Volume 20, Issue 137 (2023)                   FSCT 2023, 20(137): 74-87 | Back to browse issues page


XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Badii F, Bouzari N, Shavakhi F, Rafiee Darsangi Z. Classification of some superior local apricot genotypes of Iran based on physical and geometrical attributes. FSCT 2023; 20 (137) :74-87
URL: http://fsct.modares.ac.ir/article-7-66901-en.html
1- Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO) , f.badii@areeo.ac.ir
2- Horticultural Science Research Institute, Temperate Fruits Research Center, AgriculturalResearch, Education and Extension Organization (AREEO)
3- Agricultural Engineering Research Institute, Agricultural Research, Education and ExtensionOrganization (AREEO)
Abstract:   (879 Views)
In this study some physical and geometrical properties of 11 superior apricot genotypes were determined. These properties such as respiration rate, fruit dimensions, perimeter, surface area, volume, compact factor, geometric mean diameter, projected area, shape factor, circularity, length to width ratio, length to thickness ratio, and length to mass ratio were measured at harvesting moisture content ranging from 75.19 to 87.67 %. Then the correlation among average values of the attributes was performed and the genotypes were classified using principal component analysis (PCA). The results showed that all the genotypes had significant differences in terms of all the studied attributes (P<0.01). There was a significant and positive correlation between fruit length and weight and fruit length and moisture content. Fruit shape factor and compression factor showed a positive and significant correlation, while these attributes had negative and significant correlations with circularity. This study showed that Iranian apricot genotypes could be discriminated by differences in their geometrical characteristics using principal component analysis. Based on the PCA results, the first two components account for the most of the variation in the data (91%) and five distinct groups were observed. Overall, the results of this study can be beneficial for the design of equipment for harvesting, transportation, separating, packaging, and processing of apricot fruit.
 
Full-Text [PDF 524 kb]   (1489 Downloads)    
Article Type: Original Research | Subject: Post-harvest food technology
Received: 2023/01/18 | Accepted: 2023/05/31 | Published: 2023/07/1

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.