A new technique for grape inspection and sorting classification (Record no. 42937)

MARC details
000 -LEADER
fixed length control field 02384cab a2200301Ia 45 0
001 - CONTROL NUMBER
control field u188020
003 - CONTROL NUMBER IDENTIFIER
control field SIRSI
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 101219s2006 ua ss b eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EAL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
Language code of summary or abstract ara
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) ART AUJAS V14 No2 2
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Elbatawi, I. E.
240 10 - UNIFORM TITLE
Uniform title Arab universities journal of agricultural sciences, 2006 v. 14 (2)
Medium [electronic resource].
245 12 - TITLE STATEMENT
Title A new technique for grape inspection and sorting classification
Medium [electronic resource].
246 15 - VARYING FORM OF TITLE
Title proper/short title أستخدام التقنية الحديثة في فحص وفرز العنب.
300 ## - PHYSICAL DESCRIPTION
Extent p.555-573
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes references.
520 ## - SUMMARY, ETC.
Summary, etc. Sorting and classification of fruits are the main problem specially for Superior and King Ruby varieties which represent more than 50% of grape production in Egypt. A usual procedure to carry out this task is based on human visual inspection considering general fruit attributes like color, size, shape, fmnness and sugar content of grape cluster. Color contains important information about fruit status and in some cases it is decisive for fruit quality differences. This paper provides a new technique to investigate the applicability of color classification, sugar content and fll11lness of grape. Standard RGB color chart, artificial neural network and a potential of nearinfrared (NIR) reflectance as a means for nondestructive measurements of grape firmness and sugar content were used. NIR spectral data were collected from the two varieties of grape in the spectral region between 800 nm and 1700 nm. Statistical models were developed using the partial least square method to predict the finnness and sugar content ofgrape. The models gave relatively good predictions ofthe firmness of both Superior and King Ruby, with corresponding r values of 0.80 and 0.65. The NIR models gave excellent prediction for grape sugar content with values of 0.71 % and 0.65 % Brix for Superior and King Ruby, respectively.
546 ## - LANGUAGE NOTE
Language note Summary in Arabic.
596 ## -
-- 1
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Grapes
General subdivision Breeding.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Grapes
General subdivision Varieties.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Grapes
General subdivision Postharvest technology.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Grapes
General subdivision Grading.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ebaid, M. T.
773 0# - HOST ITEM ENTRY
Title Arab universities journal of agricultural sciences.
Related parts 2006.v.14(2)
International Standard Serial Number 1110-2675
Control subfield nnas
Record control number u118374
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://nile.enal.sci.eg/EALE/2006/AUJAS/1406/2/555.pdf">http://nile.enal.sci.eg/EALE/2006/AUJAS/1406/2/555.pdf</a>
Public note Full Text Article.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Articles
Source of classification or shelving scheme Library of Congress Classification

No items available.


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