Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey.

The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models.

Classification of 7 monofloral honey varieties by PTR-ToF-MS direct headspace analysis and chemometrics.

Honey, in particular monofloral varieties, is a valuable commodity. Here, we present proton transfer reaction-time of flight-mass spectrometry, PTR-ToF-MS, coupled to chemometrics as a successful tool in the classification of monofloral honeys, which should serve in fraud protection against mispresentation of the floral origin of honey. We analyzed 7 different honey varieties from citrus, chestnut, sunflower, honeydew, robinia, rhododendron and linden tree, in total 70 different honey samples and a total of 206 measurements.

Investigation of the applicability of Zernike moments to the classification of SDS 2D-PAGE maps.

The aim of this work is to investigate the performance of multivariate classification techniques like partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) when using Zernike moments as global image descriptors, in the classification of sodium dodecyl sulphate (SDS) two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) maps affected by different levels of deformation. Synthetic sets of images simulating real SDS 2D-PAGE maps were analysed in controlled conditions to obtain information on the robustness and limits of applicability of the classification techniques operating on the basis of a given image decomposition method.

A new algorithm for the simulation of sodium dodecil sulfate two-dimensional polyacrylamide gel electrophoresis data sets.

A new algorithm for generating simulated sodium dodecil sulfate two-dimensional polyacrylamide gel electrophoresis (SDS 2D-PAGE) map images was developed. To choose the simulation strategy able to provide realistic 2D-PAGE maps, several parameters that characterize the statistical features of the images and data sets of images were taken into account, such as the distribution of size, intensity, and volume of the spots and their changes of position and volume along different replications of the same 2D-PAGE map. In this way, also the low reproducibility of replications of the same SDS 2D-PAGE maps was taken into account.

The principle of exhaustiveness versus the principle of parsimony: a new approach for the identification of biomarkers from proteomic spot volume datasets based on principal component analysis.

The field of biomarkers discovery is one of the leading research areas in proteomics. One of the most exploited approaches to this purpose consists of the identification of potential biomarkers from spot volume datasets produced by 2D gel electrophoresis. In this case, problems may arise due to the large number of spots present in each map and the small number of maps available for each class (control/pathological).

High-performance liquid chromatography-ultraviolet detection method for the simultaneous determination of typical biogenic amines and precursor amino acids. Applications in food chemistry.

A reversed-phase high-performance liquid chromatography (HPLC) method was developed for the simultaneous determination in food of biogenic amines and their precursor amino acids after a precolumn derivatization with dansyl chloride. The chromatographic conditions, selected to be suitable for mass spectrometry detection, were optimized through experimental design and artificial neural networks. The HPLC-UV method was validated by comparing the separation results with those obtained through a HPLC method, working under the same chromatographic conditions but employing mass spectrometry detection.

Butene concentration prediction in ethylene/propylene/1-butene terpolymers by FT-IR spectroscopy through multivariate statistical analysis and artificial neural networks.

This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy. 1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a constrained experimental design for mixtures, plus 12 terpolymers used for external validation) was analysed by FT-IR spectroscopy.

Monitoring of pigmented surfaces in accelerated ageing process by ATR-FT-IR spectroscopy and multivariate control charts.

This work is an extension of a method for monitoring the conservation state of pigmented surfaces presented in a previous paper. A cotton canvas painted with an organic pigment (Alizarin) was exposed to UV light in order to evaluate the effects of the applied treatment on the surface of the sample. The conservation state of the pigmented surface was monitored with ATR-FT-IR spectroscopy and multivariate control charts.

Optimisation of sensitivity in the multi-elemental determination of 83 isotopes by ICP-MS as a function of 21 instrumental operative conditions by modified simplex, principal component analysis and partial least squares.

The optimisation of the sensitivity in the ICP-MS determination of 83 isotopes, as a function of 21 operative parameters was performed by generating an initial experimental design that was used to define, by principal component analysis, the multi-criteria target function. The first PC, which contained an overall evaluation of the signal intensity of all isotopes, was used to rank the experiments. The modified simplex optimisation technique was then applied on the ranked experiments.

