scholarly journals Tomato quality based on colorimetric characteristics of digital images

2020 ◽  
Vol 24 (8) ◽  
pp. 567-572 ◽  
Author(s):  
Thaísa B. Bello ◽  
Anderson G. Costa ◽  
Thainara R. da Silva ◽  
Juliana L. Paes ◽  
Marcus V. M. de Oliveira
Keyword(s):  
Multivariate Analysis ◽  
Principal Components ◽  
Digital Camera ◽  
Color Model ◽  
Clustering Method ◽  
Data Set ◽  
Tomato Quality ◽  
Physico Chemical ◽  
Maturation Stages ◽  
Components Analysis

ABSTRACT Results of evaluations using optical evaluation methods may be correlated with tomato quality and maturation. In this context, the objective of this study was to evaluated the correlation between tomato colorimetric and physico-chemical variables, clustering them as a function of maturation stages, using multivariate analysis. The experiment was conducted using 150 fruits and three maturation stages (immature, light red and mature). The physico-chemical variables were evaluated through traditional methods. The colorimetric variables were assessed on images in RGB color model taken with a digital camera. The correlation between colorimetric and physico-chemical variables was analyzed using the Pearson’s coefficient. Principal components analysis and k-means clustering method was applied to three data set: RGB isolated variables; colorimetric variables calculated by relation between the RGB bands (colorimetric indexes); and physico-chemical variables. The colorimetric variables present higher explanatory capacity of the maturation variation than physico-chemical variables. The colorimetric indexes presented higher performance in clustering (accuracy of 0.98) tomatoes as a function of maturation.

scholarly journals Dietary patterns in UK adolescents obtained from a dual-source FFQ and their associations with socio-economic position, nutrient intake and modes of eating

2013 ◽  
Vol 17 (7) ◽  
pp. 1476-1485 ◽  
Author(s):  
Kate Northstone ◽  
Andrew DAC Smith ◽  
Victoria L Cribb ◽  
Pauline M Emmett
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Data Set ◽  
Sociodemographic Differences ◽  
Traditional Health ◽  
Parents And Children ◽  
Components Analysis ◽  
Positive Correlations ◽  
Dietary Data

AbstractObjectiveTo derive dietary patterns using principal components analysis from separate FFQ completed by mothers and their teenagers and to assess associations with nutrient intakes and sociodemographic variables.DesignTwo distinct FFQ were completed by 13-year-olds and their mothers, with some overlap in the foods covered. A combined data set was obtained.SettingAvon Longitudinal Study of Parents and Children (ALSPAC), Bristol, UK.SubjectsTeenagers (n 5334) with adequate dietary data.ResultsFour patterns were obtained using principal components analysis: a ‘Traditional/health-conscious’ pattern, a ‘Processed’ pattern, a ‘Snacks/sugared drinks’ pattern and a ‘Vegetarian’ pattern. The ‘Traditional/health-conscious’ pattern was the most nutrient-rich, having high positive correlations with many nutrients. The ‘Processed’ and ‘Snacks/sugared drinks’ patterns showed little association with important nutrients but were positively associated with energy, fats and sugars. There were clear gender and sociodemographic differences across the patterns. Lower scores were seen on the ‘Traditional/health conscious’ and ‘Vegetarian’ patterns in males and in those with younger and less educated mothers. Higher scores were seen on the ‘Traditional/health-conscious’ and ‘Vegetarian’ patterns in girls and in those whose mothers had higher levels of education.ConclusionsIt is important to establish healthy eating patterns by the teenage years. However, this is a time when it is difficult to accurately establish dietary intake from a single source, since teenagers consume increasing amounts of foods outside the home. Further dietary pattern studies should focus on teenagers and the source of dietary data collection merits consideration.

The use of principal components analysis for the investigation of an organic air pollutants data set

1984 ◽  
Vol 18 (11) ◽  
pp. 2471-2478 ◽  
Author(s):  
J. Smeyers-Verbeke ◽  
J.C. Den Hartog ◽  
W.H. Dehker ◽  
D. Coomans ◽  
L. Buydens ◽  
...  
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Air Pollutants ◽  
Data Set ◽  
Components Analysis

scholarly journals Multivariate Characterization of Hydrogen Balmer Emission in Cataclysmic Variables

2006 ◽  
Vol 23 (3) ◽  
pp. 106-118 ◽  
Author(s):  
Gordon E. Sarty ◽  
Kinwah Wu
Keyword(s):  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Function Analysis ◽  
Cataclysmic Variables ◽  
Data Set ◽  
Analysis Methods ◽  
Wide Range ◽  
Components Analysis

AbstractThe ratios of hydrogen Balmer emission line intensities in cataclysmic variables are signatures of the physical processes that produce them. To quantify those signatures relative to classifications of cataclysmic variable types, we applied the multivariate statistical analysis methods of principal components analysis and discriminant function analysis to the spectroscopic emission data set of Williams (1983). The two analysis methods reveal two different sources of variation in the ratios of the emission lines. The source of variation seen in the principal components analysis was shown to be correlated with the binary orbital period. The source of variation seen in the discriminant function analysis was shown to be correlated with the equivalent width of the Hβ line. Comparison of the data scatterplot with scatterplots of theoretical models shows that Balmer line emission from T CrB systems is consistent with the photoionization of a surrounding nebula. Otherwise, models that we considered do not reproduce the wide range of Balmer decrements, including ‘inverted’ decrements, seen in the data.

