scholarly journals Differences between strawberry cultivars based on principal component analysis

2020 ◽  
Vol 8 (6) ◽  
pp. 136-145
Author(s):  
Darlei Michalski Lambrecht ◽  
Alessandro Dal'Col Lúcio ◽  
Maria Inês Diel ◽  
Denise Schmidt ◽  
Francieli De Lima Tartaglia ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Latent Variables ◽  
Covariance Structure ◽  
Principal Component ◽  
Titratable Acidity ◽  
Component Analysis ◽  
Soluble Solids ◽  
Family Farms ◽  
Multivariate Technique ◽  
Strawberry Cultivars

Strawberry culture is of extreme economic importance, especially for small producers, as it has the capacity to add value to small family farms, in addition to absorbing family labor. Principal component analysis (PCA) is a multivariate technique for modeling covariance structure, where a basic idea is to find latent variables that represent linear combinations of a group of variables under study, which in turn are related between itself. In this way, the objective of the work was estimated, through the analysis of main components (PCA), as relationships between development variables, products and fruit quality in different strawberry cultivars. The design used was a randomized block with 11 treatments, consisting of strawberry cultivars of Italian and American origins, with four replications. During the culture cycle, the following variables were evaluated: phyllochron, number of commercial (FC) and non-commercial (FNC) fruits, mass of commercial (MFC) and non-commercial (MFNC) fruits, total titratable acidity (AT), total soluble quantities (SST) and total soluble ratio, titratable acidity (SST / AT). The relationships between the variables were evaluated by the PCA analysis and the results were plotted on the Biplot graph. From the analysis, it was possible to identify the relationships between the variables that show how to cultivate the same photoperiod and the same characteristic origin. Growing short photoperiods are more productive, for example, as the neutral photoperiod has less phyllochron and less acidity. The increase in soluble solids can cause a reduction in acidity, which is one of the characteristics that add flavor to the fruit.

scholarly journals Repeatability coefficients and number of measurements for evaluating traits in strawberry

2020 ◽  
Vol 42 ◽  
pp. e43357 ◽  
Author(s):  
Maria Inês Diel ◽  
Alessandro Dal'Col Lúcio ◽  
Tiago Olivoto ◽  
Marcos Vinícius Marques Pinheiro ◽  
Dionatan Ketzer Krysczun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Block Design ◽  
Principal Component ◽  
Titratable Acidity ◽  
Component Analysis ◽  
Soluble Solids ◽  
Repeatability Coefficient ◽  
Randomized Block Design ◽  
Crop Cycle ◽  
Substrate Mixtures

The objective of this study was to estimate the coefficient of repeatability and the number of measurements required for production and quality variables in a strawberry crop. An experiment was conducted with two strawberry cultivars from two origins grown in four substrate mixtures, totaling 16 treatments, evaluated in a randomized block design with four replications. Mass (MF) and number (NF) of fruits per plant were evaluated as measures of production, and total soluble solids (SST), titratable acidity (AT) and firmness (FIR) of fruits during the crop cycle were evaluated as measures of quality. Subsequently, the repeatability coefficient was estimated by the following methods: analysis of variance (ANOVA), principal component analysis using a correlation matrix (PCcor), principal component analysis using a variance-covariance matrix (PCcov) and structural analysis (SA). The number of measurements was adjusted for each studied variable based on determination coefficients of 0.80, 0.85, 0.90, and 0.95. The repeatability coefficients ranged from low to medium. The ANOVA method gave the lowest r values, while the PCcov method presented the highest values of r. When using the PCcov method, 3.6, 2.9, 6.2, 3.2, and 3.8 measurements were needed to reach 80% confidence for the variables MF, NF, SST, AT, and FIR, respectively, and this increased to 7.3, 14.0, 29.6, 15.4, and 18.1 for 95% confidence in the results for MF, NF, SST, AT, and FIR, respectively.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

scholarly journals Rootstock-scion interaction:5. Effect on the evolution of Cabernet Sauvignon grape ripening

2019 ◽  
Vol 41 (3) ◽  
Author(s):  
Alberto Miele ◽  
Luiz Antenor Rizzon
Keyword(s):  
Principal Component Analysis ◽  
Regression Analysis ◽  
Yield Components ◽  
Polynomial Regression ◽  
Principal Component ◽  
Titratable Acidity ◽  
Soluble Solids ◽  
Cabernet Sauvignon ◽  
Total Soluble Solids ◽  
Wine Composition

