Application of the Surface Regression Technique for Enhancing the Input Factors and Responses for Processing Coconut Oil under Vertical Compression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41310%2F24%3A97979" target="_blank" >RIV/60460709:41310/24:97979 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001220056700001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001220056700001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/foods13091384" target="_blank" >10.3390/foods13091384</a>
Alternative languages
Result language
angličtina
Original language name
Application of the Surface Regression Technique for Enhancing the Input Factors and Responses for Processing Coconut Oil under Vertical Compression
Original language description
This study optimized the input processing factors, namely compression force, pressing speed, heating temperature, and heating time, for extracting oil from desiccated coconut medium using a vertical compression process by applying a maximum load of 100 kN. The samples' pressing height of 100 mm was measured using a vessel chamber of diameter 60 mm with a plunger. The Box-Behnken design was used to generate the factors' combinations of 27 experimental runs with each input factor set at three levels. The response surface regression technique was used to determine the optimum input factors of the calculated responses: oil yield (%), oil expression efficiency (%), and energy (J). The optimum factors' levels were the compression force 65 kN, pressing speed 5 mm min-1, heating temperature 80 degrees C, and heating time 52.5 min. The predicted values of the responses were 48.48%, 78.35%, and 749.58 J. These values were validated based on additional experiments producing 48.18 +/- 0.45%, 77.86 +/- 0.72%, and 731.36 +/- 8.04 J. The percentage error values between the experimental and the predicted values ranged from 0.82 +/- 0.65 to 2.43 +/- 1.07%, confirming the suitability of the established regression models for estimating the responses.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
21101 - Food and beverages
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Foods
ISSN
2304-8158
e-ISSN
2304-8158
Volume of the periodical
13
Issue of the periodical within the volume
9
Country of publishing house
CH - SWITZERLAND
Number of pages
15
Pages from-to
1-15
UT code for WoS article
001220056700001
EID of the result in the Scopus database
2-s2.0-85192760983