Application Of Chemometric Techniques Research Articles

Combination of Total-Reflection X-Ray Fluorescence Method and Chemometric Techniques for Provenance Study of Archaeological Ceramics.

The provenance study of archaeological materials is an important step in understanding the cultural and economic life of ancient human communities. One of the most popular approaches in provenance studies is to obtain the chemical composition of material and process it with chemometric methods. In this paper, we describe a combination of the total-reflection X-ray fluorescence (TXRF) method and chemometric techniques (PCA, k-means cluster analysis, and SVM) to study Neolithic ceramic samples from eastern Siberia (Baikal region). A database of ceramic samples was created and included 10 elements/indicators for classification by geographical origin and ornamentation type. This study shows that PCA cannot be used as the primary method for provenance purposes, but can show some patterns in the data. SVM and k-means cluster analysis classified most of the ceramic samples by archaeological site and type with high accuracy. The application of chemometric techniques also showed the similarity of some samples found at sites located close to each other. A database created and processed by SVM or k-means cluster analysis methods can be supplemented with new samples and automatically classified.

Application of Chemometric Techniques In The Evaluation of Bioactive Compounds and Antioxidant Activity of Fruit From Brazilian Cerrado

Application of Chemometric Techniques In The Evaluation of Bioactive Compounds and Antioxidant Activity of Fruit From Brazilian Cerrado

Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics.

The differentiation of cultivars is carried out by means of morphological descriptors, in addition to molecular markers. In this work, near-infrared spectroscopy (NIR) and chemometric techniques were used to develop classification models for two different commercial sesame cultivars (Sesamum indicum) and 3 different strains. The diffuse reflectance spectra were recorded in the region of 700 to 2500 nm. Based on the application of chemometric techniques: principal component analysis—PCA, hierarchical cluster analysis—HCA, k-nearest neighbor—KNN and the flexible independent modeling of class analogy—SIMCA, from the infrared spectra in the near region, it was possible to perform the genotyping of two sesame cultivars (BRS Seda and BRS Anahí), and to classify these cultivars with 3 different sesame strains, obtaining 100% accurate results. Due to the good results obtained with the implemented models, the potential of the methods for a possible realization of forensic, fast and non-destructive authentication, in intact sesame seeds was evident.

Application of chemometric techniques: An innovative approach to discriminate two seaweed cultivars by physico-functional properties

Chemometric techniques were employed to discriminate two Indonesian seaweed cultivars (Kappaphycus alvarezii and Sargassum duplicatum) based on their physico-functional properties. Data of physico-functional properties, including emulsifying activity and bulk density, were processed using multivariate, discriminant analysis (DA), principal component analysis (PCA), and cluster analysis (CA). Significant differences (p < 0.05) and discrimination (multivariate analysis (F9,14 = 57.85; Wilks’s lambda = 0.02, p < 0.0001)) were determined among physico-functional properties of seaweed powders. In addition, DA method clarified (Λ = 0.02 and χ2 = 63.74) variations between samples along with two discriminate functions. Moreover, PCA (49.63% with Eigenvalues > 1) and CA (clusters 1 and 2) methods confirmed that the seaweed samples possessed differing physico-functional properties, which implied their distinctive use in food product development. Overall, our results demonstrated strong variation in seaweed powers using chemometrics, which might contribute to the rapid applicability of chemometrics in evaluating novel food material.

Application of chemometric techniques in studies of toxicity of selected commercially available products for infants and children.

The goal of the present study is to assess the impact of the experimental conditions for extraction procedures (time of extraction, thermal treatment and type of extraction media) as applied to several baby and infant products checked for their possible ecotoxicological response when tested by various ecotoxicity tests (Microtox®, Ostracodtoxkit F™ and Xenoscreen YES/YAS™). The systems under consideration are multidimensional by nature and, therefore, the appropriate assessment approach was intelligent data analysis (chemometrics). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were selected as reliable data mining methods for the interpretation of the ecotoxicity data. We show that the different experimental conditions have a significant impact on the ecotoxicity levels observed, especially those measured by Microtox® and Ostracodtoxkit F™ tests. The time of contact proves to be a very significant factor for all extraction media and ecotoxicity test procedures. The present study is a pioneering effort to offer a specific expert approach for analysing links between the type of test measurement methodology and imposed experimental conditions to mimic real-life circumstances in the use of baby and infant products.

