Use of INAA and PIXE for multipollutant air quality assessment and management

The objective of this paper was to assess the air pollution and the main sources of Air Particulate Matter in the Setúbal urban/industrial area, Portugal. PM_2.5 and PM_2.5?C10 were sampled in Nuclepore filters and lichens transplants were exposed during 9?months. The levels of elements in these two matrixes were measured by INAA and PIXE. A large data base was created and source apportionment was performed by using Principal Component Analysis. The results showed that the main sources of fine particles were anthropogenic and were related with traffic and local industry. There was an important contribution of natural sources, mainly for the coarse fraction, associated with the sea and the soil. Lichens characterization and mapping showed that different site-specific characteristics controlled the spatial distribution of different elements. This study showed that biomonitoring is an effective complementary method to traditional sampling systems.     

THE EFFECT OF ALCOHOLS WITH DIFFERENT STRUCTURES ON THE FORMATION OF WARM O/W MICROEMULSIONS

The influence of twenty five different alcohols on the formation of warm oil-in-water (O/W) microemulsions was investigated. Selected concentrations of each alcohol were added to fixed amounts of stearic acid, Tween 20 and water at 65 ° C. Fifteen alcohols formed microemulsions, at least at one of the concentrations. A pattern recognition study was performed to elucidate the activities of the alcohols by Principal Component Analysis (PCA). Linear Discriminant Analysis (LDA) was used to classify them. Two classification functions, obtained for alcohols forming / not forming microemulsions, suggest that the formation of warm O/W microemulsion is linked to the nature and the dimension/lipophilicity of the alcohol.     

Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.     

Provenance study of obsidian samples by using portable and conventional X ray fluorescence spectrometers. Performance comparison of both instrumentations

The potentiality of portable instrumentation lies on the possibility of the in situ determinations. Sampling, packaging and transport of samples from the site to the laboratory are avoided and the analysis becomes non destructive at all. However, detection limits for light elements are, in most cases, a limitation for quantification purposes. In this work a comparison between the results obtained with an X ray fluorescence spectrometer laboratory based and a portable instrument is performed. A set of 76 obsidian archaeological specimens from northwest Patagonia, Argentina was used to carry out the study. Samples were collected in the area of the middle and high basin of the Limay River. The analytical information obtained with both instrumentations was complemented with Principal Component Analysis in order to define groups and identify provenance sources. The information from both instruments allows arriving to the same conclusion about sample provenance and mobility of hunter-gatherer groups. Three groups of sources were identified in both cases matching with the geographical information. Also, same sets of outlier samples or not associated to these sources were found. Artifact samples were associated mainly to the closest sources, but some of them are related to sources located more than three hundred kilometers, evidencing the large mobility of the hunter-gatherers by the obsidian interchange. No significant differences between concentrations values obtained by laboratory based instrument and portable one were found.     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

Pattern recognition analysis for classification of hypertensive model rats and diurnal variation using 1H-NMR spectroscopy of urine.

Urine samples were collected during the daytime and nighttime from spontaneously hypertensive model rats and normal rats without dosing. The 1H NMR spectra were measured for their urine samples, and analyzed by a pattern recognition method, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). The separation of urinary data due to the diurnal variation (daytime and nighttime) and also to the difference between the two strains of rat was achieved in the PCA score plot. Differences of the urinary profiles in the respective separation were effectively extracted as marker variables by the SIMCA method. NMR measurements coupled with pattern recognition methods provide a straightforward approach to inspect the disease metabolic status and the preliminary screening tool of marker candidates for further development.     

Analysis of Equilibrium and Kinetics of Chromium-Fluoride Complexation from Spectroscopic Data via Chemometrics Methods

IntroductionStudies on reversible kinetic systems are consi-dered as a hotspot of chemical and biochemical kineticresearches[1,2]. Of late, some researches have been fo-cused on the simultaneous optimization of the obverseand reverse rate constants[3,4].H…     

An application of cluster analysis and multivariate classification methods to spring water monitoring data

An optimized model of multivariate classification for the monitoring of eighteen spring waters in the land of Serra St. Bruno, Calabria, Italy, has been developed. Thirty analytical parameters for each water source were investigated and reduced to eight by means of Principal Component Analysis (PCA). Water springs were grouped in five distinct classes by cluster techniques (CA) and a model for their classification was built by a Partial Least Squares–Discriminant Analysis (PLS–DA) procedure. The model was optimized and validated and then applied to new data matrices, containing the analytical parameters carried out on the same sources during the successive years. This model proved to be able to notice deviations of the global analytical characteristics, by pointing out in the course of time a different distribution of the samples within the classes. The variation of nitrate concentration was demonstrated to be the major responsible for the observed class shifts. The shifting sources were localized in areas used as sowable lands and high variability of nitrate content was ascribed to the practice of crop rotation, involving a varying use of the nitrogenous chemical fertilizers.     

