scholarly journals Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth

Plants ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1826
Author(s):  
Veronica Conti ◽  
Marco Romi ◽  
Sara Parri ◽  
Iris Aloisi ◽  
Giovanni Marino ◽  
...  
Keyword(s):  
Stress Condition ◽  
Principal Component ◽  
Morphological Parameters ◽  
Solanum Lycopersicum L ◽  
Commercial Cultivars ◽  
Use Efficiency ◽  
Tomato Cultivars ◽  
Vegetative And Reproductive Growth ◽  
Selection Of

Irrigation is fundamental for agriculture but, as climate change becomes more persistent, there is a need to conserve water and use it more efficiently. It is therefore crucial to identify cultivars that can tolerate drought. For economically relevant crops, such as tomatoes, this purpose takes on an even more incisive role and local agrobiodiversity is a large genetic reservoir of promising cultivars. In this study, nine local Italian cultivars of tomatoes plus four widely used commercial cultivars were considered. These experienced about 20 d of drought, either at vegetative or reproductive phase. Various physio-morphological parameters were monitored, such as stomatal conductance (gs), photosynthesis (A), water use efficiency (WUE), growth (GI) and soil water content (SWC). The different responses and behaviors allowed to divide the cultivars into three groups: tolerant, susceptible, and intermediate. The classification was also confirmed by a principal component analysis (PCA). The study, in addition to deepening the knowledge of local Italian tomato cultivars, reveals how some cultivars perform better under stress condition than commercial ones. Moreover, the different behavior depends on the genotype and on the growth phase of plants. In fact, the Perina cultivar is the most tolerant during vegetative growth while the Quarantino cultivar is mostly tolerant at reproductive stage. The results suggest that selection of cultivars could lead to a more sustainable agriculture and less wasteful irrigation plans.

scholarly journals Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 2055
Author(s):  
Marta Bystrzanowska ◽  
Marek Tobiszewski
Keyword(s):  
Green Chemistry ◽  
Principal Component ◽  
Sustainable Chemistry ◽  
Structure Property ◽  
Sustainable Solutions ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Multivariate Ranking ◽  
Selection Of

In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry.

The selection of adaptive region of geomagnetic map based on PCA and GA-BP neural network

2020 ◽  
Author(s):  
Yang Chong ◽  
Dongqing Zhao ◽  
Guorui Xiao ◽  
Minzhi Xiang ◽  
Linyang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Principal Component ◽  
Background Field ◽  
Characteristic Parameters ◽  
Positioning Accuracy ◽  
Matching Performance ◽  
Bp Neural Network Model ◽  
Selection Of

<p>The selection of adaptive region of geomagnetic map is an important factor that affects the positioning accuracy of geomagnetic navigation. An automatic recognition and classification method of adaptive region of geomagnetic background field based on Principal Component Analysis (PCA) and GA-BP neural network is proposed. Firstly, PCA is used to analyze the geomagnetic characteristic parameters, and the independent characteristic parameters containing principal components are selected. Then, the GA-BP neural network model is constructed, and the correspondence between the geomagnetic characteristic parameters and matching performance is established, so as to realize the recognition and classification of adaptive region. Finally, Simulation results show that the method is feasible and efficient, and the positioning accuracy of geomagnetic navigation is improved.</p>

scholarly journals Index selection of tropical maize genotypes for nitrogen use efficiency

Bragantia ◽  
2014 ◽  
Vol 73 (2) ◽  
pp. 153-159 ◽  
Author(s):  
Ítalo Stefanine Correia Granato ◽  
Felipe Pereira Bermudez ◽  
Gabriel Gonçalves dos Reis ◽  
Julio César Dovale ◽  
Glauco Vieira Miranda ◽  
...  
Keyword(s):  
Principal Component ◽  
N Availability ◽  
Tropical Maize ◽  
Soil Leaching ◽  
Nitrogen Use ◽  
Maize Genotypes ◽  
Selection Indices ◽  
Use Efficiency ◽  
Efficient Selection ◽  
Selection Of

Nitrogen (N) limitation in maize crops is related to the fact that the efficiency of nitrogen fertilization in maize does not exceed 50%, primarily due to volatilization, denitrification and soil leaching. Therefore, the development of new nitrogen use efficient (NUE) cultivars is necessary. The aim of the present study was to develop indices for the accurate selection of NUE maize genotypes for use in conditions of both high and low N availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Federal University of Viçosa during October 2010. A total of 39 experimental hybrid combinations and 14 maize lines differing in NUE were evaluated under two N availability conditions. We determined the relative importance of the studied characters using principal component analysis, factor analysis and by developing efficient selection indices. We conclude that indirect and early selection of tropical maize genotypes can be performed using the indices I HN = 0.022 SDM + 0.35 RSDM + 0.35 RL A + 0.35 NUE for high N availability environments and I LN = -0.06 RSDM + 0.35 RSA A + 0.35 RL A + 0.39 SDM for low N availability environments.