Spatial assessment and source identification of heavy metals pollution in surface water using several chemometric techniques

This study presents the determination of the spatial variation and source identification of heavy metal pollution in surface water along the Straits of Malacca using several chemometric techniques. Clustering and discrimination of heavy metal compounds in surface water into two groups (northern and southern regions) are observed according to level of concentrations via the application of chemometric techniques. Principal component analysis (PCA) demonstrates that Cu and Cr dominate the source apportionment in northern region with a total variance of 57.62% and is identified with mining and shipping activities. These are the major contamination contributors in the Straits. Land-based pollution originating from vehicular emission with a total variance of 59.43% is attributed to the high level of Pb concentration in the southern region. The results revealed that one state representing each cluster (northern and southern regions) is significant as the main location for investigating heavy metal concentration in the Straits of Malacca which would save monitoring cost and time. CapsuleThe monitoring of spatial variation and source of heavy metals pollution at the northern and southern regions of the Straits of Malacca, Malaysia, using chemometric analysis.

Current application of chemometrics in traditional Chinese herbal medicine research.

Traditional Chinese herbal medicines (TCHMs) are promising approach for the treatment of various diseases which have attracted increasing attention all over the world. Chemometrics in quality control of TCHMs are great useful tools that harnessing mathematics, statistics and other methods to acquire information maximally from the data obtained from various analytical approaches. This feature article focuses on the recent studies which evaluating the pharmacological efficacy and quality of TCHMs by determining, identifying and discriminating the bioactive or marker components in different samples with the help of chemometric techniques. In this work, the application of chemometric techniques in the classification of TCHMs based on their efficacy and usage was introduced. The recent advances of chemometrics applied in the chemical analysis of TCHMs were reviewed in detail.

Identification Source of Variation on Regional Impact of Air Quality Pattern Using Chemometric

This study intends to show the effectiveness of hierarchical agglomerative cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), factor analysis (FA) and multiple linear regressions (MLR) for assessing the air quality data and air pollution sources pattern recognition. The data sets of air quality for 12 months (January–December) in 2007, consisting of 14 stations around Peninsular Malaysia with 14 parameters (168 datasets) were applied. Three significant clusters - low pollution source (LPS) region, moderate pollution source (MPS) region, and slightly high pollution source (SHPS) region were generated via HACA. Forward stepwise of DA managed to discriminate 8 variables, whereas backward stepwise of DA managed to discriminate 9 out of 14 variables. The method of PCA and FA has identified 8 pollutants in LPS and SHPS respectively, as well as 11 pollutants in MPS region, where most of the pollutants are expected derived from industrial activities, transportation and agriculture systems. Four MLR models show that PM10 categorize as the primary pollutant in Malaysia. From the study, it can be stipulated that the application of chemometric techniques can disclose meaningful information on the spatial variability of a large and complex air quality data. A clearer review about the air quality and a novel design of air quality monitoring network for better management of air pollution can be achieved.

Application of Chemometric Techniques in the Assessment of Groundwater Quality of Udham Singh Nagar, Uttarakhand, India

In the present study, groundwater quality of district Udham Singh Nagar was evaluated using chemometric techniques viz. cluster, principal component and factor analyses to investigate the spatial variations of the most significant water quality parameters and to determine origin of pollution sources. Water samples collected from 6 sampling sites were analysed for 23 water quality parameters in pre- and post-monsoon seasons during the years 2010 and 2011. The concentration of alkalinity, total hardness, total dissolved solids, calcium, magnesium total coliform and faecal coliform of samples exceeded the Bureau of Indian Standard limits. Correlations among selected water quality parameters were statistically examined. Cluster analysis grouped 6 sampling sites into three clusters, i.e. less, moderate and high polluted, based on similarities of water quality characteristics. The principal component/factor analysis extracted four factors, which explain the ground water quality. Factor 1 (anthropogenic pollution) includes hardness, alkalinity, TDS calcium and magnesium, Factor 2 (hydrogeologic and agricultural pollution) includes turbidity, pH and nitrate, Factor 3 (industrial pollution) comprises \(\hbox {Cl}^{-}\) and Fe, and Factor 4 (agricultural pollution) includes sulphate. Piper diagram illustrated that water samples of the studied area are dominant of \(\mathrm{{Ca}}{-}\mathrm{{Mg}}{-}\mathrm{{HCO}}_{3}\) hydrochemical facies. Moreover, water quality index (WQI) described the suitability of water sources for drinking purpose. Compared to 2010, WQI was higher in 2011, which led most of the sites unsuitable with grade ‘E’. The results revealed that the selected groundwater samples, being used for drinking and domestic purposes, are deteriorating, and thus highlighted the application of proper treatment strategy before domestic water supply.

