Finding the Most Significant Elements for the Classification of Organic Orange Leaves: A Data Mining Approach

Brazil is the world’s largest producer of oranges. The Brazilian conventional citrus crop requires repeated application of agrochemicals to achieve satisfactory levels of productivity. The organic citriculture is an alternative production system, which is environmentally friendly and offers a safe food to consumers. However, it is difficult to determine if a food or plant was cultivated in organic or conventional system by just common observation, which makes the customers of organic food market vulnerable against fraudulent entrepreneurs. In this study, we present a data mining approach for the study of Brazilian organic citrus leaves which can aid in the certification of authenticity of the citrus leaves. The elemental composition is determined by inductively coupled plasma-mass spectrometry (ICP-MS). We developed classification models based on support vector machines and artificial neural networks capable of predicting whether a citrus leaf is organic or conventional through analysis of the concentration levels of the 14 chemical elements (Al, Ba, Co, Cr, Cs, Cu, Fe, Mg, Mn, Ni, Rb, Si, Sr, and V) found in both types of leaves. Feature selection filter methods are used to determine the most relevant elements for the classification process. Our best model obtained was a support vector machine with approximately 88% prediction accuracy. The elements Mn, Mg, and Rb were evaluated as the most significant for the classification decision. This is the first paper which addresses the problem of classification of organic orange leaves based on chemical composition. The presented methodology is useful for attesting authenticity of organic citrus leaves and can be adapted for other organic food or substances.     

Classification and technology of byzantine mosaic glass

Glass-making is a very sophisticated skill and the contribution given by the chemical analyses of glass materials is fundamental for the classification of glass types and for identifying compositional groups according to consistent characteristics that can be associated with chronological and geographical differentiations. The chemical composition of glasses is particularly complex: to a few basic constituents many components were added, either derived from impurities in the raw materials or intentionally incorporated into the glass mix. The field of study concerning the chemical composition and the technology of Byzantine mosaic production has not been dealt with in a systematic manner and certainly not exhaustively from the view point of classification according to the reconstruction of chronological and geographical development. Nevertheless, it is of great interest because it is probable that during the Byzantine period the production of mosaic glass was greater than for any other type of glass. We propose a methodology for classifying Byzantine mosaic glasses on the basis of simple statistical treatment of the chemical composition data. Compositional data relative to basic and accessory constituents together with colorants were analysed and elaborated through binary diagrams. Byzantine glasses are also compared to glasses of different epoch and provenance.     

Application of chemical and chemometric analytical techniques to the study of ancient ceramics from Dougga (Tunisia)

In the present paper chemical characterization has been carried out on 67 shards of archaeological pottery from Dougga (North Tunisia). The analysed shards, dated to the Byzantine period (VI–VII century A.D.), belong to the three ceramic classes African Red Slip Ware, Dougga Ware and African cooking Ware. Fourteen elements have been determined by both atomic emission spectroscopy with flame as source (AES) and by using an inductively coupled plasma source (ICP-OES). The data acquired have been treated by statistical techniques in order to define grouping for the examined shards. Both unsupervised and supervised methods have been employed in order to define groups of different pottery shards. As a comparison, some samples (control group) coming from Southern Tunisia have been examined. All the statistical methods employed have evidenced how the control group, as concerns the chemical composition, is clearly distinguishable from Ain Wassel samples which are highly homogeneous. In fact because of the compositional homogeneity of the Northern Tunisia productions, it is quite difficult to establish a good classification and distribution of the samples in well defined cluster. Nevertheless supervised analysis has evidenced how, among the three classes, the African cooking Ware is the more distinguishable one confirming the archaeologists' hypothesis that Dougga Ware is an imitation of African Red Slip Ware.     

Which kind of data (normally distributed, standardized, transformed) for a realistic factor analysis?

