scholarly journals Local Food as a Tool of Tourism Development in Regions

2021 ◽  
Vol 5 (1) ◽  
pp. 54-68
Author(s):  
Alžbeta Kiráľová ◽  
Lukáš Malec
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Local Food ◽  
Tourism Development ◽  
Random Variable ◽  
Demand And Supply ◽  
Variable Source ◽  
Linear Discriminant ◽  
Food And Beverages ◽  
Source Data

This study aims to identify the importance of local food for both the demand and supply sides and to show how local food can be bounded with tourism development in the region. The data presented are based on secondary and primary research. Secondary research includes the literature review and content analysis of documents. The qualitative research included a questionnaire survey among guests of the gastronomic establishments and entrepreneurs. Partial least squares variant of linear discriminant analysis (PLS-LDA) and partial least squares (PLS) as an alternative to standard multivariate methods were used to show the gastronomic establishments guests' and entrepreneurs' opinions on local seasonal food and beverages. The opinions are moreover related to the economically driven interest of guests and entrepreneurs. Based on the typical random variable source, data were gathered from three Czech regions covering the scope of this study. The significant disputes between opinions on local food and beverages are directly applicable in practice, including individual items.

scholarly journals INVESTIGATING SELECTED GASTRONOMIC TRENDS FROM DEMAND AND SUPPLY SIDE

2020 ◽  
Vol 3 (1) ◽  
pp. 27-33
Author(s):  
Alžbeta Kiráľová ◽  
Lukáš Malec
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Stability Property ◽  
Supply Side ◽  
Economic Interest ◽  
Slow Food ◽  
Demand And Supply ◽  
Linear Discriminant ◽  
Quantitative And Qualitative Research

The study aims to identify the role of the selected gastronomic trends in the Czech gastronomic establishments. The study highlights the key findings of quantitative and qualitative research provided with the focus on both the demand and the supply side. It is focusing on the dispute between guests’ opinions and entrepreneurs’ views based on few variables for gastronomic trends. Entrepreneurs’ and guests’ views in three Czech Regions were studied in one set with notes incorporated on possible mutual differences between them. The partial least squares variant of linear discriminant analysis (plsLDA) and partial least squares (PLS) was applied as they give a clear superiority due to both, interpretational and stability property. It was proven that the partial least squares variants lead to direct answers to questions in the studied field. Participation/organization of food festivals and slow food are positively related. The significant tasks emerge to a great extent covering differences between guests´ and entrepreneurs´ opinions. On the other hand, the connection of economic interest to gastronomic trends is relatively weak.

Creativity as a Tool of Tourism Development

2020 ◽  
pp. 504-530
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

scholarly journals PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem

2020 ◽  
Vol 43 (2) ◽  
pp. 233-249
Author(s):  
Adolphus Wagala ◽  
Graciela González-Farías ◽  
Rogelio Ramos ◽  
Oscar Dalmau
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Linear Regression ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Classification Error ◽  
Support Vector ◽  
Linear Discriminant ◽  
Generalized Linear Regression

This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining  it with logistic regression and  linear  discriminant analysis,  to  get a  partial least  squares generalized linear  regression-logistic regression model (PLSGLR-log),  and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative  study  of  the obtained  classifiers with   the   classical  methodologies like  the k-nearest  neighbours (KNN), linear   discriminant  analysis  (LDA),   partial  least  squares discriminant analysis (PLSDA),  ridge  partial least squares (RPLS), and  support vector machines(SVM)  is  then  carried  out.    Furthermore,  a  new  methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based  on the lowest  classification error  rates  compared to  the  others  when  applied   to  the  types   of data   are considered;  the  un- preprocessed and preprocessed.

Creativity as a Tool of Tourism Development

2017 ◽  
pp. 67-93
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods

2007 ◽  
Vol 15 (5) ◽  
pp. 291-297 ◽  
Author(s):  
Hai-Yan Fu ◽  
Shuang-Yan Huan ◽  
Lu Xu ◽  
Li-Juan Tang ◽  
Jian-Hui Jiang ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Geographical Origin ◽  
Quality Analysis ◽  
Moving Window ◽  
Linear Discriminant ◽  
Pattern Recognition Methods

Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.

scholarly journals Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat

Foods ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1865
Author(s):  
Alberto Ortiz ◽  
Lucía León ◽  
Rebeca Contador ◽  
David Tejerina
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Raw Material ◽  
Modified Atmosphere ◽  
Least Squares Regression ◽  
Linear Discriminant

This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.

scholarly journals Application of UHPLC Fingerprints Combined with Chemical Pattern Recognition Analysis in the Differentiation of Six Rhodiola Species

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6855
Author(s):  
Didi Ma ◽  
Lijun Wang ◽  
Yibao Jin ◽  
Lifei Gu ◽  
Xiean Yu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Rhodiola Rosea ◽  
Linear Discriminant ◽  
Chemical Pattern ◽  
Pattern Recognition Analysis ◽  
Chemical Pattern Recognition

Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.

Face and Ethnical Group Recognition with Images of Different Resolutions

2021 ◽  
Vol 9 (1) ◽  
pp. 140-147
Author(s):  
Chong Lu ◽  
Yan Ren ◽  
Liying Han
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Two Dimensional ◽  
Face Database ◽  
Linear Discriminant ◽  
Better Than

In this paper, a dataset for Xinjiang minority ethnical groups is introduced, and implementation of two dimensional Linear Discriminant Analysis (2DLDA) and two-dimensional Partial Least Squares (2DPLS) is investigated. Two important topics for face recognition and the ethnicity recognition are investigated for database with different image resolutions. Experiments show that 2DLDA performances better than 2DPLS on our face database.

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