Temporal trends of elements in Turin (Italy) atmospheric particulate matter from 1976 to 2001

The temporal trends of major, minor and trace elements in the total atmospheric particulate sampled in the urban area of Turin (Italy) were determined for the following years: 1976, 1986, 1996 and 2001. The wavelength dispersive X-ray fluorescence (WD-XRF) technique was adopted to determine the concentrations of Ba, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, Ni, Pb, S, Ti and Zn. A smaller number of samples was also analysed by ICP atomic emission spectroscopy (ICP-AES) and the results were compared with those obtained by WD-XRF to confirm their validity. A clear seasonal pattern with higher concentrations of the aforementioned elements in the cold periods was observed. Moreover, a change in the chemical composition of atmospheric particulate matter was evidenced, particularly between the first (1976 and 1986) and the last (1996 and 2001) years. This change can be attributed both to the greater contribution of Pb and Br to atmospheric pollution in the past and, in recent years, to the higher level of pollutants associated with increased vehicular traffic and industrial activities. The application of chemometric techniques (Principal Component Analysis and Cluster Analysis) allowed us to speculate about the main emitting sources influencing the total atmospheric particulate in these years.

DETERMINATION OF APRICOT FRUIT QUALITY USING FOURIER TRANSFORM INFRARED SPECTROSCOPY

This work reports the rapid determination of apricot fruit quality traits using Fourier Transform near (NIR) and mid (MIR) infrared reflectance spectroscopy. The FT-NIR (800-2500 nm) spectra were acquired on intact fruits using an integrating sphere (diffuse reflectance). The FT-MIR spectra were acquired on fruit slurries using a horizontal ATR cell (Attenuated Total Reflectance). Relationships between spectral data and quality attributes obtained by traditional methods are evaluated by the application of chemometric techniques based on partial least squares (PLS) on fruit set divided randomly into two groups: 598 fruits for calibration and 279 for validation. The non-destructive method using NIR spectroscopy allows the prediction of soluble solids content and titratable acidity (correlation coefficient of 0.92 and 0.89, respectively, and root mean square error of prediction of 0.98% Brix and 3.62 meq.100g -1 FW, respectively). The ATR-FTIR performed on apricot slurries is well-adapted for the prediction of the individual compounds: sucrose, glucose, malic and citric acids (correlation coefficients greater than or equal to 0.92 and errors smaller than 16%). Prediction model for fructose, the minor compound in apricot, showed the lowest performance with r = 0.86 and error of validation of 18%. Moreover, the technique that consists in acquiring spectra and performing PCA on spectra allows the discrimination of apricot sets according to their ripening stages and their genetic characteristics.

A New Application of Chemometric Techniques to HPLC Data for the Simultaneous Analysis of a Two‐Component Mixture

A new chemometric approach using high performance liquid chromatography (HPLC) with photodiode array (PDA) detection was developed and applied to the simultaneous determination of enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets. Chemometric calibration techniques, classical least squares (CLS), principle component regression (PCR), and partial least squares (PLS) were subjected to the peak area at multiwavelength PDA detector responses. The combination of HPLC and chemometric calibration techniques was called HPLC‐CLS, HPLC‐PCR, and HPLC‐PLS. For comparison purposes, the HPLC method called classical HPLC method was used for the confirmation of the results obtained from combined HPLC‐chemometric calibration techniques. A good chromatographic separation between two drugs and internal standard (IS) was achieved using a Waters Symmetry® C18 Column 5 µm 4.6×250 mm and a mobile phase consisting of 0.2 M acetate buffer and acetonitrile (v/v, 60∶40). The multiwavelength PDA detection was done at 230 (A), 240 (B), 250 (C), 250 (D), 240 (E) nm wavelengths, and peak area was recorded for the concentration set in the mobile phase. Three HPLC‐chemometric calibrations and a classical‐HPLC method were tested by analyzing the synthetic mixture of EA and HCT in the presence of losartan potassium (IS). The proposed methods were applied to real samples containing the present two drugs. The obtained results were statistically compared with each other.

