A high throughput phenotyping technique for banana cultivar Sukali Ndizi based on internal fruit quality attributes
1 National Banana Research Program, National Agricultural Research Organization, P.O. Box 7065, Kampala, Uganda.
2 National Crops Resources Research Institute, NARO, P. O. Box 7084, Kampala, Uganda.
3 Department of Agricultural Production, College of Agricultural and Environmental Sciences, Makerere University, P. O. Box 7062, Kampala, Uganda.
4 Food Biosciences and Agribusiness Program, National Agricultural Research Organization, P. O. Box 7065, Kampala, Uganda.
Research Article
International Journal of Science and Technology Research Archive, 2022, 03(02), 258-269.
Article DOI: 10.53771/ijstra.2022.3.2.0155
Publication history:
Received on 01 November 2022; revised on 20 December 2022; accepted on 22 December 2022
Abstract:
Background: Sukali Ndizi quality traits such as Total soluble solid (TSS) content, pulp texture and sugar/acid (S/A) ratio are critical in quality assessment. Screening very large numbers of fruit genotypes has prompted the development of a high throughput method using Near Infrared spectrometry (NIRS).
Results: The calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure. Calibration equations for all analytical characteristics were computed by NIR Software ISI Present WINISI using Modified Partial Least Squares (MPLS) and Partial Least Squares. The quality of calibration models were evaluated by Standard Error of Calibration and coefficient of determination parameters between the measured and the predicted values. The results obtained with FOSS NIR systems 2500 spectrometer (model DS 2500) using the 350-2500 nm range, showed good prediction of the quality traits TSS content, pulp texture and S/A ratio. The MPLS method produced satisfactory Calibration model performance for TSS, texture and S/A ratio, with typical Rc2 of 0.73%Brix, 0.69kgf and 0.7; and root mean squared standard error of calibration of 0.73%Brix, 0.25kgf and 5.36 respectively. This is a good set of quality traits predicting Sukali Ndizi quality with NIRS with robustness, as it was obtained by using diverse Ndizi populations.
Conclusions: This can be a useful tool to phenotype large numbers of Ndizi hybrids per day, making it possible to reduce on the resources spent when utilizing organoleptic evaluation selection technique.
Keywords:
Sukali Ndizi; Phenotyping; Platform; Quality traits
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