Taking into account that the problem of the best pretreatment of data for factor analysis has not yet arrived at generally accepted solutions, it has been tried to estimate, by an empiric procedure, the goodness of the results of repeated factor analyses with different pretreatments, conditions and statistical distribution of variables. Sets of multivariate data for river waters have been constructed firstly, after fixing the number and nature of the latent factors corresponding to the sources of pollution. A series of R-mode factor analyses has then been performed on these data, using various pretreatments (autoscaling, logarithmic transformation and their combinations), various factor rotations (rigid and oblique) and methods of computation of significant factors (40 data processing on the whole). Factor analyses have also been performed on real data of the Po river in the Piedmont region. Comparisons between the results obtained by factor analysis and the actual situation of the systems investigated have made it possible to draw some conclusions on how to proceed in order to obtain realistic results with this chemometric technique.     

Pattern recognition analysis for characterization of coal and ash samples

Different pattern recognition techniques were applied for classification and characterization of a large number of coal, and coal fly ash samples. Cluster analysis was performed on 116 samples using the concentration data of 40 elements. The effect of the number and type of the elements on the clustering was studied in detail. It was proved that short time activation analysis enables the characterization of these types of samples if¹³⁹Ba and⁸⁷Sr are included, these data being obtained by increasing the irradiation and counting times. The two elements and chlorine were found to be necessary for such a classification. The combination between cluster analyses and principal component analysis gives accurate and confirmed results. The statistical analyses of the subgroups are compared.     

An archaeometric approach about the study of medieval glass from Siponto (Foggia, Italy)

Medieval glasses, including feet and rims of chalices, fragments of lamps and globular bottles, coming from the archaeological site of Siponto (Foggia, Italy), were analyzed by Inductively Coupled Plasma Emission Spectroscopy and Graphite Furnace Atomic Absorption Spectroscopy for investigating and defining glass production technology in Apulia (Italy) in the Middle Ages, because of the poor understanding currently achieved on either compositional and technological features of medieval glass items. The examined finds, whether colourless or coloured blue, yellow-green, yellow, pink and red, revealed a typical silica-soda-lime-composition. The chemical analysis and the statistical treatment of data allowed to trace former, flux, modifier and, where it is present, the element responsible for colour, clarifying production technology issues. It has been possible to identify, moreover, objects obtained by recycling of cullets or finished items.Finally, this work evaluates the effectiveness of the statistical multivariate treatment by Principal Component Analysis (PCA), Clustering Analysis (CA) and Factor Analysis (FA) on compositional data to obtain technological information in opposition to the conventional binary oxides diagram, which represent the most common, widely assessed, archaeometrical practice to obtain technological information from compositional data.     

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.     

Towards quantitative laser-induced breakdown spectroscopy analysis of soil samples

A quantitative analysis of chromium in soil samples is presented. Different emission lines related to chromium are studied in order to select the best one for quantitative features. Important matrix effects are demonstrated from one soil to the other, preventing any prediction of concentration in different soils on the basis of a univariate calibration curve. Finally, a classification of the LIBS data based on a series of Principal Component Analyses (PCA) is applied to a reduced dataset of selected spectral lines related to the major chemical elements in the soils. LIBS data of heterogeneous soils appear to be widely dispersed, which leads to a reconsideration of the sampling step in the analysis process.     

Comparative analysis of ancient ceramics by neutron activation analysis,inductively coupled plasma-optical-emission spectrometry,inductively coupled plasma-mass spectrometry,and X-ray fluorescence

The accurate measurement of the maximum possible number of elements in ancient ceramic samples is the main requirement in provenance studies. For this reason neutron activation analysis (NAA) and X-ray fluorescence (XRF) have been successfully used for most of the studies. In this work the analytical performance of inductively coupled plasma-optical-emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS) has been compared with that of XRF and NAA for the chemical characterization of archaeological pottery. Correlation coefficients between ICP techniques and XRF or NAA data were generally better than 0.90. The reproducibility of data calculated on a sample prepared and analysed independently ten times was approximately 5% for most of the elements. Results from the ICP techniques were finally evaluated for their capacity to identify the same compositional pottery groups as results from XRF and NAA analysis, by use of multivariate statistics.     