Abstract The rootstock effect on grapevine yield components, grape must and wine composition and wine sensory characteristics were evaluated in previous studies. This experiment carried out over five years had the objective to determine the effect of the rootstock on the evolution of variables related to sugar and acidity contents of the juice during grape ripening. The treatments consisted of Cabernet Sauvignon grapevine grafted on rootstocks such as Rupestris du Lot, 101- 14 Mgt, 3309 C, 420A Mgt, 5BB K, 161-49 C, SO4, Solferino, 1103 P, 99 R, 110 R, Gravesac, Fercal, Dogridge and Isabel. The berries were sampled during the grape ripening period, on nine dates during the summer of each year. Taken to the laboratory, they were hand crushed and the juice was centrifuged to separate the solid and liquid phase, where the supernatant was then used for physicochemical analyses. The data were submitted to Principal Component Analysis (PCA) and polynomial regression analysis. The main results show that, at grape maturity, the PCA discriminated mainly the juices of CS/101-14 Mgt, CS/SO4 and CS/Gravesac, which had high density, total soluble solids, total soluble solids/titratable acidity ratio and pH, and CS/Dogridge and CS/Fercal, which had high titratable acidity. The density, total soluble solids, titratable acidity, total soluble solids/titratable acidity ratio increased as grape ripened, but the titratable acidity decreased. However, the increase or decrease rates were lower at the end of the grape ripening cycle according to the variable, and the total soluble solids having the highest increase (116.3%) and the titratable acidity the highest decrease (68.3%).

Assessment of the differences in the physical, chemical and phytochemical properties of four strawberry cultivars using principal component analysis

2016 ◽  
Vol 194 ◽  
pp. 828-834 ◽  
Author(s):  
Dunja Šamec ◽  
Marina Maretić ◽  
Ivana Lugarić ◽  
Aleksandar Mešić ◽  
Branka Salopek-Sondi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Chemical ◽  
Strawberry Cultivars

scholarly journals Adaptive Elastic-Net Sparse Principal Component Analysis for Pathway Association Testing

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Xi Chen
Keyword(s):  
Principal Component Analysis ◽  
Pathway Analysis ◽  
Latent Variables ◽  
Statistical Tests ◽  
Principal Component ◽  
Component Analysis ◽  
Elastic Net ◽  
Sparse Principal Component Analysis ◽  
Modeling Framework ◽  
Adaptive Elastic Net

Pathway or gene set analysis has become an increasingly popular approach for analyzing high-throughput biological experiments such as microarray gene expression studies. The purpose of pathway analysis is to identify differentially expressed pathways associated with outcomes. Important challenges in pathway analysis are selecting a subset of genes contributing most to association with clinical phenotypes and conducting statistical tests of association for the pathways efficiently. We propose a two-stage analysis strategy: (1) extract latent variables representing activities within each pathway using a dimension reduction approach based on adaptive elastic-net sparse principal component analysis; (2) integrate the latent variables with the regression modeling framework to analyze studies with different types of outcomes such as binary, continuous or survival outcomes. Our proposed approach is computationally efficient. For each pathway, because the latent variables are estimated in an unsupervised fashion without using disease outcome information, in the sample label permutation testing procedure, the latent variables only need to be calculated once rather than for each permutation resample. Using both simulated and real datasets, we show our approach performed favorably when compared with five other currently available pathway testing methods.

scholarly journals The Use of the Principal Component Analysis for the Prediction of Superlattices Hard Perovskites and Inverse Perovskites

2021 ◽  
Author(s):  
Boubchir Mohamed Abdeldjalil ◽  
Rachid Boubchir ◽  
Hafid Aourag
Keyword(s):  
Fracture Toughness ◽  
Principal Component Analysis ◽  
Thermal Barrier Coating ◽  
Principal Component ◽  
High Hardness ◽  
Component Analysis ◽  
Thermal Barrier ◽  
Coating Materials ◽  
Multivariate Technique ◽  
Barrier Coating

Abstract We present in this work an approach to predict which compounds among perovskites and inverse perovskites with the potential for achieving high hardness and fracture toughness for applications as a thermal barrier coating (TBC). We employ a throughout multivariate technique based on the principal component analysis (PCA). Among the 129 tested perovskites and inverse perovskites, only ~ 10 compounds may exhibit an interesting potential as thermal barrier coating. These results may serve as a map for the design of perovskite-related new multilayer ultra-hard coating materials.

scholarly journals Evaluation of structure in Pinus halepensis M. stands in North Greece

1997 ◽  
Vol 62 ◽  
Author(s):  
D. Karamanolis ◽  
G. Stamatelos ◽  
P. Gkanatsas
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Stand Structure ◽  
Pinus Halepensis ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Multivariate Technique ◽  
New Variables

The  Principal Component Analysis (P.C.A.) is a multivariate technique useful in  the description and    the revealing of relations between variables in a great number of data. The  structure of Pinus    halepensis forests by P.C.A. was studied. The  method was applied in silvicultural data of Pinus    halepensis forests in Kassandra Peninsula.  Sampling was done on 49 plots spreaded over of the    peninsula. By the analysis of a total of 12 initial variables it was found  that the first 6 principal    components, new variables, interpret almost 83% of the total variance. It  was also found that the    first component, which explains 29.6%, affects the configuration of stand  structure.

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