Spatial optimisation of mango leather production and colour estimation through conventional and novel digital image analysis technique

Abstract

Being a seasonal fruit mango cannot be cherished over the year; dehydration may be a solution to preserve the deliquesce of mango as mango leather. The processing parameters like puree load (0.4–0.6 g/cm2), total soluble solid (20–30 °B), oven temperature (60–80 °C), and microwave power level (100–300 W) were optimised for a superior textural attribute (hardness) primitive drying method like sun drying, industrially practiced modern methods like hot air oven drying and microwave drying and cutting-edge drying technique like freeze-drying. Response surface methodology and artificial neural network technique were adapted to model these drying procedures by considering the central composite design. The mathematical operations guiding to describe the model were studied. Being an imperative parameter colour quantification is essential for food industries. Current research employs microwave drying to produce mango leather with colour quantification approach. The L, ‘a’ and ‘b’ values of the product have been measured by Hunter Lab colorimeter and by digital image analysis, to determine the chromatic view harmonious to human vision. The relative analysis of colour measurement through these two techniques has been studied.

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Acknowledgement

We acknowledge Dr. Subhendu Ghoshal for his support throughout the research work by providing necessary rare books and articles for a thorough literature survey. We also acknowledge the teaching and non teaching members of Malda polytechnic, Mr. Snehashis Guha (O.I.C. Malda Polytechnic) for their support throughout the work.

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Correspondence to Runu Chakraborty.

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Sarkar, T., Salauddin, M., Choudhury, T. et al. Spatial optimisation of mango leather production and colour estimation through conventional and novel digital image analysis technique. Spat. Inf. Res. (2021). https://doi.org/10.1007/s41324-020-00377-z

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Keywords

  • Response surface methodology
  • Artificial neural network
  • Drying
  • Digital image
  • Hunter colorimeter