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical,Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.     

氨基酸广义信息因子分析标度组合支持向量机模型用于β-turns预测及特征分析

提出一种新的组合方法用于β-turns预测和特征分析.该方法包括两步:如何表征β-turns特征和如何构建其预测模型.第一步应用氨基酸广义信息因子分析标度表征蛋白质中β-turns的结构特征,该标度涉及氨基酸的疏水性、α-螺旋与转角倾向、体积性质、构成特征、局部柔性及静电性.第二步以426个蛋白质为训练集样本,通过留1/7法交互验证,基于支持向量机建立β-turns预测模型.该模型分别成功地预测547和823个蛋白的β-turns.所得结果与所对比方法结果相当,更重要的是,SVM模型提供了一些关于β-turns特征的重要结构信息.该组合方法可以进一步尝试用于蛋白质结构预测及特征分析.     

Molecular mass concentrations for a powdered SRM sample using a quantitative single particle analysis

In a quantitative single particle analysis, named the low-Z particle EPMA, number concentration data for chemical species encountered in aerosol sample are provided. However, it will be more useful if mass concentration data can be obtained from single particle analysis; i.e., the single particle analysis data for weight fractions of chemical species can be complementarily used in combination with the bulk analysis data, for more clearly understanding the behavior of airborne aerosols. In order to investigate how reliably mass concentration data can be obtained from the low-Z particle EPMA technique, a potassium feldspar powdered standard reference material (SRM), of which elemental weight fractions are well defined by various bulk analytical techniques, was analyzed using the low-Z particle EPMA technique. In this work, it is demonstrated that weight fractions of major elements in the powdered SRM sample obtained by the low-Z particle EPMA are within 8% to the certified values obtained by bulk analytical techniques, although the single particle and bulk analyses employ different approaches. Further, it is shown that the quantitative single particle analysis, i.e., low-Z particle EPMA, can provide molecular mass concentration data for chemical species, which is not easy to obtain using bulk analysis.     

PLS‐DA for compositional data with application to metabolomics

When quantifying information in metabolomics, the results are often expressed as data carrying only relative information. Vectors of these data have positive components, and the only relevant information is contained in the ratios between their parts; such observations are called compositional data. The aim of the paper is to demonstrate how partial least squares discriminant analysis (PLS‐DA)—the most widely used method in chemometrics for multivariate classification—can be applied to compositional data. Theoretical arguments are provided, and data sets from metabolomics are investigated. The data are related to the diagnosis of inherited metabolic disorders (IMDs). The first example analyzes the significance of the corresponding regression parameters (metabolites) using a small data set resulting from targeted metabolomics, where just a subset of potential markers is selected. The second example—the approach of untargeted metabolomics—was used for the analysis detecting almost 500 metabolites. The significance of the metabolites is investigated by applying PLS‐DA, accommodated according to a compositional approach. The significance of important metabolites (markers of diseases) is more clearly visible with the compositional method in both examples. Also, cross‐validation methods lead to better results in case of using the compositional approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Photon level chemical classification using digital compressive detection

A key bottleneck to high-speed chemical analysis, including hyperspectral imaging and monitoring of dynamic chemical processes, is the time required to collect and analyze hyperspectral data. Here we describe, both theoretically and experimentally, a means of greatly speeding up the collection of such data using a new digital compressive detection strategy. Our results demonstrate that detecting as few as ∼10 Raman scattered photons (in as little time as ∼30 μs) can be sufficient to positively distinguish chemical species. This is achieved by measuring the Raman scattered light intensity transmitted through programmable binary optical filters designed to minimize the error in the chemical classification (or concentration) variables of interest. The theoretical results are implemented and validated using a digital compressive detection instrument that incorporates a 785 nm diode excitation laser, digital micromirror spatial light modulator, and photon counting photodiode detector. Samples consisting of pairs of liquids with different degrees of spectral overlap (including benzene/acetone and n-heptane/n-octane) are used to illustrate how the accuracy of the present digital compressive detection method depends on the correlation coefficients of the corresponding spectra. Comparisons of measured and predicted chemical classification score plots, as well as linear and non-linear discriminant analyses, demonstrate that this digital compressive detection strategy is Poisson photon noise limited and outperforms total least squares-based compressive detection with analog filters.     

