scholarly journals Urban Environmental Quality Assessment by Spectral Characteristics of Mulberry (Morus L.) Leaves

Environments ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 87
Author(s):  
Snejana Dineva ◽  
Petya Veleva-Doneva ◽  
Zlatin Zlatev
Keyword(s):  
Air Pollution ◽  
Environmental Pollution ◽  
Principal Components ◽  
Latent Variables ◽  
Spectral Characteristics ◽  
Surface Characteristics ◽  
Linear Discriminant ◽  
Mulberry Leaves ◽  
Mulberry Leaf

In this paper, an analysis of the possibility of passive determination of the degree of environmental pollution based on data from the leaf blade of mulberry is made. With existing solutions in this area, the mulberry has been found to be under-researched. A disadvantage of the available solutions is that spectral indices are used, which is not a sufficient criterion for passively determining the degree of air pollution based on the surface characteristics of the mulberry leaves. Methods have been used to reduce the amount of data by latent variables and principal components. It has been found that a kernel variant of the principal components, combined with linear discriminant analysis, is an appropriate method for distinguishing the degree of air pollution from mulberry leaf data. The results obtained can be used to refine the approaches used to passively determine the degree of air pollution in the habitat area of the plant. Methods and software tools could be used to develop mobile applications and new approaches to remote sensing, in express determination of the degree of environmental pollution, according to data from the mulberry leaves.

scholarly journals URBAN ENVIRONMENTAL QUALITY ASSESSMENT BY SHAPE AND SPECTRAL INDICES OF MULBERRY LEAVES

2019 ◽  
Vol 7 (3) ◽  
pp. 184-205
Author(s):  
Snejana Dineva ◽  
Zlatin Zlatev
Keyword(s):  
Quality Assessment ◽  
Environmental Quality ◽  
Principal Components ◽  
Latent Variables ◽  
Support Vector ◽  
Mulberry Leaves ◽  
Environmental Quality Assessment ◽  
Mulberry Leaf ◽  
Vector Machines ◽  
Potential Use

In this paper, an analysis of the potential use of the surface and geometric characteristics of mulberry leaves as parameters for environmental quality assessment is made. Methods have been used to reduce the amount of data of latent variables, linear and kernel variants of principal components. It has been found that a kernel variant of the principal components, combined with nonlinear separating functions of discriminant analysis and a method of support vector machines, are an appropriate methods for distinguishing the degree of air pollution from the mulberry leaf data. The results obtained could be used as preliminary baseline data for future evaluations and studies related to remote monitoring of urban air quality.

How We Can Use Multivariate Statistical Analysis to Predict Creasing of Fabrics

2014 ◽  
Vol 543-547 ◽  
pp. 1930-1933
Author(s):  
Katarína Zelová ◽  
Ludmila Fridrichová
Keyword(s):  
Statistical Analysis ◽  
Principal Components ◽  
Latent Variables ◽  
Analysis Method ◽  
Pca Analysis ◽  
Multivariate Statistical ◽  
Method Of Principal Components ◽  
Innovative Method ◽  
Component Loading

The creasing of textiles was evaluated by means of the innovative method of measuring the angle of recovery. Our aim is to find statistically significant features contributing to the determination of creasing materials. For this purpose, to identify the inner structures of data, the method of PCA analysis was used a method with latent variables. By means of PCA analysis (method of principal components) the original nine characteristics can be reduced to two latent variables, i.e. principal components. The structure and links among the examined features are characterized by methods like: Scree Plot, Score and component loading, Scatrerplot and Dendrogram.

scholarly journals Chlorophyll Assessment and Sensitive Wavelength Exploration for Tea (Camellia sinensis) Based on Reflectance Spectral Characteristics

HortScience ◽  
2008 ◽  
Vol 43 (5) ◽  
pp. 1586-1591 ◽  
Author(s):  
Xiao-li Li ◽  
Yong He
Keyword(s):  
Latent Variables ◽  
Low Cost ◽  
Spectral Characteristics ◽  
Tea Plant ◽  
Nondestructive Method ◽  
Calibration Model ◽  
Analysis Techniques ◽  
Calibration Models ◽  
Chlorophyll Index

A nondestructive method for the determination of chlorophyll index for the tea plant based on reflectance spectral characteristics was investigated. Spectral data were collected from 184 samples with a spectroradiometer in a field experiment. Multivariate analysis techniques, including partial least squares (PLS) and multiple linear regression (MLR), were used for developing calibration models for the determination of chlorophyll index of the tea plant. The best calibration model was achieved using the PLS technique with a correlation coefficient (r) of 0.95, a se of prediction of 3.40, and a bias of 1.9e−06. When the model was used for predicting the unknown samples, good performance was also obtained with r of 0.91, se of calibration of 4.77, and bias of 0.02. Sensitive wavelengths were selected through loading analysis of latent variables in the optimal PLS model, and the validity of these wavelengths was proved by MLR and statistical analysis. Three fingerprint wavelengths (488, 695, and 931 nm) were determined and could potentially be used for developing a simple, low-cost, and efficient instrument for the measurement of chlorophyll index. The results proved the feasibility of reflectance spectra for measurement of chlorophyll index of the tea plant.

Determination of Gasoline Adulteration by Principal Components Analysis-Linear Discriminant Analysis Applied to FTIR Spectra

2006 ◽  
Vol 20 (3) ◽  
pp. 1097-1102 ◽  
Author(s):  
Rita C. C. Pereira ◽  
Vinicius L. Skrobot ◽  
Eustáquio V. R. Castro ◽  
Isabel C. P. Fortes ◽  
Vânya M. D. Pasa
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Ftir Spectra ◽  
Linear Discriminant ◽  
Components Analysis

Effects of Metal Stress on Visible/Near-Infrared Reflectance Spectra of Vegetation

2011 ◽  
Vol 347-353 ◽  
pp. 2735-2738 ◽  
Author(s):  
Guang Yu Chi ◽  
Yi Shi ◽  
Xin Chen ◽  
Jian Ma ◽  
Tai Hui Zheng
Keyword(s):  
Heavy Metal ◽  
Structural Changes ◽  
Near Infrared ◽  
Leaf Surface ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Surface Characteristics ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Near Infrared Reflectance

Vegetation which suffers from heavy metal stresses can cause changes of leaf color, shape and structural changes. The spectral characteristics of vegetation leaves is related to leaf thickness, leaf surface characteristics, the content of water, chlorophyll and other pigments. So the eco-physiology changes of plants can be reflected by spectral reflectance. Studies on the spectral response of vegetation to heavy metal stress can provide a theoretical basis for remote sensing monitoring of metal pollution in soils. In recent decades, there are substantial amounts of literature exploring the effects of heavy metals on vegetation spectra.

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