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Not AvailableThe correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post-harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s-curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R2 = 0.956) and ellipsoid volume (R2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracyNot Availabl

Mass modeling of kinnow

Not AvailableThe correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post-harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s-curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R2 = 0.956) and ellipsoid volume (R2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracy.Not Availabl

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Not AvailableThe correlation between the physical properties of fruits such as their dimensions, projected areas, volume, and mass may assist in predicting fruit quality along with the development of post-harvest machinery. Thus, the present study aims to predict the mass of kinnow mandarin (Citrus reticulata L.) fruit as a function of its axial dimensions, projected areas, and volume using linear and nonlinear mathematical models (quadratic, power, and s-curve). Further, the mass models were presented under three different classifications: dimension based, projected area based, and volume based. The effect of size grading was also evaluated and compared with the data of ungraded fruits. Results showed that mass modeling based on dimensions and volume of ungraded fruits was more appropriate compared to individual grades. The quadratic model based on geometric mean diameter (R2 = 0.956) and ellipsoid volume (R2 = 0.955) are recommended for predicting the mass of ungraded fruits with maximal accuracy.Not Availabl

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Not AvailablePeel is one of the prominent by-product of citrus fruit industry. Peel, in general, is discarded as waste which can act as a potential source of phenolic compounds and nutraceutical. In this study, drying characteristics of kinnow and sweet lime peels as a function of drying method and temperature were investigated. The drying experiments were performed using polyhouse drying and cabinet drying (50 and 60 °C). Different drying models such as Page, Modified Page, Henderson and Pabis, Logarithmic were fitted to the drying data. The fitting of the models was compared using various statistical parameters such as correlation coefficient (R2), chi square (χ2), mean bias error (MBE) and root mean square error (RMSE) to the suitability of the model. According to the regression analysis, Page model was found to be best fitted which satisfactorily describing the drying curves with a correlation coefficient (R2 ≥ 0.99) for all drying conditions.Not Availabl