scholarly journals The Use of Fluorescence Spectra for the Detection of Scab and Rot in Fruit and Vegetable Crops

2021 ◽  
Vol 8 ◽  
Author(s):  
Ruslan M. Sarimov ◽  
Vasily N. Lednev ◽  
Alexey V. Sibirev ◽  
Sergey V. Gudkov
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Spectra ◽  
Fruits And Vegetables ◽  
Mechanical Damage ◽  
Emission Spectra ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Vegetable Crops ◽  
Fluorescence Spectrometer ◽  
Detection And Separation

Using Fluorescence Spectrometer Lumina, fluorescence spectra of surface slices of apples and potatoes were measured. Some of the samples were healthy, some were infected: apples had scabs, potatoes had rot and mechanical damage. For apples, two zones were found where the emission spectra of healthy and scab-affected samples differed significantly from each other. This is the region of 400–450 nm with excitation of 300–350 nm, as well as the region of 680–750 nm with excitation of 400–450 nm. For potatoes, the differences between a healthy and rot-affected sample were found only in the region of 400–450 nm with excitation at 300–350 nm. The found differences are clearly manifested in the correlation coefficients between the spectra - the minimum correlation coefficient for healthy apples and scab at 450 nm excitation r = 0.51. Also, healthy and diseased samples are well separated using principal component analysis (PCA). The revealed differences in the fluorescence spectra can be used for the detection and separation of diseased and healthy fruits and vegetables.

Correlating Variations in the Dynamic Resistance Signature to Weld Strength in Resistance Spot Welding Using Principal Component Analysis

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
David W. Adams ◽  
Cameron D. E. Summerville ◽  
Brendan M. Voss ◽  
Jack Jeswiet ◽  
Matthew C. Doolan
Keyword(s):  
Principal Component Analysis ◽  
Quality Control ◽  
Spot Welding ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Weld Strength ◽  
Dynamic Resistance ◽  
Resistance Spot ◽  
Resistance Spot Welds ◽  
Key Features

Traditional quality control of resistance spot welds by analysis of the dynamic resistance signature (DRS) relies on manual feature selection to reduce the dimensionality prior to analysis. Manually selected features of the DRS may contain information that is not directly correlated to strength, reducing the accuracy of any classification performed. In this paper, correlations between the DRS and weld strength are automatically detected by calculating correlation coefficients between weld strength and principal components of the DRS. The key features of the DRS that correlate to weld strength are identified in a systematic manner. Systematically identifying relevant features of the DRS is useful as the correlations between weld strength and DRS may vary with process parameters.

scholarly journals Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Correlation Coefficients ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Daily Weight Gain ◽  
Meat Production ◽  
Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

Emission Spectral Reconstruction Based on Principal Component Analysis Applied to Fluorescence Full-Color Prints

2019 ◽  
Vol 11 (10) ◽  
pp. 1349-1356
Author(s):  
Mi Shang ◽  
Ling Yang ◽  
Danfei Liu ◽  
Zijie Cui ◽  
Yunfei Zhong
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Emission ◽  
Emission Spectra ◽  
Principal Component ◽  
Color Difference ◽  
Component Analysis ◽  
Fluorescence Emission Spectrum ◽  
Color Science ◽  
Full Color ◽  
Spectral Reconstruction

Color reproduction of fluorescent full-color prints depends on many factors, such as preparation of luminescent inks, ratio of luminescent inks to each other, printing technology and so on. In order to make color expression more abundant on fluorescent full-color prints, reconstruction of fluorescence emission spectrum is particularly significant. As opposed to custom methods, principal component analysis has been applied to color science permanently. The method was applied to emission spectral reconstruction in this work and the up-conversion luminescent inks were selected. 336 samples were composed of single ink halftone at a quarter, half, 75%, and 100% surface coverages. The samples were firstly superimposed in one ink and two inks on the blank paper. Moreover, their emission spectral was measured and the procedure for principal component analysis was also performed. The emission spectral was reconstructed by using 1 nm interval from 351 nm to 748 nm. Ultimately, the accuracy of recovery spectral was evaluated through CIEDE2000 color difference evaluation. The obtained results indicated that principal component analysis can be used to reconstruct emission spectra. Besides, the method can also be used for color estimation between different printing materials.

GENETIC VARIATION OF FABABEAN GERMPLASM

2016 ◽  
Vol 3 (4) ◽  
Author(s):  
RASHMI YADAV ◽  
ANIL KUMAR SINGH ◽  
K. K. GANGOPADHYAYA ◽  
ASHISH KUMAR SINGH ◽  
ASHOK KUMAR ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Genetic Variation ◽  
Seed Yield ◽  
Faba Bean ◽  
Seed Weight ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Vicia Faba L ◽  
Number Of Branches

Variability in 66 accessions of faba bean (Vicia faba L.) was assessed for different agro-morphological and quality parameters. Variability parameters, correlation coefficients, clustering and PCA were performed for yield and its contributing parameters. A very good variability was found in number of branches per plant ranged from 5.4 to 14.4, number of nods per main branch from 10.22 to 26.31, no. of pods in main branch varies from 8.61 to 19.65, 1000-seed weight from 271.69 to 390.31and seed yield per plant varied from 31.32 to 100.3.The protein content (%) of the genotypes varies widely from 26.31 to 31.52.Positive and significant correlation coefficients were also obtained between grain yield and 1000-seed weight (r = 0.33**) and number of pods in main branch (r = 0.01*). Principal Component Analysis shows that PC5 explained 62.8% of the total variance and was most closely associated with number of pods per cluster.

I will do it tomorrow! Exploring the dimensionality of procrastination in Poland

2016 ◽  
Vol 28 (1) ◽  
pp. 415-437 ◽  
Author(s):  
Aneta M Przepiorka ◽  
Agata Blachnio ◽  
Juan F Díaz-Morales
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Correlation Coefficients ◽  
Principal Component ◽  
International Debate ◽  
Total Correlation ◽  
Adult Participants ◽  
Confirmatory Factor ◽  
Decisional Procrastination ◽  
Best Fit

The aim of this study was to analyze the psychometric properties of the widely used general procrastination, decisional procrastination, and adult inventory of procrastination scales in both undergraduate and adult Polish populations. Polish versions of these scales were filled out by 390 student and 513 adult participants. Principal component analysis indicated one-factor structure for each scale. The pattern of loadings was congruent between student and adult samples. The item-total correlation coefficients were adequate in each sample, with higher Cronbach's alpha values in adults compared to students, who reported higher procrastination scores in general procrastination and decisional procrastination scales. Confirmatory factor analysis showed that the unidimensional model emerged as the best fit when the three scales were considered together. The results of the study suggest that Polish versions of the three procrastination scales are effective and reliable and contribute to the international debate about the dimensionality of procrastination.

scholarly journals Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
István P. Sugár ◽  
Xiuhong Zhai ◽  
Ivan A. Boldyrev ◽  
Julian G. Molotkovsky ◽  
Howard L. Brockman ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Combination ◽  
Emission Spectra ◽  
Principal Component ◽  
Component Analysis ◽  
Lipid Monolayer ◽  
Lateral Organization ◽  
Binary Constituent ◽  
Fluorescent Lipid

Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA) each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions.

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