Discriminant Analysis

McGarigal, Kevin; Stafford, Susan G.; Cushman, Sam

doi:10.1007/978-1-4612-1288-1_4

Cited by 14 publications

(11 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discriminant Function Analysis is one of the most commonly methods used to decide which variables allow correct classification of a set of objects [ 30 , 31 ]. DFA was used for discrimination of the data obtained from e-nose analysis of spirit beverages.…”

Section: Resultsmentioning

confidence: 99%

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

Wiśniewska

Śliwińska

Namieśnik

et al. 2016

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

Spirit beverages are a diverse group of foodstuffs. They are very often counterfeited which cause the appearance of low quality products or wrongly labelled products on the market. It is important to find a proper quality control and botanical origin method enabling the same time preliminary check of the composition of investigated samples, which was the main goal of this work. For this purpose, the usefulness of electronic nose based on ultra-fast gas chromatography (fast GC e-nose) was verified. A set of 24 samples of raw spirits, 33 samples of vodkas, and 8 samples of whisky were analysed by fast GC e-nose. Four data analysis methods were used. The PCA was applied for the visualization of dataset, observation of the variation inside groups of samples, and selection of variables for the other three statistical methods. The SQC method was utilized to compare the quality of the samples. Both the DFA and SIMCA data analysis methods were used for discrimination of vodka, whisky, and spirits samples. The fast GC e-nose combined with four statistical methods can be used for rapid discrimination of raw spirits, vodkas, and whisky and in the same for preliminary determination of the composition of investigated samples.

show abstract

Section: Resultsmentioning

confidence: 99%

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

Wiśniewska

Śliwińska

Namieśnik

et al. 2016

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

show abstract

“…To verify the LDA assumptions, [46] data is tested if have: multivariate normal distribution; same withingroup variance-covariance structure for all groups (groups have equal dispersions); multicollinearity (requires that no discriminating variable be perfectly correlated with another variable); and presence of outliers.…”

Section: Discussionmentioning

confidence: 99%

Semi‐Quantitative Discrimination of Honey Adulterated with Cane Sugar Solution by an ETongue

Dias

Bruni

Anizelli

et al. 2022

Chemistry & Biodiversity

View full text Add to dashboard Cite

This study successfully applied a potentiometric E‐tongue with 20 cross‐selectivity lipidic polymeric membranes in the discrimination of three semi‐quantitative groups, that represented the following intervals of honey adulteration percentage with cane sugar: 0 %; [0, 10]%; [10, 20]% of adulteration. We analysed five different types of Portuguese honey; five brands of cane sugar were added to the adulterated samples; a comparative analysis was then performed. Linear discriminant analysis coupled with a tabu search algorithm for feature selection was applied to the ETongue's analytical data to select the best model. A discriminant model with 12 sensors was obtained. This model classified correctly all samples in both in internal (train data, 15 samples) and external validation (test data,10 samples). Also, multiple linear regression with tabu search was applied to verify if ETongue's data would allow quantifying the honey's adulteration level. The results showed that it was possible to obtain a quantitative model but with unsatisfactory predictive performance in the test data group (external validation), giving, in general, values below the expected concentrations. E‐tongue is a real‐time green, flexible and low‐cost analytical tool that requires minimum sample preparation and no special technical skills, being a promising tool for everyday application.

show abstract

“…B is the between-groups matrix, and W is the within-group matrix. The Eigenvalue can be explained as the ratio of the between-groups sum of squares to the within-group sum of squares (McGarigal et al, (2000) [14] .…”

Section: Wilk's Lambdamentioning

confidence: 99%

Fisher’s discriminant modeling for human platelet parameters

2024

Int. J. Stat. Appl. Math.

View full text Add to dashboard Cite

This study employed multivariate modeling techniques to explore the relationship between platelet parameters and demographic factors. Assumptions such as normality, multicollinearity, and covariance matrix equality were rigorously assessed. Data normalization using the Box-Cox method improved normality, facilitating more robust analyses. Multivariate analysis of Variance revealed significant variations in platelet parameters across demographic groups. Furthermore, linear discriminant analysis demonstrated the capacity to classify individuals based on platelet parameters, particularly concerning gender. The findings underscore the importance of platelet parameters in understanding population characteristics and their potential implications for medical research and clinical practice.

show abstract

Discriminant Analysis

Cited by 14 publications

References 65 publications

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

The Verification of the Usefulness of Electronic Nose Based on Ultra-Fast Gas Chromatography and Four Different Chemometric Methods for Rapid Analysis of Spirit Beverages

Semi‐Quantitative Discrimination of Honey Adulterated with Cane Sugar Solution by an ETongue

Fisher’s discriminant modeling for human platelet parameters

Contact Info

Product

Resources

About