Sensory Lexicon for Fresh Squeezed and Processed Orange Juices

2008 ◽  
Vol 14 (5_suppl) ◽  
pp. 131-141 ◽  
Author(s):  
P. Ruiz Pérez-Cacho ◽  
H. Galan-Soldevilla ◽  
K. Mahattanatawee ◽  
A. Elston ◽  
R.L. Rouseff
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Processing Technology ◽  
Sensory Panel ◽  
Geometric Means ◽  
Thermally Processed ◽  
Components Analysis ◽  
Tactile Sensations

The aim of this study was to develop a flavor vocabulary (odor, aroma basic tastes and trigeminal/tactile sensations) to describe both fresh-squeezed and thermally processed (commercial) orange juices. Two independent panels located in different countries (Spain and USA) selected a common lexicon using multivariate analysis. Two sets of samples were selected and evaluated independently: the American sensory panel analyzed 40 orange juices varied in processing technology (pasteurized, refrigerated from concentrated, frozen concentrated and canned juices) and cultivars (Valencia, Temple, Navel, Hamlin, and Amber Sweet). The Spanish panel analyzed 26 samples that included thermally processed juices (pasteurized and refrigerated from concentrated) and unheated, hand squeezed juices (Valencia and Navel). A total of 34 common attributes were selected (15 for odor, 12 for aroma, 3 for basic tastes and 4 for trigeminal/tactile sensations). Data obtained were analyzed by geometric means, principal components analysis (PCA) and by Kruskal-Wallis test. Significant differences between the major categories of commercial juices were observed for all attributes in both countries and were also observed between fresh-squeezed and processed orange juices.

Planktonic Biomass Trajectories In Lake Ecosystems

1983 ◽  
Vol 40 (10) ◽  
pp. 1752-1760 ◽  
Author(s):  
Michael A. Gates ◽  
Ann P. Zimmerman ◽  
W. Gary Sprules ◽  
Roy Knoechel
Keyword(s):  
Hypothesis Testing ◽  
Principal Components Analysis ◽  
Biomass Allocation ◽  
Principal Components ◽  
Primary Component ◽  
Size Category ◽  
Data Set ◽  
Lake Ecosystems ◽  
The Mean ◽  
Components Analysis

We introduce a method, based on principal components analysis, for studying temporal changes in biomass allocation among 16 size–category compartments of lake plankton. Applied to data from a series of 12 Ontario lakes over three sampling seasons, the technique provides a simple means of visualizing shifts in patterns of biomass allocation, and it allows comparative analyses of biomass fluctuations in different lakes. Each of the primary component axes is interpretable. Furthermore, a large proportion of the variance in both the mean position of a lake and its movement along these axes is interpreted as a function of lake physicochemistry. The analysis also provides weighted scores for use in hypothesis testing which are an improvement over mean biomass values alone, because they take into account the structure of variation in the data set.

A multivariate analysis of the niches of plant populations in raised bogs. II. Niche width and overlap

1977 ◽  
Vol 55 (9) ◽  
pp. 1211-1220 ◽  
Author(s):  
E. A. Johnson
Keyword(s):  
Multivariate Analysis ◽  
Species Richness ◽  
Principal Components ◽  
Niche Differentiation ◽  
Niche Width ◽  
Ecological Niches ◽  
Environmental Complexity ◽  
Plant Populations ◽  
Raised Bogs ◽  
Components Analysis

The results of a principal components analysis are used to define the ecological niches of the plant populations in raised bogs. This paper examines the niche parameters of width and overlap. Dominance is positively related to niche width and inversely related to the number of species (richness). Richness is shown to be positively related to increased environmental complexity and predictability. Dominants appear to decrease in environments that are more complex and predictable because as a class these organisms are less opportunistic. Niche differentiation measured as overlap is greater in the richer, more environmentally complex and predictable parts of raised bogs.

scholarly journals Multivariate analysis of 3D ToF-SIMS images: method validation and application to cultured neuronal networks

The Analyst ◽  
2016 ◽  
Vol 141 (1) ◽  
pp. 90-95 ◽  
Author(s):  
S. Van Nuffel ◽  
C. Parmenter ◽  
D. J. Scurr ◽  
N. A. Russell ◽  
M. Zelzer
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Method Validation ◽  
Cell Cultures ◽  
Principal Components ◽  
Neuronal Cell ◽  
Training Set ◽  
Neuronal Cell Cultures ◽  
Tof Sims ◽  
Components Analysis

Here, we demonstrate that by using a training set approach principal components analysis (PCA) can be performed on large 3D ToF-SIMS images of neuronal cell cultures.

scholarly journals Haplotype Classification Using Copy Number Variation and Principal Components Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 19-24
Author(s):  
Kevin Blighe
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Large Scale ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Reduction Techniques ◽  
Number Variation ◽  
Components Analysis

Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.

Robust Statistics: A Survey and Some Prescriptions

1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer
Keyword(s):  
Factor Analysis ◽  
Multivariate Analysis ◽  
Standard Deviation ◽  
Principal Components Analysis ◽  
Analysis Of Variance ◽  
Principal Components ◽  
Robust Statistics ◽  
Robust Methods ◽  
The Mean ◽  
Components Analysis

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.

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