Evaluation of the variables characterized by significant discriminating power in the application of SIMCA classification method to proteomic studies.

SIMCA classification can be applied to 2D-PAGE maps to identify changes occurring in cellular protein contents as a consequence of illnesses or therapies. These data sets are complex to treat due to the large number of proteins detected. A method for identifying relevant proteins from SIMCA discriminating powers is proposed, based on the Box-Cox transformation coupled to probability papers.

2D-PAGE maps analysis.

Due to the low reproducibility affecting 2D gel-electrophoresis and the complex maps provided by this technique, the use of effective and robust methods for the comparison and classification of 2D maps is a fundamental tool for the development of automated diagnostic methods. A review of classical and recently developed methods for the comparison of 2D maps is presented here. The methods proposed regard both the analysis of spot volume datasets through multivariate statistical tools (pattern recognition methods, cluster analysis, and classification methods) and the analysis of 2D map images through fuzzy logic, three-way PCA, and the use of moment functions.

A new method of comparing 2D-PAGE maps based on the computation of Zernike moments and multivariate statistical tools.

The aim of this work was to obtain the correct classification of a set of two-dimensional polyacrylamide gel electrophoresis map images using the Zernike moments as discriminant variables. For each 2D-PAGE image, the Zernike moments were computed up to a maximum p order of 100. Partial least squares discriminant analysis with variable selection, based on a backward elimination algorithm, was applied to the moments calculated in order to select those that provided the lowest error in cross-validation.

Application of partial least squares discriminant analysis and variable selection procedures: a 2D-PAGE proteomic study.

2D gel electrophoresis is a tool for measuring protein regulation, involving image analysis by dedicated software (PDQuest, Melanie, etc.). Here, partial least squares discriminant analysis was applied to improve the results obtained by classic image analysis and to identify the significant spots responsible for the differences between two datasets.

Monitoring of the surface of paper samples exposed to UV light by ATR-FT-IR spectroscopy and use of multivariate control charts.

The effect of exposure of paper samples to UV light was monitored by use of ATR-FT-IR spectroscopy and multivariate statistical tools. Three types of paper were tested: common laser-printer paper, news print, and thermal fax paper. The samples were first characterised by ATR-FT-IR spectroscopy to determine natural experimental variability.

Modeling of the polluting emissions from a cement production plant by partial least-squares, principal component regression, and artificial neural networks.

A Portland cement process was taken into consideration and monitored for one month with respect to polluting emissions, fuel and raw material physical-chemical properties, and operative conditions. Soft models, based on linear (partial least-squares, PLS, and principal component regression, PCR) and nonlinear (artificial neural networks, ANNs) approaches, were employed to predict the polluting emissions. The predictive ability of the three regression methods was evaluated by means of the partition of the dataset by Kohonen self-associative maps into both a training and a test set.

Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis.

Mantle cell lymphoma (MCL) cell lines have been difficult to generate, since only few have been described so far and even fewer have been thoroughly characterized. Among them, there is only one cell line, called GRANTA-519, which is well established and universally adopted for most lymphoma studies. We succeeded in establishing a new MCL cell line, called MAVER-1, from a leukemic MCL, and performed a thorough phenotypical, cytogenetical and molecular characterization of the cell line.

Use of Legendre moments for the fast comparison of two-dimensional polyacrylamide gel electrophoresis maps images.

In this paper, Legendre moments are calculated to extract the global information from a set of two-dimensional polyacrylamide gel electrophoresis map images. The dataset contains 18 samples belonging to two different cell lines (PACA44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of this work was to obtain the correct classification of the 18 samples, using the Legendre moments as discriminant variables.

The state of conservation of painted surfaces in the presence of accelerated ageing processes monitored by use of FT-Raman spectroscopy and multivariate control charts.

A new method has been developed for monitoring the degradation of paintings. Two inorganic pigments (ultramarine blue and red ochre) were blended with linseed oil and spread on canvas. Each canvas was subjected to simulated accelerated ageing in the presence of typical degradation agents (UV radiation and acidic solution).

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.