Pallet-level Classification Using Principal Component Analysis in Ensemble Learning Model

Mekatronika ◽  
2020 ◽  
Vol 2 (1) ◽  
pp. 23-27
Author(s):  
Chun Sern Choong ◽  
Ahmad Fakhri Ab. Nasir ◽  
Muhammad Aizzat Zakaria ◽  
Anwar P.P. Abdul Majeed ◽  
Mohd Azraai Mohd Razman
Keyword(s):  
Principal Component Analysis ◽  
Ensemble Learning ◽  
Radio Frequency Identification ◽  
Principal Component ◽  
Component Analysis ◽  
Forest Models ◽  
Random Forest Models ◽  
Frequency Identification ◽  
Selection Of

In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models.

scholarly journals Tropical maize selection indexes genotypes for efficiency in use of nutrients: phosphorus

Revista CERES ◽  
2017 ◽  
Vol 64 (3) ◽  
pp. 266-273 ◽  
Author(s):  
Gabriel Gonçalves dos Reis ◽  
Felipe Bermudez Pereira ◽  
Italo Stefanine Correia Granato ◽  
Júlio César DoVale ◽  
Roberto Fritsche-Neto
Keyword(s):  
Selection Index ◽  
Principal Component ◽  
P Availability ◽  
Tropical Maize ◽  
Maize Cultivars ◽  
Maize Genotypes ◽  
Low Phosphorus ◽  
Use Efficiency ◽  
Selection Indexes ◽  
Selection Of

ABSTRACT Brazil generates an annual demand for more than 2.83 million tons of phosphate fertilizers. Part of this is due to low P use efficiency (PUE) by plants, particularly in current maize cultivars. Thus, the aim of this study was to create indexes that allow accurate selection of maize genotypes with high PUE under conditions of either low or high P availability. The experiment was conducted in a greenhouse (20º45'14"S; 42º52'53"W) at the Universidade Federal de Viçosa in October 2010. We evaluated 39 experimental hybrid combinations and 14 maize inbred lines with divergent PUE under two conditions of P availability. The relative importance of the traits studied was analyzed and estimated by principal component analysis, factor analysis, and establishment of selection indexes. To obtain genotypes responsive to high P availability, the index SIHP (selection index for high phosphorus) = 0.3985 RDM + 0.3099 SDM + 0.5567 RLLAT + 0.2340 PUEb - 0.1139 SRS is recommended. To obtain genotypes tolerant to low P availability, the index SILP (selection index for low phosphorus) = 0.3548 RDM + 0.3996 RLLAT + 0.3344 SDM + 0.0041 SH/RS - 0.1019 SRS is suggested.

scholarly journals Analytically Directed Flavor Selection in Breeding Food Crops

2003 ◽  
Vol 128 (5) ◽  
pp. 711-720 ◽  
Author(s):  
Yan Wang ◽  
Stanley J. Kays
Keyword(s):  
Ipomoea Batatas ◽  
Principal Component ◽  
Food Crops ◽  
Breeding Programs ◽  
Active Compounds ◽  
Large Numbers ◽  
Trimethyl Benzene ◽  
And Cluster Analysis ◽  
Selection Of

Flavor quality is one of the most difficult traits to select in plant breeding programs due to the large number of sensory panelists required, the small number of samples that can be evaluated per day, and the subjectivity of the results. Using sweetpotato [Ipomoea batatas (L.) Lam.] as a model, clones exhibiting distinctly different flavors were analyzed for sugars, nonvolatile acids, and aroma chemistry to identify the critical flavor components. Differences in sugars, sucrose equivalents, nonvolatile acids, and 19 odor-active compounds were identified that accounted for differences in flavor among the clones. Using the intensity of the aroma per microliter for each of the 17 most important aroma-active compounds (maltol, 5-methyl-2-furfural, 2-acetyl furan, 3-furaldehyde, 2-furmethanol, benzaldehyde, phenylacetaldehyde, β-ionone, 1,2,4-trimethyl benzene, 2-pentyl furan, 2,4-decadienal, 2,4-nonadienal, linalool, geraniol, cyperene, α-copane and a sesquiterpene) and the relative sweetness of individual sugars × their respective concentrations, multivariate (principal component and cluster) analysis allowed accurate classification of the clones according to flavor type without sensory analysis. The level of precision was such that sweetness, starch hydrolysis potential, and the concentration of β-carotene could be accurately predicted by quantifying specific volatiles. Analytical assessment of flavor would greatly facilitate the accurate evaluation of large numbers of progeny, the simultaneous selection of multiple flavor types, and the development of superior new cultivars for a wide cross-section of food crops.

