Fabrication of a Pineapple Peeling Machine and Performance Evaluation as Influenced by Pineapple Dimensions

A pineapple peeling machine was completed, and the functionality was. The Central composite rotate strategy CCRD involving three variables at five (5) stages was employed for the investigations plus the investigational variables been weight, diameter and height of pineapple, while weights of peeled pineapple and peel, machine peeling time and efficiency were the dependent variables. Data was analysed using quadratic interaction regressions model at P ≤ 0.05 levels of significance, then the working variables were correlated to the pineapple machine peeling efficiency. The investigational outcomes were applied to confirm the predicted optimal settings. Maximal pineapple peeling efficacy of 91.89 % was found at unpeeled weights 3.34 kg, diameter 70 mm and height 220 mm of pineapple. Peeling time recorded was between 9 and 10 seconds, while the predicted best values were: peeling efficiency 90.586 %, weights of peeled (2.495 kg) and peel of pineapples 1.153 kg having desirability 1. The determination coefficient (R2) for machine peeling efficiency was 0.5554; weight of pineapples ranged between 1.66 - 3.34 kg, diameter 53.18 - 86.82 mm and height 203.218 - 236.81 mm, respectively. The differences that occurred between the predicted and investigational rates were minimal and statistically non-significant, which indicated that the designated model can be ably used to calculate the machines’ peeling efficiency.

Publication Details

Journal
Journal of Bioresource Management
Published
2025-06-30
Primary Topic
Teacher Education and Leadership Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fabrication of a Pineapple Peeling Machine and Performance Evaluation as Influenced by Pineapple Dimensions

Journal of Bioresource Management
Teacher Education and Leadership Studies
article

Fabrication of a Pineapple Peeling Machine and Performance Evaluation as Influenced by Pineapple Dimensions

article en

Abstract

A pineapple peeling machine was completed, and the functionality was. The Central composite rotate strategy CCRD involving three variables at five (5) stages was employed for the investigations plus the investigational variables been weight, diameter and height of pineapple, while weights of peeled pineapple and peel, machine peeling time and efficiency were the dependent variables. Data was analysed using quadratic interaction regressions model at P ≤ 0.05 levels of significance, then the working variables were correlated to the pineapple machine peeling efficiency. The investigational outcomes were applied to confirm the predicted optimal settings. Maximal pineapple peeling efficacy of 91.89 % was found at unpeeled weights 3.34 kg, diameter 70 mm and height 220 mm of pineapple. Peeling time recorded was between 9 and 10 seconds, while the predicted best values were: peeling efficiency 90.586 %, weights of peeled (2.495 kg) and peel of pineapples 1.153 kg having desirability 1. The determination coefficient (R2) for machine peeling efficiency was 0.5554; weight of pineapples ranged between 1.66 - 3.34 kg, diameter 53.18 - 86.82 mm and height 203.218 - 236.81 mm, respectively. The differences that occurred between the predicted and investigational rates were minimal and statistically non-significant, which indicated that the designated model can be ably used to calculate the machines’ peeling efficiency.

Journal of Bioresource Management
Openalex Percentile: Top 37%
Teacher Education and Leadership Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.