Igeo), Cd and Cu were classified in that order as the most contaminated elements in most sediment. Comparison of the total metal concentrations with the threshold (TELs) and probable (PELs) effect levels in sediment quality guidelines suggested a more worrisome situation for Cu, of which concentrations were occasional associated with adverse biological effects in thirteen sediments, followed by Ni in nine sediments; while adverse effects were rarely associated with Cd. Probably, Cu could be considered as the most dangerous in the whole bay because it was classified in the high contamination levels by all indexes and, simultaneously, associated to occasional adverse effects in most samples. Despite the bioavailability was partially evaluated with the HCl method, the low extraction of Ni (<3% in all samples) and Cu (<55%, except sample 3) and the relative high extraction of Cd (50% or more, except sample 14) could be considered as an attenuating (Ni and Cu) or increasing (Cd) factor in the risk assessment of those element.

Integrated environmental monitoring and multivariate data analysis-A case study.

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC.

Comparative Analysis of a Principal Component Analysis-Based and an Artificial Neural Network-Based Method for Baseline Removal.

This work presents a non-parametric method based on a principal component analysis (PCA) and a parametric one based on artificial neural networks (ANN) to remove continuous baseline features from spectra. The non-parametric method estimates the baseline based on a set of sampled basis vectors obtained from PCA applied over a previously composed continuous spectra learning matrix. The parametric method, however, uses an ANN to filter out the baseline. Previous studies have demonstrated that this method is one of the most effective for baseline removal. The evaluation of both methods was carried out by using a synthetic database designed for benchmarking baseline removal algorithms, containing 100 synthetic composed spectra at different signal-to-baseline ratio (SBR), signal-to-noise ratio (SNR), and baseline slopes. In addition to deomonstrating the utility of the proposed methods and to compare them in a real application, a spectral data set measured from a flame radiation process was used. Several performance metrics such as correlation coefficient, chi-square value, and goodness-of-fit coefficient were calculated to quantify and compare both algorithms. Results demonstrate that the PCA-based method outperforms the one based on ANN both in terms of performance and simplicity.

Phenolic compounds of Brazilian beers from different types and styles and application of chemometrics for modeling antioxidant capacity.

In the present study we aimed at investigating, for the first time, phenolic compounds in Brazilian beers of different types and styles. We also aimed at applying chemometrics for modeling beer's antioxidant capacity as a function of their physicochemical attributes (density, refractive index, bitterness and ethanol content). Samples (n=29) were analyzed by PCA originating five groups, especially according to ethanol contents and bitterness. In general, Group V (alcoholic beers with very high bitterness) presented higher refractive index, bitterness, ethanol and phenolics contents than Groups I (non-alcoholic beers) and II (alcoholic beers with low bitterness). Brazilian beers phenolics profile was distinct from that of European beers, with high contents of gallic acid (0.5-14.7 mg/L) and low contents of ferulic acid (0.2-1.8 mg/L). Using PLS, beer's antioxidant capacity measured by FRAP assay could be predicted with acceptable precision by data of ethanol content and density, bitterness and refractive index values.