Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.

Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Genetic differentiation of a southern Africa tepary bean (Phaseolus acutifolius A Gray) germplasm collection using high-density DArTseq SNP markers.

Genetic resources of tepary bean (Phaseolus acutifolius A. Gray) germplasm collections are not well characterized due to a lack of dedicated genomic resources. There is a need to assemble genomic resources specific to tepary bean for germplasm characterization, heterotic grouping, and breeding. Therefore, the objectives of this study were to deduce the genetic groups in tepary bean germplasm collection using high-density Diversity Array Technology (DArT) based single nucleotide polymorphism (SNP) markers and select contrasting genotypes for breeding. Seventy-eight tepary bean accessions were genotyped using 10527 SNPs markers, and genetic parameters were estimated. Population structure was delineated using principal component and admixture analyses. A mean polymorphic information content (PIC) of 0.27 was recorded, indicating a relatively low genetic resolution of the developed SNPs markers. Low genetic variation (with a genetic distance [GD] = 0.32) existed in the assessed tepary bean germplasm collection. Population structure analysis identified five sub-populations through sparse non-negative matrix factorization (snmf) with high admixtures. Analysis of molecular variance indicated high genetic differentiation within populations (61.88%) and low between populations (38.12%), indicating high gene exchange. The five sub-populations exhibited variable fixation index (FST). The following genetically distant accessions were selected: Cluster 1:Tars-Tep 112, Tars-Tep 10, Tars-Tep 23, Tars-Tep-86, Tars-Tep-83, and Tars-Tep 85; Cluster 3: G40022, Tars-Tep-93, and Tars-Tep-100; Cluster 5: Zimbabwe landrace, G40017, G40143, and G40150. The distantly related and contrasting accessions are useful to initiate crosses to enhance genetic variation and for the selection of economic traits in tepary bean.