Synthesis and acid digestion of biomorphic ceramics: determination of alkaline and alkaline earth ions

Ceramic and glass are some of the more recent engineering materials and those that are most resistant to environmental conditions. They belong to advanced materials in that they are being developed for the aerospace and electronics industries. In the last decade, a new class of ceramic materials has been the focus of particular attention. The materials were produced with natural, renewable resources (wood or wood-based products). In this work, we have synthesised a new biomorphic ceramic material from oak wood and Si infiltration. After the material characterization, we have optimized the dissolution of the sample by acid attack in an oven under microwave irradiation. Experimental designs were used as a multivariate strategy for the evaluation of the effects of varying several variables at the same time. The optimization was performed in two steps using factorial design for preliminary evaluation and a Draper-Lin design for determination of the critical experimental conditions. Five variables (time, power, volume of HNO3, volume H2SO4 and volume of HF) were considered as factors and as a response the concentration of different metal ions in the optimization process. Interactions between analytical factors and their optimal levels were investigated using a Draper-Lin design.     

Application of multivariate calibration to the simultaneous routine determination of ethanol, glycerol, fructose, glucose and total residual sugars in botrytized-grape sweet wines by means of near-infrared reflectance spectroscopy

The simultaneous determination of ethanol, glycerol, fructose, glucose and residual sugars in botrytized-grape sweet white wines was performed by means of near-infrared reflectance using 19 interference filters and a partial least squares (PLS) model in latent variables as a multivariate calibration technique. The results were compared with those obtained using other multivariate calibration techniques, such as MLR, SWR and PCR, by means of a validation set of samples with known compositions. Ethanol, fructose and residual sugars were well predicted by all the multivariate techniques. Glycerol and glucose showed the highest prediction residuals. The technique may be of practical interest in the routine analysis of these types of wines with low cost in terms of samples, time and personnel. Received: 12 June 1995 / Revised: 5 February 1996 / Accepted: 7 February 1996     

Multivariate optimization of the synthesis and of the microwave dissolution of biomorphic silicon carbide ceramics

Biomorphic silicon carbide ceramics are a new class of materials that are used for various industrial applications owing to its attractive properties. The efficiency of the synthesis and the partly extreme properties of the biomorphic ceramic depend decisively on the synthesis parameters and on the impurities of the final ceramic. In the present article the synthesis as well as the decomposition of these materials is optimized using a multivariate methodology for the design of experiments. Three variables (initial amount of Si, infiltration temperature and reaction time) were considered as factors in the synthesis optimization and six variables (digestion time, ramp time, microwave power, volumes of concentrated HF, HNO₃ and H₂SO₄) in the microwave dissolution optimization. Interactions, between analytical factors and their optimised levels were investigated using full factorial, Plackett–Burman and central composite designs. The synthesis parameters that found higher percentage of SiC (quantified by FTIR) and the digestion procedure that found higher concentrations of metals (Co, Cr and Ni, determined by FI-ETAAS) were considered the optimum.     

Physicochemical and Chemometric Characterisation of Late Roman Amphorae from Straits of Gibraltar

?A physicochemical characterisation of a selected group of late Roman amphorae was carried out using x-ray diffraction spectrometry, flame atomic absorption and emission spectroscopy as well as petrographic polarisation microscopy. Most of these amphorae were found in underwater environments on both coasts of the Straits of Gibraltar: Ceuta and Cadiz (Spain). A multivariate statistical study of the obtained data for 99 samples and 21 variables (Si, Al, Ca, Mg, K, Na, Fe, Mn, Ti, Cr, Cu, Ni, Pb, Zn, calcite, quartz, dolomite, orthoclase, plagioclase, phyllosilicates and gypsum) was performed. Relations among amphorae that shared a similar composition were established by factor analysis. As a result, amphorae coming from Cadiz showed a different composition than those coming from Ceuta. This is demonstrated by the increase in concentration of certain chemical elements, such as Cu, Cr and Ca. This fact supports the archaeological hypothesis that some amphorae of South-Hispanic typology found in Ceuta were produced locally, although there is no proof of the existence of kilns in this area. On the other hand, neither the different typologies nor the food stored or transported in these amphorae seem to affect the mineralogical and chemical composition of the ceramic pastes. However, their porosity contributes to the enrichment in trace elements and salt contents in those samples that had been in marine underwater contexts for a large period of time. Correspondence: Departamento de Química Analíticay Análisis Instrumental, Facultad de Ciencias, Universidad Autónoma de Madrid, E-28049 Madrid, Spain. e-mail: mdolores.petit@uam.es Received July 21, 2002; accepted December 2, 2002 Published online April 11, 2003     

