scholarly journals Multivariate classification techniques and mass spectrometry as a tool in the screening of patients with fibromyalgia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo V. S. Alves ◽  
Lanaia I. L. Maciel ◽  
Ruver R. F. Ramalho ◽  
Leomir A. S. Lima ◽  
Boniek G. Vaz ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Principal Component ◽  
Depression And Anxiety ◽  
Multivariate Classification ◽  
Successive Projections Algorithm ◽  
Linear Discriminant ◽  
Paper Spray Ionization ◽  
Chemical Work ◽  
Successive Projections

AbstractFibromyalgia is a rheumatological disorder that causes chronic pain and other symptomatic conditions such as depression and anxiety. Despite its relevance, the disease still presents a complex diagnosis where the doctor needs to have a correct clinical interpretation of the symptoms. In this context, it is valid to study tools that assist in the screening of this disease, using chemical work techniques such as mass spectroscopy. In this study, an analytical method is proposed to detect individuals with fibromyalgia (n = 20, 10 control samples and 10 samples with fibromyalgia) from blood plasma samples analyzed by mass spectrometry with paper spray ionization and subsequent multivariate classification of the spectral data (unsupervised and supervised), in addition to the treatment of selected variables with possible associations with metabolomics. Exploratory analysis with principal component analysis (PCA) and supervised analysis with successive projections algorithm with linear discriminant analysis (SPA-LDA) showed satisfactory results with 100% accuracy for sample prediction in both groups. This demonstrates that this combination of techniques can be used as a simple, reliable and fast tool in the development of clinical diagnosis of Fibromyalgia.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

scholarly journals Metabolomics-based authentication of wines according to grape variety

2019 ◽  
Vol 37 (No. 4) ◽  
pp. 239-245 ◽  
Author(s):  
Leos Uttl ◽  
Kamila Hurkova ◽  
Vladimir Kocourek ◽  
Jana Pulkrabova ◽  
Monika Tomaniova ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Statistical Models ◽  
High Performance ◽  
Geographical Origin ◽  
Principal Component ◽  
White Wine ◽  
Tandem Mass ◽  
Grape Variety ◽  
Varietal Identification

In 2008, the European Commission highlighted the risk of wine mislabelling regarding the geographical origin and varietal identification. While analytical methods for the identification of wine by geographical origin exist, a reliable strategy for authentication of wine variety is still missing. Here, we investigate the suitability of the metabolomic fingerprinting of ethyl acetate wine extracts, using ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. In total, 43 white wine samples (three varieties) were analysed within our study. The generated data were processed by principal component analysis and then by partial least squares discriminant analysis. The resulting statistical models were validated and assessed according to their R2 (cum) and Q2 (cum) parameters. The most promising models were based on positive ionisation data, enabling successful classification of 92% of wine samples.

scholarly journals Characterization and classification of Romanian acacia honey based on its physicochemical parameters and chemometrics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mihaela Emanuela Crăciun ◽  
Oana Cristina Pârvulescu ◽  
Andreea Cristina Donise ◽  
Tănase Dobre ◽  
Dumitru Radu Stanciu
Keyword(s):  
Physicochemical Parameters ◽  
Stable Carbon Isotopes ◽  
Reducing Sugars ◽  
Activity Ratio ◽  
Sugar Content ◽  
Principal Component ◽  
Reducing Sugar ◽  
Linear Discriminant ◽  
Acacia Honey

AbstractThree groups of Romanian acacia honey, i.e., pure, directly adulterated (by mixing the pure honey with three sugar syrups), and indirectly adulterated (by feeding the bees with the same syrups), were characterized and discriminated based on their physicochemical parameters. Moisture, ash, 5-hydroxymethylfurfural (HMF), reducing sugars (fructose and glucose), and sucrose contents, free acidity, diastase activity, ratio between stable carbon isotopes of honey and its proteins (δ13CH and δ13CP) were evaluated. Adulteration led to a significant increase in sucrose content, HMF level, and Δδ13C = δ13CH‒δ13CP as well a decrease in reducing sugar content and diastase activity. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to experimental data in order to distinguish between pure and adulterated honey. The most relevant discriminative parameters were diastase activity, HMF, sucrose, and reducing sugar contents. Posterior classification probabilities and classification functions obtained by LDA revealed that 100% of honey samples were correctly assigned to their original group.

