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Development and Performance Analysis of an Automatic Core Cutter for Elephant Apple ( Dillenia indica L.) Processing.

Deepanka SaikiaRadhakrishnan KesavanMinaxi SharmaBaskaran Stephen InbarajPrakash Kumar NayakKandi Sridhar
Published in: Foods (Basel, Switzerland) (2024)
Elephant apple, a fruit with numerous bioactive compounds, is rich in therapeutic qualities. However, its use in processed products is limited due to insufficient postharvest processing methods. To address this issue, an automatic core cutter (ACC) was developed to handle the hard nature of the fruit while cutting. The physical characteristics of the elephant apple were considered for designing and development of the cutter. The cutter is divided into four main sections, including a frame, collecting tray, movable coring unit, and cutting base with five fruit holders. The parts that directly contact the fruit are made of food-grade stainless steel. The efficiency of the cutter was analyzed based on cutting/coring capacity, machine efficiency, loss percentage, and other factors, and was compared to traditional cutting methods (TCM) and a foot-operated core cutter (FOCC). The ACC had an average cutting/coring capacity of 270-300 kg/h, which was significantly higher than TCM's capacity of 12-15 kg/h and comparable to FOCC's capacity of 115-130 kg/h. The ACC offered a higher sepal yield of 85.68 ± 1.80% compared to TCM's yield of 65.76 ± 1.35%, which was equivalent to the yield obtained by FOCC. Therefore, the ACC outperforms TCM in terms of quality, quantity, and stress associated and is superior to FOCC in terms of higher efficiency of machine and labor.
Keyphrases
  • deep learning
  • machine learning
  • mental health
  • physical activity
  • risk assessment
  • human health
  • stress induced