Search results for "Component analysis"

Evaluation of infrared spectroscopy as a screening tool for serum analysis

2013

Abstract The application of attenuated total reflectance Fourier transform infrared (ATR-FT-IR) spectroscopy to the determination of clinical parameters in serum using partial least squares (PLS) has been evaluated as a point-of-care diagnostic tool. In this study the effect of using an increased size of the calibration set and the influence of the origin of samples and their interyear variation on the prediction capability of the method were considered. PLS-ATR-FT-IR provides a green, fast and cheap point-of-care tool for the determination of total protein. Albumin, glucose, urea, HDL, LDL and total cholesterol were predicted with relative errors between 15 and 32%. The analytical predicti…

Archaeopolymetallurgical study of materials from an Iberian culture site in Spain by scanning electron microscopy with X-ray microanalysis, chemometr…

2010

Abstract Archaeometallurgical materials from “La Bastida de Moixent”, a site in Valencia (Spain), from the second Iberian iron age (4th Century B.C.) have been studied using metallographic techniques, microanalysis, chemometrics and image analysis. The materials come from various phases of iron production and cupellation of argentiferous lead to obtain silver. Scanning electron microscopy (SEM) is used to determine the morphological, microstructural and topographic characteristics of the samples. Image analysis was used to obtain a numeric estimate of the main components in these materials. X-ray microanalysis (SEM/EDX) provides qualitative and quantitative information about the elements in…

Multivariate data analysis and bivariate regression studies applied to comparison of two multi-elemental methods for analysing wine samples

2002

Two inductively coupled plasma mass spectrometry (ICP-MS) methods which permit multi-elemental analysis in wine samples have been compared following two strategies. First, a multivariate tool based on principal component analysis (PCA) was employed for a global (all analytes) qualitative comparison of the two methods. A single plot based on the confidence limits of the Q and T2 PCA model statistics corresponding to the ‘standard’ method results (calibration set) was used to check the comparability of the ‘candidate’ method (test samples). The residual matrix (after test matrix interpolation into the PCA model) gives qualitative information about the nature of the main errors. This approach …

Origin based classification of crude oils by infrared spectrometry and chemometrics

2019

Abstract Crude oil samples from different Iranian petrol resources in both, raw and mixture forms have been characterized by attenuated total reflectance mid infrared spectroscopy. Obtained spectra were classified by chemometric techniques to propose a method for geological based classification of crude oil samples. Totally 251 samples from 7 petrol fields and 3 mixtures were analyzed. Mean centering and principal component analysis (PCA) supported – leverage value based outlier detection were used as preprocessing approaches. PCA, cluster analysis and soft independent modeling of class analogy (SIMCA) were utilized to classify the spectra. Obtained results confirmed that SIMCA is a robust …

Botanical Origin Authentication of Polish Phacelia Honey Using the Combination of Volatile Fraction Profiling by HS-SPME and Lipophilic Fraction Prof…

2019

Eleven samples of Polish Phacelia tanacetifolia Benth., three Brassica napus and one Salix spp. honeys were characterized by melissopalynology and analysis of the compositions of their volatile fractions. Headspace solid-phase microextraction coupled with gas chromatography mass spectrometry (HS-SPME/GC–MS) using PDMS/CAR/DVB fiber was used for the isolation of low-molecular weight compounds which create a volatile fraction. To differentiate and indicate the most representative unifloral samples, chemometric techniques such as principal component analysis (PCA) and hierarchical-tree clustering (HTC) were applied to the dataset of the chromatographic fingerprints. Based on the obtained resul…

Overlapped moving windows followed by principal component analysis to extract information from chromatograms and application to classification analys…

2015

Variable generation from chromatograms is conveniently accomplished using unsupervised rather than manual techniques. With unsupervised techniques, there is no need for selecting a few peaks for manual integration and valuable information is quickly and efficiently collected. The generation of variables can be performed by using either peak searching or moving window (MW) strategies. With a MW approach, the peaks are ignored and many variables, only part of them carrying information, are generated. Thus, variable generation by MWs should be followed by data compression to generate the variables to be further used for classification or quantitation purposes. In this work, unsupervised proces…

A gallery of Chua's Attractors - Part IV

2007

The on-line curvilinear component analysis (onCCA) for real-time data reduction

2015

Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component analysis, can work in real time by using iterative algorithms while all known nonlinear techniques cannot be implemented in such a way and actually always work on the whole database at each epoch. Among these nonlinear tools, the Curvilinear Component Analysis (CCA), which is a non-convex techni…

Dimensionality reduction via regression on hyperspectral infrared sounding data

2014

This paper introduces a new method for dimensionality reduction via regression (DRR). The method generalizes Principal Component Analysis (PCA) in such a way that reduces the variance of the PCA scores. In order to do so, DRR relies on a deflationary process in which a non-linear regression reduces the redundancy between the PC scores. Unlike other nonlinear dimensionality reduction methods, DRR is easy to apply, it has out-of-sample extension, it is invertible, and the learned transformation is volume-preserving. These properties make the method useful for a wide range of applications, especially in very high dimensional data in general, and for hyperspectral image processing in particular…

Shelf life assessment of industrial durum wheat bread as a function of packaging system

2017

This study compared the effect of different packaging systems on industrial durum wheat bread shelf-life, with regard to thermoformed packaging (TF) and flow-packaging (FP). Two TFs having different thickness and one FP were compared by assessing physico-chemical and sensorial properties and volatile compounds of sliced bread during 90 days of storage. Texture, aw and bread moisture varied according to a first-order kinetic model, with FP samples ageing faster than TFs. Sensorial features such as consistency, stale odor, and sour odor, increased their intensity during storage. Furans decreased, whereas hexanal increased. The Principal Component Analysis of the whole dataset pointed out that…