Neutron activation analysis of bulk samples from Chinese ancient porcelain to provenance research

Neutron activation analysis (NAA) is an important technique to determine the provenance of ancient ceramics. The most common technique used for preparing ancient samples for NAA is to grind them into a powder and then encapsulate them before neutron irradiation. Unfortunately, ceramic materials are typically very hard making it a challenge to grind them into a powder. In this study we utilize bulk porcelain samples cut from ancient shards. The bulk samples are irradiated by neutrons alongside samples that have been conventionally ground into a powder. The NAA for both the bulk samples and powders are compared and shown to provide equivalent information regarding their chemical composition. Also, the multivariate statistical have been employed to the analysis data for check the consistency. The findings suggest that NAA results are less dependent on the state of the porcelain sample, and thus bulk samples cut from shards may be used to effectively determine their provenance.     

Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets

The large size of the hyperspectral datasets that are produced with modern mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable overview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the datasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate analysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. Three multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments and one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate algorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is shown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality significantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant amount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the analyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial scores into one three-dimensional view on the complete datacube.     

Identification of discriminatory variables in proteomics data analysis by clustering of variables

This article presents a data analysis method for biomarker discovery in proteomics data analysis. In factor analysis-based discriminate models, the latent variables (LV's) are calculated from the response data measured at all employed instrument channels. Since some channels are irrelevant and their responses do not possess useful information, the extracted LV's possess mixed information from both useful and irrelevant channels. In this work, clustering of variables (CLoVA) based on unsupervised pattern recognition is suggested as an efficient method to identify the most informative spectral region and then it is used to construct a more predictive multivariate classification model. In the suggested method, the instrument channels (m/z value) are clustered into different clusters via self-organization map. Subsequently, the spectral data of each cluster are separately used as the input variables of classification methods such as partial least square-discriminate analysis (PLS-DA) and extended canonical variate analysis (ECVA). The proposed method is evaluated by the analysis of two experimental data sets (ovarian and prostate cancer data set). It is found that our proposed method is able to detect cancerous from healthy samples with much higher sensitivity and selectivity than conventional PLS-DA and ECVA methods.     

Instrumental neutron activation analysis of mass fractions of toxic metals in plastic

It is very challenging to decompose a plastic product for the purpose of analysis of hazardous elements contained. To circumvent such technical problem, it is imperative that an analyst employ a nondestructive analytical method free of any pretreatments. The analytical results of the concentrations of toxic metals such as Cd and Cr in polypropylene for seven samples at two different levels were obtained using the instrumental neutron activation analysis. This work was intended ultimately to establish certified reference materials (CRMs) of these metals in the polypropylene, traceable to the SI. The uncertainties associated with the analytical procedures were estimated in accordance with the ISO guideline. The results were subsequently validated by a comparison with those for CRM-680 and −681 of the Bureau Communautaire de Reference (BCR), which demonstrated acceptable agreement within their uncertainty ranges.     

Experimental design in the optimization of a microwave acid digestion procedure for the determination of metals in biomorphic ceramic samples by inductively coupled plasma mass spectrometry and atomic absorption spectrometry

In the present paper, we have synthesized a biomorphic ceramic material from oak wood as biological template structure and infiltration with zirconia-sol. After the material characterization, we have optimized the sample dissolution by acid attack in an oven under microwave irradiation. Experimental designs were used as a multivariate strategy for the effect's evaluation of varying several variables. This article describes the development by response surface methodology (RSM) of a procedure for zirconium determination, and other ions, such as copper and nickel by inductively coupled plasma mass spectrometry (ICP-MS) and others, such as iron, calcium and magnesium determination by flame atomic absorption spectrometry (FAAS) in the synthesized sample after digestion. A full factorial design (3³) was used to find optimal conditions for the procedure through response surface study. Three variables (time, HNO₃ volume and HF volume) were regarded as factors and as response to the concentration of different metal ions in the optimization study.     