Multivariate calibration of spectra obtained by Laser Induced Breakdown Spectroscopy of plutonium oxide surrogate residues

Laser Induced Breakdown Spectroscopy (LIBS) was used to determine elemental concentration of plutonium oxide surrogate (cerium oxide) residue for monitoring the fabrication of lanthanide borosilicate glass. Quantitative analysis by LIBS is affected by the severe limitation of variation in the induced plasma due to changes in the matrix. Multivariate calibration was applied to LIBS data to predict the concentrations of Ce, Cr, Fe, Mo, and Ni. A total of 18 different samples were prepared to compare calibration from univariate data analysis and from multivariate data analysis. Multivariate calibration was obtained using Principal Component Regression (PCR) and Partial Least Squares (PLS). Univariate calibration was obtained from background-corrected atomic emission lines. Calibration results show improvement in the coefficient of determination from 0.87 to 0.97 for Ce compared to univariate calibration. The root mean square error also reduced from 7.46 to 2.93%. A similar trend was obtained for Cr, Fe, Mo, and Ni also. These results clearly demonstrate the feasibility of using LIBS for online process monitoring in a hazardous waste management environment.     

On heteroaromaticity of nucleobases. Bond lengths as multidimensional phenomena

Three hundred and nine carbon-carbon, carbon-nitrogen, and carbon-oxygen pi-bond lengths in high precision crystal structures of 31 purine and pyrimidine nucleobases were related to the Pauling pi-bond order, its analogues corrected to crystal packing effects, the numbers of non-hydrogen atoms around the bond, and the sum of atomic numbers of the bond atoms. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) demonstrated that the bond lengths in the nucleobases are three-dimensional phenomenon, characterized by nine distinct classes of bonds. Bond lengths predicted by Linear Regression models, Pauling Harmonic Potential Curves, Multiple Linear Regression, Principal Component, and Partial Least Squares Regression were compared to those calculated by molecular mechanics, semiempirical, and ab initio methods using PCA-HCA procedure on the calculated bond lengths, statistical parameters, and structural aromaticity indices. Incorporation of crystal packing effects into bond orders makes multivariate models to be competitive to semiempirical results, while further improvement of quantum chemical calculations can be achieved by geometry optimization of molecular clusters.     

Application of receptor models to airborne particulate matter

The human activities in their various aspects cause a change in the natural air quality. This change results more marked in very populated and in high industrialized areas. Some pollutants emitted are typical of a particular activity. Each source of pollution is identified by its profile in the composition of the emissions in the environment. Multivariate receptor models can be used in order to apportion pollutants to the different sources assessing the contribution of each source to the total pollution.This paper deals with the application of Absolute Principal Component Scores (APCS) receptor model to data obtained from the automatic network of air quality monitoring in the city of Bari (South Italy). The parameters monitored by automatic networks, as bihourly values, are PM₁₀, NO_x, CO, Benzene, Toluene, Xilene. The data shown in this paper concerning 1 month almost of sampling in different monitoring stations of Bari Municipality during the period of time from January 2005 to April 2006. Moreover preliminary results obtained applying the APCS model to daily PM_2.5 samples collected during SITECOS PRIN project are shown. The results concerning data collected in corso Cavour (Bari) during the month of October 2005.The results obtained by APCS receptor model seem to suggest a poor contribution of the “vehicular traffic source” and a relevant contribution of the “secondary particulate source” to particulate matter concentrations.     

Comparison of different calibration methods for the determination by FT-NIR spectroscopy of the hydroxyl number in polyester resins

Different calibration methods have been applied for the determination of the Hydroxyl Number in polyester resins, namely Partial Least Squares (PLS), Principal Component Regression (PCR), Ordinary Least Squares with selection of the variables by genetic algorithm (OLS-GEN) and back-propagation Artificial Neural Networks (BP-ANN). The predictive ability of the regression models was estimated by splitting the dataset in training and test sets by application of the Kohonen self-organising maps. The linear methods (OLS-GEN, PLS and PCR) showed comparable results while artificial neural networks provided the best results both in fitting and prediction.     

