| Sumario: | Background
Cassava utilization for food and/or industrial products depends on inherent properties of root dry matter content (DMC) and the starch fraction of amylose content (AC). Accordingly, in this study, NIRS models were developed to aid breeding and selection of DMC and AC as critical industrial traits taking care of root sample preparation and cassava germplasm diversity available in Uganda.
Results
Upon undertaking calibrations and cross-validations, best models were adopted for validation. DMC in calibration samples ranged from 20 to 45g kg^-1 while for amylose content it ranged from 14 to 33g kg^-1. In the validation set average DMC was 29.5g kg^-1 while for the amylose content it was 24.64g kg^-1. For DMC, Modified Partial least square (MPLS) regression model had regression coefficients (R2) of 0.98 and 0.96 respectively, in the calibration and validation set. These were also associated with low bias (-0.018) and ratio of performance deviation that ranged from 4.7 to 5.0. In addition, standard error of prediction values ranged from 0.9g kg^-1 to 1.06g kg^-1. For AC, the regression coefficient was 0.91 for the calibration set and 0.94 for the validation set. A bias equivalent to -0.03 and ratio of performance deviation of 4.23 were observed.
Conclusions
These findings confirm the robustness of NIRS in estimation of dry matter content and amylose content in cassava roots and thus justify its use in routine cassava breeding operations.
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