Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour
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Central Institute of Post-harvest Engineering and Technology, Ludhiana, India
Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore, India
Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, Sultanate of Oman
T.K.M.Institute of Technology, Kollam, India
Publish date: 2017-01-31
Int. Agrophys. 2017, 31(1): 35–44
Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.