The application of chemometric techniques to profile Ecstasy seizures

The application of chemometric techniques to profile Ecstasy seizures RJH WADDELL and N NIC D&ID Forensic Science Unit, University of Strathclyde, 295 Cathedral Street, Glasgow GI IXL, United Kingdom Introduction Despite legal restrictions in the UK, namely the Misuse of Drugs Act 1971 and the Misuse of Drugs Act Regulations 1985, the clandestine synthesis, widespread availability and consequent misuse of Ecstasy continues. The chemical composition of tablets sold as Ecstasy (Figure 1) varies. The majority contain MDMA or MDEA although the presence of other active ingredients, such as other phenethylamines or caffeine, is common. Physical examination of tablets offers no indication of the chemical composition while the same tablet shape and motif may be used by different manufacturers. Conventional chemical profiling of illicit Ecstasy seizures relies upon identification and quantification of the active ingredient and organic impurities present. Identification of such impurities can determine the synthetic route employed and hence, establish batch links among seizures. Quantification of the active ingredient has ramifications in subsequent criminal proceedings.

Application of chemometric techniques to the characterisation of impregnated materials obtained following supercritical fluid technology

The impregnation of salicylic acid following a supercritical fluid technology was determined experimentally for various adsorbing matrices such as Florisil, zeolite, silica gel and Amberlite under a wide range of experimental conditions. A thorough characterisation of these supercritical adsorption processes requires a large number of runs under various experimental conditions in order to cover all possible sources of variance, which may result in a tedious and time-consuming task. The overall information contained in the data set generated in this series of experimental runs can be analysed more efficiently using chemometrics. Here, multivariate data consisting of adsorption values determined for each material were analysed with principal component analysis and partial least-squares regression (PLS). The corresponding PLS models were subsequently applied to predict the impregnated amounts for other experimental conditions. Results showed a reasonable concordance between the experimental and predicted values.

Application of Chemometric Techniques in Obtaining Macerates with Phenolic Compound Content Similar to That of Wines from the Jerez-Shery Region Subjected to Oxidative Aging

This paper studies and quantifies the final concentration of phenolic aldehydes and acids (determined by HPLC) in a series of sobretablas wine macerates prepared with American oak shavings subjected to an accelerated aging system developed by our research group and based on thermal processes traditionally used in cooperage. This experiment aims to considerably reduce and control the oxidative aging period of oloroso wines from the Jerez-Sherry region as occurs in the dynamic system of soleras and criaderas, with the consequent economic benefits. To standardize the process by controlling the production technique of the macerates, the results were subjected to surface response methodology as a means of optimizing the experiment. The proposed model was found to be suitable after evaluation of the factors affecting the final concentration. Of the factors studied, it was found to be essential to control the charring time and/or the interactions between temperature and charring time for 10 of the 11 phenolic compounds studied.

Application of chemometric techniques to the analysis of underground water from the Majorca Island

Summary We applied three multivariate analysis techniques (principal component analysis, correspondence factor analysis and cluster analysis) to physico‐chemical data obtained by analyzing underground waters from the Majorca Island in 1987. We studied the information obtained on plotting samples and variables in the new factor space defined and found conclusions to be consistent throughout the interval studied and the occurrence of redundant information. The sample groups obtained and their relationship to the variables assayed allow hypotheses to be formulated on the geographical origin of the samples.

An investigation of tumor 1H nuclear magnetic resonance spectra by the application of chemometric techniques.

1H nuclear magnetic resonance (NMR) spectra of tumors and normal tissue include signals from all hydrogen-containing metabolites and can therefore be considered multicomponent multivariate mixtures. We have obtained 1H spectra from perchloric acid extracts of three normal tissues (liver, kidney, and spleen) and five rat tumors (GH3 prolactinoma, Morris hepatomas 7777 and 9618a, LBDS1 fibrosarcoma, and Walker 256 carcinosarcoma). We have applied several different chemometric methods to analyze the data. First, we used principal component analysis, cluster analysis, and an optimized artificial neural network to develop a classification rule from a training set of samples of known origin or class. The classification rule was then assessed using a set of unknown samples. We were able to successfully determine the class of each unknown sample. Second, we used the chemometric techniques of factor analysis followed by target testing to investigate the underlying biochemical differences that are detected between the classes of samples.