Towards obtaining more information from gas chromatography–mass spectrometric data of essential oils: An overview of mean field independent component analysis

Mean field independent component analysis (MF-ICA) along with other chemometric techniques was proposed for obtaining more information from multi-component gas chromatographic–mass spectrometric (GC–MS) signals of essential oils (mandarin and lemon as examples). Using these techniques, some fundamental problems during the GC–MS analysis of essential oils such as varying baseline, presence of different types of noise and co-elution have been solved. The parameters affecting MF-ICA algorithm were screened using a 2⁵ factorial design. The optimum conditions for MF-ICA algorithm were followed by deconvolution of complex GC–MS peak clusters. The number of independent components (ICs) (chemical constituents) in each peak cluster was estimated using morphological score method. Eigenvalue profiles of evolving factor analysis (EFA) and pure variables from orthogonal projection approach (OPA) were used as initial mixing matrix (chromatograms) in iterative process. The resolved mass spectra were satisfactorily identified using NIST mass spectral search system. Finally, the results of optimized MF-ICA were compared with those obtained using multivariate curve resolution-alternating least square (MCR-ALS), multivariate curve resolution-objective function minimization (MCR-FMIN) and heuristic evolving latent projection (HELP) methods. It is demonstrated that MF-ICA can be used as an alternative method for a quick and accurate analysis of real multi-component problematic systems such as essential oils.     

Analytical and multivariate study of roman age architectural terracotta from northeast of Spain

Roman culture employed architectural terracotta made from baked clay as original material to manufacture ceramic pieces. It was often used as a basis for construction of functional and/or decorative elements in roofs, such as plane and curve tiles as well as antefixes with their corresponding “imbrexes”. Some of them are conserved nowadays. They were collected in Roman quarries discovered in old cities and villages sited in the Hispania Citerior (northeast of Spain in Roman age). A study of the origin and manufacturing process (moulding, baking, touching up and painting) of these terracotta pieces has been made on the basis of the data obtained from a physicochemical characterization of samples. The used techniques were mainly flame absorption and emission spectrometry for the elemental analysis (major and minor elements), dilatometry for the study of thermal behaviour, scanning electron microscopy (SEM) for observation of thin layers and X-ray diffraction spectrometry (XRD) for mineralogical composition. In addition, a supervised pattern recognition programme was applied to the results for a selected group of 85 samples and five variables (chromium, copper, lead, nickel and zinc contents). Dilatometry and SEM results showed baking temperatures of these materials below 900 °C and the existence of zones with very different porosity in the same ceramic piece. Results obtained from multielemental analysis and multivariate statistical study by linear discriminant analysis lead us to the following conclusions: (i) the high content of lead found in a large number of antefixes demonstrates the use of lead oxide as an additive in the lime grout treatment, (ii) different contents of Cu, Zn, Cr, and Ni were indicative of the use of varied clay types in the manufacture process (even in the same production centre) as well as of the existence of a pigmentation process, although this last affirmation is not corroborated by the presence of remains of evident painting in the ceramic pieces, (iii) samples can be classified according to the places where these pieces came from and (iv) more variety in their composition was found in Roman age terracotta production centres.     

Effect of glass transition temperature of polymeric binders on properties ceramic materials

In the paper are presented the studies of the effect of glass transition temperature of new water-thinnable polymeric binders on the properties of ceramic materials obtained by die pressing. The parameters of ceramic samples comprising polymeric binders have been compared with those of samples comprising poly(vinyl alcohol) (PVA) — water-soluble binder. When using poly(acrylic-styrene) (AS), poly(acrylic-allyl) (AA) and poly(vinyl-allyl) (VA) water-thinnable binders, materials of greater density and mechanical strength were obtained in the green state as well as after sintering than those in the case of using PVA. The dependence of the chemical structure of the binders applied on the properties of samples such as the glass transition temperature and hydrophobic-hydrophilic balance of the copolymers has been discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Chemical analysis of uranium and titanium niobotantalate metamict minerals by ion-exchange chromatography and spectrophotometric procedures

An ion-exchange separation followed by spectrophotometric determinations is applied to some metamict minerals. These minerals, containing very high amounts of elements which present some problems to the analyst, such as uranium, titanium, niobium and rare-earth elements, are fused with potassium bisulphate, and the cooled melts dissolved in sulphuric acid. The solutions are passed through a series of three ion-exchange columns to separate those mineral-forming elements for which the colorimetric procedures suffer interference from the elements listed above. The procedure has been tested with a synthetic solution and with solutions of the minerals.     

