Preference of banana flavour and aroma are not affected by time of the year or 'winter' chilling

2002 ◽  
Vol 42 (2) ◽  
pp. 201 ◽  
Author(s):  
A. Klieber ◽  
M. N. Muchui
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Musa Acuminata ◽  
Mass Spectrometric ◽  
Soluble Solids ◽  
Eating Quality ◽  
Sensory Testing ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

Banana (Musa acuminata Coll. Cavendish cv. Williams) eating quality was assessed through winter, spring and summer, and for mildly chilled bananas with symptoms mimicking that of ‘winter-chilled’ fruit using sensory testing and principal component analysis of mass-spectrometric data obtained from a ‘chemical nose’. Sensory testing of bananas harvested at different times of the year showed no differences in flavour preference, even though soluble solids were higher in spring at 23.6%, compared with winter at 22.6% and summer at 21.9%. Principal component analysis of mass-spectrometric data did not reveal aroma differences between times of the year. Results of difference tests for mildly chilled and non-chilled bananas indicated no differences in aroma intensity or sweetness. Mild chilling did not alter soluble solids and principal component analysis of mass-spectrometric data did not discriminate between chilled and non-chilled bananas.

Evaluation of mass spectrometric data using principal component analysis for determination of the effects of organic lakes on protein binder identification

2015 ◽  
Vol 50 (11) ◽  
pp. 1270-1278 ◽  
Author(s):  
Stepanka Hrdlickova Kuckova ◽  
Gabriela Rambouskova ◽  
Radovan Hynek ◽  
Pavel Cejnar ◽  
Doris Oltrogge ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Mass Spectrometric ◽  
Mass Spectrometric Data ◽  
Spectrometric Data

scholarly journals Evaluation on the phenotypic diversity of Calamansi (Citrus microcarpa) germplasm in Hainan island

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Yong-Hui Xin ◽  
Yuan-Xin Wu ◽  
Bin Qiao ◽  
Long Su ◽  
Shang-Qian Xie ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Phenotypic Diversity ◽  
Principal Component ◽  
Hainan Island ◽  
Component Analysis ◽  
Cell Number ◽  
Tree Canopy ◽  
Soluble Solids ◽  
Phenotypic Traits

AbstractCalamansi or Philippine lime (Citrofortunella macrocarpa) is an important crop for local economic in Hainan Island. There is no study about Calamansi germplasm evaluation and cultivar development. In this study, Calamansi data were collected from 151 of Calamansi seedling trees, and 37 phenotypic traits were analyzed to investigate their genetic diversities. The cluster analysis and principal component analysis were conducted aiming to provide a theoretical basis for the Calamansi genetic improvement. The results of the diversity analysis revealed: (1) the diversity indexes for qualitative traits were ranged from 0.46–1.39, and the traits with the highest genetic diversity level were fruit shaped and pulp colored (H′ > 1.20); and the diversity indexes for quantitative traits ranged from 0.67–2.10, with the exception of a lower in fruit juice rate (1.08) and lower in number of petals (0.67). (2) The clustering analysis of phenotypic traits have arranged the samples into 4 categories: the first group characterized by fewer flesh Segment number per fruit (SNF) and more Oil cell number (OCN); the second group had 7 samples, all characterized with larger Crown breadth (CB), higher Yield per tree (YPT), the lager leaf, the higher Ascorbic acid (AA), and less Seed number per fruit (SNPF); the third group had 25 samples characterized by smaller Tree foot diameter (TFD),smaller Fruit shape index (FSI) and higher Total soluble solids (TSS) contain; the fourth group had 87 samples, they were characterized by shorter Petiole length (PEL), larger fruit, higher Juice ratio (JR), multiple Stamen number (SN) and longer Pistil length (PIL). (3) The principal component analysis showed the values of the first 9 major components characteristic vectors were all greater than 3, the cumulative contribution rate reach 72.20%, including the traits of single fruit weight, fruit diameter, tree height, tree canopy width etc. Finally, based on the comprehensive main component value of all samples, the Calamansi individuals with higher testing scores were selected for further observation. This study concludes that Calamansi seedling populations in the Hainan Island holds great genetic diversity in varies traits, and can be useful for the Calamansi variety improvements.

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.

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