Consumers prefer cassava roots that cook quickly during boiling. Current methods to evaluate cooking time (CT) are slow and labour-intensive. This article describes improved protocols for assessing CT in roots. We evaluated CT in 36 genotypes monthly at 8-11 months after planting. CT showed differences for plant age at harvest and among genotypes. During boiling, roots absorbed water (WAB) and thus reduced their relative density (DEN). We classified three groups of genotypes with increasing CT (≤25 min, 25-40 min and >40 min), associated with decreasing WAB, respectively, 15.3 AE 3.1, 10.7 AE 1.7 and 4.9 AE 3.8% of initial root weight. A similar trend was observed for changes in DEN (46.3 AE 9.8, 54.5 AE 11.1 and 75.9 AE 6.9% of initial DEN, respectively). The highest correlations between WAB and DEN with CT (r 2 > 0.6) were found at 30-min boiling. These alternative protocols facilitate screening large numbers of cassava genotypes for CT.

Past research developed reliable equations to base selections for high b-carotene on nearinfrared spectroscopy (NIR) predictions (100 genotypes d −1

The CGIAR Harvest Plus Challenge Program began in the mid-2000s to support the genetic improvement of nutritional quality in various crops, including the carotenoids content of cassava roots. Successful conventional breeding requires a large number of segregating progenies. However, only a few samples can be quantified by high performance liquid chromatography each day for total carotenoids (TCC) and b-carotene (TBC) contents, limiting the gains from breeding. This study describes the usefulness of near infrared (NIR) spectroscopy and the efficiency of a large database coupled to a LOCAL regression algorithm to reach accurate TCC/TBC predictions on fresh cassava roots. The cassava database (6026 samples) was built over six years. TCC values ranged from 0.11 μg g -1 to 29.0 μg g -1 , whereas TBC ranged from negligible values up to 20.1 μg g -1 . All values were measured and expressed on a fresh weight basis. Between 2009 and 2014 increases in TCC and TBC were 86% and 122%, respectively. A comparison of calibrations using partial least squares (PLS) regression and LOCAL regression was done. The standard error of prediction were 1.82 μg g -1 for TCC and 1.28 μg g -1 for TBC using PLS model and 1.38 μg g -1 and 1.02 μg g -1 , respectively, using LOCAL regression. The specificity of the data, with increasing content of the constituent of interest year after year, clearly showed the limitation of the classical partial least squares regression approach. The LOCAL regression algorithm takes advantage of large databases; this study highlighted the efficiency of this concept. NIR spectroscopy coupled to LOCAL regression led to efficient models for breeding programmes aiming at increasing carotenoids content in fresh cassava roots. NIR spectroscopy can also be used to predict other important constituents such as dry matter content and cyanogenic glucosides.

Vitamin A deficiency (VAD) is a preventable tragedy that affects millions of people, particularly in sub-Saharan Africa. A large proportion of these people rely on diets based on cassava as a source of calories. During the last two decades, significant efforts have been made to identify sources of germplasm with high pro-vitamin A carotenoids (pVAC) and then use them to develop cultivars with a nutritional goal of 15 μg g −1 of β-carotene (fresh weight basis) and good agronomic performance. The protocols for sampling roots and quantifying carotenoids have been improved. Recently, NIR predictions began to be used. Retention of carotenoids after different root processing methods has been measured. Bioavailability studies suggest high conversion rates. Genetic modification has also been achieved with mixed results. Carotenogenesis genes have been characterized and their activity in roots measured.