scholarly journals Classification of Multi-Frequency Polarimetric SAR Images Based on Multi-Linear Subspace Learning of Tensor Objects

2015 ◽  
Vol 7 (7) ◽  
pp. 9253-9268 ◽  
Author(s):  
Chun Liu ◽  
Junjun Yin ◽  
Jian Yang ◽  
Wei Gao
Keyword(s):  
Linear Subspace ◽  
Principal Component ◽  
Subspace Learning ◽  
Classification Performance ◽  
Polarimetric Sar ◽  
Third Order ◽  
Sar Images ◽  
Linear Discriminant ◽  
Multi Band

One key problem for the classification of multi-frequency polarimetric SAR images is to extract target features simultaneously in the aspects of frequency, polarization and spatial texture. This paper proposes a new classification method for multi-frequency polarimetric SAR data based on tensor representation and multi-linear subspace learning (MLS). Firstly, each cell of the SAR images is represented by a third-order tensor in the frequency, polarization and spatial domains, with each order of tensor corresponding to one domain. Then, two main MLS methods, i.e., multi-linear principal component analysis (MPCA) and multi-linear extension of linear discriminant analysis (MLDA), are used to learn the third-order tensors. MPCA is used to analyze the principal component of the tensors. MLDA is applied to improve the discrimination between different land covers. Finally, the lower dimension subtensor features extracted by the MPCA and MLDA algorithms are classified with a neural network (NN) classifier. The classification scheme is accessed using multi-band polarimetric SAR images (C-, L- and P-band) acquired by the Airborne Synthetic Aperture Radar (AIRSAR) sensor of the Jet Propulsion Laboratory (JPL) over the Flevoland area. Experimental results demonstrate that the proposed method has good classification performance in comparison with the classic multi-band Wishart classifier. The overall classification accuracy is close to 99%, even when the number of training samples is small.

Rapid classification of enzymes in cleaning products by hydrolysis, mass spectrometry and linear discriminant analysis

2008 ◽  
Vol 22 (22) ◽  
pp. 3667-3672 ◽  
Author(s):  
Miriam Beneito-Cambra ◽  
José Manuel Herrero-Martínez ◽  
Ernesto F. Simó-Alfonso ◽  
Guillermo Ramis-Ramos
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Linear Discriminant ◽  
Cleaning Products

scholarly journals Characterizing Protein Shape by a Volume Distribution Asymmetry Index

2012 ◽  
Vol 6 (1) ◽  
pp. 20-27
Author(s):  
Nicola Arrigo ◽  
Paola Paci ◽  
Luisa Di Paola ◽  
Daniele Santoni ◽  
Micol De Ruvo ◽  
...  
Keyword(s):  
Principal Component ◽  
Shape Index ◽  
Asymmetry Index ◽  
Volume Distribution ◽  
Linear Discriminant ◽  
Contact Maps ◽  
Topological Features ◽  
Three Space ◽  
Protein Shape

A fully quantitative shape index relying upon the asymmetry of mass distribution of protein molecules along the three space dimensions is proposed. Multidimensional statistical analysis, based on principal component extraction and subsequent linear discriminant analysis, showed the presence of three major ‘attractor forms’ roughly correspondent to rod-like, discoidal and spherical shapes. This classification of protein shapes was in turn demonstrated to be strictly connected with topological features of proteins, as emerging from complex network invariants of their contact maps.

scholarly journals Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Giuseppina P. Parpinello ◽  
Arianna Ricci ◽  
Panagiotis Arapitsas ◽  
Andrea Curioni ◽  
Luigi Moio ◽  
...  
Keyword(s):  
Principal Component ◽  
Rapid Screening ◽  
Support Vector ◽  
Red Wines ◽  
Grape Variety ◽  
Absorption Bands ◽  
Linear Discriminant ◽  
Mir Spectroscopy ◽  
Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

scholarly journals Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4166 ◽  
Author(s):  
Elisabeta-Irina Geană ◽  
Corina Teodora Ciucure ◽  
Constantin Apetrei ◽  
Victoria Artem
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Total Variance ◽  
Red Wines ◽  
Linear Discriminant ◽  
Vis Spectroscopy ◽  
Ft Ir ◽  
Wine Authentication ◽  
Analytical Approaches

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

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