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19 April 2017 Development of a predictive model for ‘Lapins’ sweet cherry dry matter content using a visible/near-infrared spectrometer and its potential application to other cultivars
P.M.A. Toivonen, A. Batista, B. Lannard
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Abstract

This work was conducted to determine if a useful model could be developed for a portable visible/near-infrared spectrometer for non-destructively predicting dry matter content in ‘Lapins’ and other cultivars of sweet cherries. Absorptions at a range of 948–957 nm (default selected by the instrument model-building program) or 858–1008 nm (user selected) were used to create models associating absorption values with actual measured dry matter contents of cherries. The best model was created using the user selected waveband and this model was determined to be highly predictive of dry matter, with a resolution of 0.5% dry matter content. External validation of this model was carried out using three different sweet cherry cultivars (Staccato™, Sentennial™, and Sovereign™) and the model was found to be robust, i.e., quite accurate in predicting dry matter content in these other cultivars, with R2 values of 0.96, 0.94, and 0.99, and root mean square error of prediction values of 0.51, 0.74, and 0.56, respectively. The results indicate that dry matter in sweet cherries can be predicted accurately and non-destructively using visible/near-infrared spectroscopy. In the case of the cherry cultivars tested in this paper, a model developed using ‘Lapins’ fruit was reliable in predicting dry matter in these other cultivars.

© Her Majesty the Queen in right of Canada 2017. Permission for reuse (free in most cases) can be obtained from RightsLink.
P.M.A. Toivonen, A. Batista, and B. Lannard "Development of a predictive model for ‘Lapins’ sweet cherry dry matter content using a visible/near-infrared spectrometer and its potential application to other cultivars," Canadian Journal of Plant Science 97(6), 1030-1035, (19 April 2017). https://doi.org/10.1139/cjps-2017-0013
Received: 11 January 2017; Accepted: 1 April 2017; Published: 19 April 2017
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