scholarly journals Stepwise linear discriminant analysis to differentiate Spanish red wines by their Protected Designation of Origin or category using physico-chemical parameters

OENO One ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 86-99
Author(s):  
Rubén Del Barrio Galán ◽  
Marta Bueno Herrera ◽  
Pedro López de la Cuesta ◽  
Silvia Pérez-Magariño
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Average Molecular Weight ◽  
Red Wines ◽  
Grape Variety ◽  
Chemical Parameters ◽  
Good Discrimination ◽  
Linear Discriminant ◽  
Protected Designation Of Origin ◽  
Physico Chemical

Aim: The aim of this work was to determine the physico-chemical variables that differentiating red wines from the “Castilla y León” Spanish region by their Protected Designation of Origin (PDO) and wine category ("young", “oak”, “crianza”, or “reserve”).Methods and results: A total of 135 commercial red wines from four Spanish PDOs in the region of Castilla and León were analysed. Forty physico-chemical parameters, related to classical enological parameters, phenolic and polysacharidic composition, and content of higher alcohols were evaluated. Differences in physico-chemical composition were found in red wines from different PDOs and different categories. Stepwise linear discriminant analysis (SLDA) was applied to find a linear combination of the physico-chemical variables that separate and classify the red wines according to the PDO or category. One SLDA model selected 15 physico-chemical variables that allowed for good discrimination and classification of the wines from different PDOs. The SLDA model selected seven variables for wine category differentiation, but only allowed for good discrimination between young wines and aged wines (“crianza” and “reserve”).Conclusions: The variables that contributed most to the separation of Tempranillo red wines were total polyphenols, total tannins, and absorbance values at 230 nm and 280 nm. The polysaccharides with an average molecular weight of 10 kDa, flavanols, stilbenes and 2-methyl-1-butanol were those most associated with the differentiation of the wines elaborated with the Mencía grape variety. The percentage of polymeric anthocyanins and absorbance at 230 nm could be proposed as good indicators for aged wines, and total tannins for young wines.Significance and impact of the study: This study provides improved knowledge of the physico-chemical variables that could be used as markers of the origin of wines and/or the grape variety (Tempranillo and Mencía) and that allow differentiating young wines from those aged for a long time.

Influence of diet and rennet on the composition of goats’ milk and cheese

2011 ◽  
Vol 78 (2) ◽  
pp. 250-256 ◽  
Author(s):  
María Fresno Baquero ◽  
Sergio Álvarez Ríos ◽  
Elena Rodríguez Rodríguez ◽  
Carlos Díaz Romero ◽  
Jacinto Darias Martín
Keyword(s):  
Trace Elements ◽  
Chemical Composition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Dry Matter ◽  
High Ratio ◽  
Linear Discriminant ◽  
Protected Designation Of Origin

Dry matter, protein, fat, pH, mineral (Na, K, Ca and Mg) and trace elements (Fe, Cu, Zn and Se) concentrations were determined in samples of goats’ milk and in fresh, semi-hard and hard cheeses to study the effect of the goats’ diet and the type of rennet used for the cheese processing of the Palmero Protected Designation of Origin cheeses. Two groups of 20 Palmero goats were fed 2 different diets: a Palmero diet (PD supplied by native forages adapted to subhumid areas, which had a high ratio of long fibre to concentrates (65:35), and an actual diet (AD), the most commonly used by goat farmers, with a low ratio of long fibre to concentrates (35:65). In general, the cheese samples from goats fed with PD had higher mean Ca, Zn, Cu and Se concentrations than the samples obtained from AD fed goats. The diet exhibited a greater influence on the chemical composition of the cheeses than the rennet used in their production. Applying a stepwise linear discriminant analysis a complete percentage of correct classifications of the three types of cheeses according to the diet of the goats was observed.

scholarly journals Development of cardiotoxicity model using ligand-centric and receptor-centric descriptors

2020 ◽  
Vol 4 ◽  
pp. 239784732097125
Author(s):  
Chirag N Patel ◽  
Sivakumar Prasanth Kumar ◽  
Rakesh M Rawal ◽  
Manishkumar B Thaker ◽  
Himanshu A Pandya
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Hybrid Approach ◽  
Classification Model ◽  
Test Set ◽  
Linear Discriminant ◽  
Specificity And Sensitivity ◽  
Physico Chemical

Background: Bioinformatics and statistical analysis have been employed to develop a classification model to distinguish toxic and non-toxic molecules. Aims: The primary objective of this study is to enumerate the cut-off values of various physico-chemical (ligand-centric) and target interaction (receptor-centric) descriptors which forms the basis for classifying cardiotoxic and non-toxic molecules. We also sought correlation of molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) parameters, Lipinski rules, physico-chemical parameters, etc. of human cardiotoxicity drugs. Methods: A training and test set of 91 compounds were applied to linear discriminant analysis (LDA) using 2D and 3D descriptors as discriminating variables representing various molecular modeling parameters to identify which function of descriptor type is responsible for cardiotoxicity. Internal validation was performed using the leave-one-out cross-validation methodology ensuing in good results, assuring the stability of the discriminant function (DF). Results: The values of the statistical parameters Fisher Discriminant Analysis (FDA) and Wilk’s λ for the DF showed reliable statistical significance, as long as the success rate in the prediction for both the training and the test set attained more than 93% accuracy, 87.50% sensitivity and 94.74% specificity. Conclusion: The predictive model was built using a hybrid approach using organ-specific targets for docking and ADMET properties for the FDA (Food and Drug Administration) approved and withdrawn drugs. Classifiers were developed by linear discriminant analysis and the cut-off was enumerated by receiver operating characteristic curve (ROC) analysis to achieve reliable specificity and sensitivity.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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