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).     

Thermal,chemical, and mineralogical characterization of ceramic tobacco pipes from Cyprus

This study is focused on simultaneous thermoanalytical investigations by TG/DTG-DTA technique applied for characterization of samples collected from archaeological site of Nicosia, Cyprus, dating to seventeenth century and gave new information on the firing technology. The ceramic samples derived from Ottomanic tobacco pipes were characterized by the related techniques such as X-ray powder diffraction for the mineralogical composition, and inductively coupled plasma-atomic emission spectrometry and micro-X-ray fluorescence spectroscopic analysis for the chemical content. It was found that they consisted mainly of quartz, calcite, feldspars, and micas. For the majority of the investigated ceramic samples, the thermal behavior investigation collaborates with their mineralogical findings, and resulted to the firing temperature at ~700 °C, due to the existence of calcite. Only in two samples with very high content in quartz, absence of calcite, low amounts of adsorbed water and of total mass loss, and absence of micas, the firing process resulted up to 1000 °C.     

Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets

LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable (transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e.g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu (USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.     

Validation of the uncertainty evaluation for the determination of metals in solid samples by atomic spectrometry

 Every analytical result should be expressed with some indication of its quality. The uncertainty as defined by Eurachem ("parameter associated with the result of a measurement that characterises the dispersion of the values that could reasonably be attributed to the, . . ., quantity subjected to measurement") is a good tool to accomplish this goal in quantitative analysis. Eurachem has produced a guide to the estimation of the uncertainty attached to an analytical result. Indeed, the estimation of the total uncertainty by using uncertainty propagation laws is components-dependent. The estimation of some of those components is based on subjective criteria. The identification of the uncertainty sources and of their importance, for the same method, can vary from analyst to analyst. It is important to develop tools which will support each choice and approximation. In this work, the comparison of an estimated uncertainty with an experimentally assessed one, through a variance test, is performed. This approach is applied to the determination by atomic absorption of manganese in digested samples of lettuce leaves. The total uncertainty estimation is calculated assuming 100% digestion efficiency with negligible uncertainty. This assumption was tested. Received: 3 November 1997 · Accepted: 2 January 1998     

Territorial origin of olive oil: representing georeferenced maps of olive oils by NMR profiling

ABSTRACT Proton NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work, we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a geographic information system (GIS). Nuclear magnetic resonance spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work, we describe the methods and the algorithms that permit to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by ¹H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built an LDA model that provides a classification ability up to 99% . A comparison between the variables selected in the geostatistics and classification steps is finally performed. Copyright © 2016 John Wiley & Sons, Ltd.     

Relationship between heavy metal distribution in sediment samples and their ecotoxicity by the use of the Hasse diagram technique

Many studies assessing the quality of sediments and their pollution impact use monitoring data consisting predominantly of chemical indicators. Recently, ecotoxicity estimates have been used as very important parameters of the ecological state of sediment samples. Thus, a more complete sediment risk assessment is achieved and more reliable information on the sediment pollution history is extracted. The data interpretation could be improved if multivariate statistical techniques were applied to data classification, modelling and interpretation. The starting classification of the data was performed using self-organizing maps (SOM) approach in order to reveal specific relationship patterns for objects and for variables. The original element of the present study is the use of the Hasse diagram technique (HDT) for partial ordering in order to explain some specific relations between the chemical indicators analysed (heavy metal content in different sediment compartments) and the ecotoxicity tests for acute and chronic toxicity. In principle, a reliable estimate of the pollution impact of a large environmental object (the Mar Menor lagoon in Spain) is achieved. The specific role of each one of the five heavy metals involved (Zn, Cu, Mn, Pb, and Cd) is interpreted in the context of the additional ecotoxicity tests.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.