Comparison of PAH Levels and Sources in Pine Needles from Portugal,Spain, and Greece

The main objective of this work was to assess and compare the levels, patterns, and sources of contamination of 16 polycyclic aromatic hydrocarbons (PAHs) between Portugal, Spain, and Greece (in the island of Crete). A total of 9 sampling sites were chosen (4 in urban and 5 in non-urban areas) in each country and pine needles from the Pinus pinea L. species were collected. Although the mean total PAH levels was similar in the three countries (279 ± 236 ng g^?1 for Portugal, 294 ± 258 ng g^?1 for Spain, 301 ± 253 ng g^?1 for Greece, all dry weight) and, in general, 3-ring and 4-ring PAHs were predominant (being phenanthrene consistently the most abundant), there were some visible differences in the aromatic ring patterns and possible sources between the three regions. Source apportionment was done using PAH ratios (Phen/Ant and Flt/Pyr crossplots) and reflected mixed petrogenic and pyrogenic sources. Furthermore, Principal Component Analysis (PCA) clearly separated the urban and the non-urban sites and all three countries, which reinforces that the sources of contaminations vary in each case and the suitability of pine needles for trans-boundary biomonitoring of PAHs.     

苯乙烯/丙烯酸正丁酯乳液聚合反应过程中残余单体含量的实时监测

采用近红外光谱分析技术在线测量苯乙烯(St)/丙烯酸正丁酯(BA)乳液聚合体系中残余单体的含量. 共设计9个半连续方式的St/BA乳液共聚反应, 在反应过程中实时取样测量其残余单体含量, 并记录取样时刻对应的聚合体系的近红外光谱. 采用多元散射校正法(MSC)处理光谱, 有效地克服了乳胶粒子散射效应对近红外光谱分析的影响. 采用主成分分析法(PCA)对乳液体系的近红外光谱数据进行了解析. 选取6个聚合反应对应不同反应时间的72个样品, 用于建立校正模型, 另外3个聚合反应共取36个样品用于校正模型的验证, 并在反应设计上体现了乳化剂用量的变化, 从而使校正模型对乳化剂用量的变化具有一定的适应性. 研究结果表明, 所得模型对残余单体St和BA含量的预测结果标准差(SEP)分别为0.08026和0.05305.     

Detection of antibiotic residues in bovine milk by a voltammetric electronic tongue system

A voltammetric electronic tongue (VE-tongue) was developed to detect antibiotic residues in bovine milk. Six antibiotics (Chloramphenicol, Erythromycin, Kanamycin sulfate, Neomycin sulfate, Streptomycin sulfate and Tetracycline HCl) spiked at four different concentration levels (0.5, 1, 1.5 and 2 maximum residue limits (MRLs)) were classified based on VE-tongue by two pattern recognition methods: principal component analysis (PCA) and discriminant function analysis (DFA). The VE-tongue was composed of five working electrodes (gold, silver, platinum, palladium, and titanium) positioned in a standard three-electrode configuration. The Multi-frequency large amplitude pulse voltammetry (MLAPV) which consisted of four segments (1 Hz, 10 Hz, 100 Hz and 1000 Hz) was applied as potential waveform. The six antibiotics at the MRLs could not be separated from bovine milk completely by PCA, but all the samples were demarcated clearly by DFA. Three regression models: Principal Component Regression Analysis (PCR), Partial Least Squares Regression (PLSR), and Least Squares-Support Vector Machines (LS-SVM) were used for concentrations of antibiotics prediction. All the regression models performed well, and PCR had the most stable results.     

基于支持向量机的汽油族组成近红外光谱分析方法研究

提出一种基于最小二乘支持向量机(LS-SVM)的汽油族组成近红外光谱分析方法。采用国家标准方法(GB 11132-1989液体石油产品烃类测定法——荧光指示剂吸附法)测定了重庆地区销售的汽油族组成,并采用主成分分析-最小二乘支持向量机建立汽油族组成的预测模型。预测模型对汽油中芳烃和烯烃含量的RMSEC分别为0.2090和0.2142。实验结果表明所建模型具有计算量小,预测准确、可靠,而且操作简单、维护费及测试费用低等特点。     

Voltammetric Electronic Tongue for the Qualitative Analysis of Beers

This paper deals with the application of a voltammetric electronic tongue (ET) towards beers classification. For this purpose, samples were analyzed using cyclic voltammetry without performing any sample pretreatment, albeit its dilution with distilled water. The voltammetric signals were first preprocessed employing Fast Fourier Transform (FFT). Then, using the obtained coefficients, responses were evaluated using three different clustering techniques: Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS‐DA) and Linear Discriminant Analysis (LDA). In this case, the ET has demonstrated a good capability to correctly discriminate and classify the different beer samples according to its type (Lager, Stout and IPA) and manufacture process (commercial and craft).