Micro-PIXE Analysis of Monazite from the Dora Maira Massif, Western Italian Alps

Quantitative micro-PIXE and electron microprobe analyses, as well as micro-PIXE compositional mapping of trace elements were performed on monazite [(Ce, La, Nd, Th)PO₄] inclusions in pyrope megablasts from Dora Maira Massif, Western Italian Alps for petrological and geochronological purposes. Monazite was studied by SEM-BSE imaging and by X-ray qualitative compositional maps of major elements; further WDS electron microprobe analyses were carried out in areas showing different BSE intensity in order to quantify chemical zoning. Finally, micro-PIXE compositional maps and quantitative analyses were performed on selected spots and areas. EPMA data indicate that the Dora Maira monazite is Ce- and Th-rich with homogeneous concentrations of LREE, but with a significantly heterogeneous distribution of Th, as well as of Y, Sr, U and Pb as displayed by micro-PIXE compositional mapping. HREE mostly occur in concentrations below the detection limit for standard quantitative EPMA. Th–U–Pb zoning suggests two monazite growth events, dated at 35 (±7 Ma) and 60 Ma (±10 Ma), respectively. While the younger age of 35 Ma found in high-Th monazite areas corresponds to the thermal and baric peak of the UHP metamorphism in the Dora Maira Massif, in agreement with previous literature data, the older ages of 60 Ma found in low-Th areas have to be confirmed by U–Th–Pb isotopic data.     

A likelihood ratio model for the determination of the geographical origin of olive oil

Food fraud or food adulteration may be of forensic interest for instance in the case of suspected deliberate mislabeling. On account of its potential health benefits and nutritional qualities, geographical origin determination of olive oil might be of special interest. The use of a likelihood ratio (LR) model has certain advantages in contrast to typical chemometric methods because the LR model takes into account the information about the sample rarity in a relevant population. Such properties are of particular interest to forensic scientists and therefore it has been the aim of this study to examine the issue of olive oil classification with the use of different LR models and their pertinence under selected data pre-processing methods (logarithm based data transformations) and feature selection technique. This was carried out on data describing 572 Italian olive oil samples characterised by the content of 8 fatty acids in the lipid fraction. Three classification problems related to three regions of Italy (South, North and Sardinia) have been considered with the use of LR models. The correct classification rate and empirical cross entropy were taken into account as a measure of performance of each model. The application of LR models in determining the geographical origin of olive oil has proven to be satisfactorily useful for the considered issues analysed in terms of many variants of data pre-processing since the rates of correct classifications were close to 100% and considerable reduction of information loss was observed. The work also presents a comparative study of the performance of the linear discriminant analysis in considered classification problems. An approach to the choice of the value of the smoothing parameter is highlighted for the kernel density estimation based LR models as well.     

Variety identification of wheat using mass spectrometry with neural networks and the influence of mass spectra processing prior to neural network analysis

The performance of matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry with neural networks in wheat variety classification is further evaluated.1 Two principal issues were studied: (a) the number of varieties that could be classified correctly; and (b) various means of pre-processing mass spectrometric data. The number of wheat varieties tested was increased from 10 to 30. The main pre-processing method investigated was based on Gaussian smoothing of the spectra, but other methods based on normalisation procedures and multiplicative scatter correction of data were also used. With the final method, it was possible to classify 30 wheat varieties with 87% correctly classified mass spectra and a correlation coefficient of 0.90.