scholarly journals Selection of Experimental Hybrids of Strawberry Using Multivariate Analysis

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 598 ◽  
Author(s):  
Eneide Barth ◽  
Juliano Tadeu Vilela de Resende ◽  
Aline Fabiana Paladini Moreira ◽  
Keny Henrique Mariguele ◽  
André Ricardo Zeist ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Block Design ◽  
Selection Index ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
High Variability ◽  
Soluble Solids ◽  
Fresh Market ◽  
Commercial Cultivars ◽  
Selection Of

The selection of superior strawberry genotypes is a complex process due to the high variability after hybridization that is caused by the octoploid nature and the heterozygosis, making the selection of multiple traits difficult. This study aimed to select strawberry hybrids with the potential for fresh consumption and/or processing by applying multivariate analysis to obtain traits of interest simultaneously. Hybrids were obtained from the crossing among seven commercial cultivars, defining a selection of 10% of them. The experimental design consisted of an augmented block design, with two commercial cultivars, Camarosa and Camino Real, as the controls. Different variables, including the number and average mass of commercial fruits, total fruit mass, pH, soluble solids (SS), titratable acidity (TA), SS/TA ratio, reducing sugars, pectin, ascorbic acid, phenolic compounds, and anthocyanin’s, were assessed. The selection of hybrids was based on the Mulamba and Mock rank-summation index, principal component analysis, and Ward’s hierarchical cluster analysis. The selection index was based on different weights being adopted for fresh market and processing. The assessed traits had high variability between hybrids. The highest selection gains were obtained for production traits, but the different weight assignment resulted in different classifications of hybrids for both fresh consumption and processing. Most of the hybrids selected by the index remained in the same group in the principal component and hierarchical cluster analyses, which indicates that multivariate analysis is a valuable tool for assisting in the selection of superior hybrids in the strawberry crop.

Early selection of black spruce using physiological and morphological criteria

1993 ◽  
Vol 23 (4) ◽  
pp. 657-664 ◽  
Author(s):  
A.M. Sulzer ◽  
M.S. Greenwood ◽  
W.H. Livingston ◽  
Greg Adams
Keyword(s):  
Exchange Rates ◽  
Cold Hardiness ◽  
Black Spruce ◽  
Morphological Parameters ◽  
Seedling Height ◽  
Morphological Criteria ◽  
Early Testing ◽  
Use Efficiency ◽  
Cold Hardy ◽  
Selection Of

A retrospective test of 36 half-sib black spruce (Piceamariana (Mill.) B.S.P.) families was initiated using surplus seed from the same families growing in six 10-year-old test plantations in New Brunswick. Height, diameter, cold hardiness, gas exchange rates, chlorophyll content, and leaf weight/leaf area ratios of the 3-year-old greenhouse-grown seedlings were determined and related to 10-year field height. The variables that correlated most highly with height at age 10 were seedling height (r = 0.491) and diameter (r = 0.441). Seedling cold hardiness was significantly correlated with both 3-year (r = −0.508) and 10-year height (r = −0.337), the better growing families being more cold hardy. Although photosynthesis and the ratio of photosynthesis to transpiration (a measure of "instantaneous" water use efficiency) were correlated with seedling height, neither of these measures showed a significant relationship with height at age 10. The potential usefulness of both physiological and morphological parameters for early testing purposes is discussed.

scholarly journals Classification of different tomato seed cultivars by multispectral visible-near infrared spectroscopy and chemometrics

2016 ◽  
Author(s):  
Santosh Shrestha ◽  
Lise Deleuran ◽  
René Gislum
Keyword(s):  
Discriminant Analysis ◽  
Near Infrared ◽  
Principal Component ◽  
Error Rates ◽  
Classification Error ◽  
Support Vector ◽  
Tomato Seed ◽  
Tomato Cultivars ◽  
Non Destructive

The feasibility of rapid and non-destructive classification of five different tomato seed cultivars was investigated by using visible and short-wave near infrared (Vis-NIR) spectra combined with chemometric approaches. Vis-NIR spectra containing 19 different wavelengths ranging from 375 nm to 970 nm were extracted from multispectral images of tomato seeds. Principal component analysis (PCA) was used for data exploration, while partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were used to classify the five different tomato cultivars. The results showed very good classification accuracy for two independent test sets ranging from 94% to 100% for all tomato cultivars irrespective of chemometric methods. The overall classification error rates were 3.2% and 0.4% for the PLS-DA and SVM-DA calibration models, respectively. The results indicate that Vis-NIR spectra have the potential to be used for non-destructive discrimination of tomato seed cultivars with an opportunity to integrate them into plant genetic resource management, plant variety protection or registration programmes.

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