Proximate Composition, Cyanide Content, and Carotenoid Retention after Boiling of Provitamin A-Rich Cassava Grown in Ghana

Assessment of heavy metals and human health risk associated with the consumption of crops cultivated in industrial areas of Maputo, Mozambique

This study aimed to evaluate heavy metals concentrations in soils and vegetables (cabbage, lettuce, and cassava) cultivated at Matola and Beluluane Industrial Parks, and to assess health risks linked to their consumption through estimated daily intake, hazard index (HI), and incremental lifetime cancer risk. Concentrations of Al, As, Co, Cd, Cr, Ni, Pb, and Zn were determined in the two sites. Soil concentrations of As at Beluluane site and As, Cd, and Cr at Matola site exceeded reference limits of the Food and Agriculture Organization/World Health Organization, showing heavy metal contamination. At Beluluane site, all studied vegetables presented As and Pb levels higher than reference limits, Cd concentrations were higher than the reference limit in cabbage, lettuce, and cassava leaves. At Matola site crops concentrations of As, Cd, Cr, and Pb exceeded the reference limits. Zinc exceeded the reference limit in all crops except in cabbage. HIs for vegetables from Beluluane exceeded 1.0 in cabbage (2.66), lettuce (2.27), and cassava leaves (2.37). Likewise, at Matola, HIs exceeded 1.0 in lettuce (1.67), cassava leaves (1.65), and root tubers (13). We found that vegetables cultivated in industrial parks present high carcinogenic risk due to heavy metal contamination, rendering them unsuitable for human consumption.

Solid-state fermentation of cassava (Manihot esculenta Crantz): a review

Solid-state fermentation (SSF) is a promising technology for producing value-added products from cassava (Manihot esculenta Crantz). In this process, microorganisms are grown on cassava biomass without the presence of free-flowing liquid. Compared to other processing methods, SSF has several advantages, such as lower costs, reduced water usage, and higher product yields. By enhancing the content of bioactive compounds like antioxidants and phenolic compounds, SSF can also improve the nutritional value of cassava-based products. Various products, including enzymes, organic acids, and biofuels, have been produced using SSF of cassava. Additionally, SSF can help minimize waste generated during cassava processing by utilizing cassava waste as a substrate, which can reduce environmental pollution. The process has also been explored for the production of feed and food products such as tempeh and cassava flour. However, optimizing the process conditions, selecting suitable microbial strains, and developing cost-effective production processes are essential for the successful commercialization of SSF of cassava.

CRISPR-Cas9-mediated knockout of CYP79D1 and CYP79D2 in cassava attenuates toxic cyanogen production

Cassava (Manihot esculenta) is a starchy root crop that supports over a billion people in tropical and subtropical regions of the world. This staple, however, produces the neurotoxin cyanide and requires processing for safe consumption. Excessive consumption of insufficiently processed cassava, in combination with protein-poor diets, can have neurodegenerative impacts. This problem is further exacerbated by drought conditions which increase this toxin in the plant. To reduce cyanide levels in cassava, we used CRISPR-mediated mutagenesis to disrupt the cytochrome P450 genes CYP79D1 and CYP79D2 whose protein products catalyze the first step in cyanogenic glucoside biosynthesis. Knockout of both genes eliminated cyanide in leaves and storage roots of cassava accession 60444; the West African, farmer-preferred cultivar TME 419; and the improved variety TMS 91/02324. Although knockout of CYP79D2 alone resulted in significant reduction of cyanide, mutagenesis of CYP79D1 did not, indicating these paralogs have diverged in their function. The congruence of results across accessions indicates that our approach could readily be extended to other preferred or improved cultivars. This work demonstrates cassava genome editing for enhanced food safety and reduced processing burden, against the backdrop of a changing climate.

Flour on Gluten-Free Muffins from Different Edible Cassava Varieties in Thailand

In Thailand new edible cassava varieties have been developed to be used in the food industry. The aim of this research was to analyze the difference between flour from three cassava varieties and to evaluate the suitability and quality of flour for gluten-free muffins. The physico-chemical properties of flour from three varieties were studied. The results showed the moisture content of flour was between 10.65 ± 0.01 and 10.85 ± 0.45%. Total protein content was highly significant with a difference of 1.97 ± 0.00%, 2.15 ± 0.01%, and 2.18 ± 0.01%, respectively. Moreover, ash and fat in each flour were highly significant. Amylose content was 19.93 ± 0.47%, and the viscosity was 6286.00 ± 1.52 mPa.s. The color of flour values of L* a* b* value was not statistically different in each variety of flour. Fourier transform infrared spectroscopy (FTIR) analysis was used for the biochemical change in flour. The PCA and cluster analysis results revealed that cassava flour from Pirun 6 was different from Pirun 2 and Pirun 4. After that, the test using selected cassava flour from Pirun 6 to test the physical properties and sensory attributes of gluten-free muffins compared with wheat flour found that gluten-free muffins were overall better than basic muffins.

Phenotypic diversity and selection in biofortified cassava germplasm for yield and quality root traits

Increasing carotenoid content and improving other root quality traits has been the focus of cassava biofortification. This study aimed to (i) evaluate the genetic variability for total carotenoid content (TCC), as well as for root yield and root quality attributes; (ii) estimate potentially useful correlations for selection; and (iii) select parents for breeding and estimate the genetic gain. Data from 2011 to 2020 of 265 cassava genotypes with cream and yellow roots were analyzed for dry matter content (DMC), shoot yield, fresh root yield (FRY), dry root yield (DRY), harvest index, average number of roots per plant, starch content, root pulp color, cyanogenic compounds, and TCC. The best linear unbiased predictions showed great phenotypic variation for all traits. Six distinct groups were formed for productive characteristics of root quality, mainly TCC, DMC and FRY. Only TCC showed high broad-sense heritability ( h 2 = 0.72), while the other traits had low to medium magnitude (0.21 ≤ h 2 ≤ 0.60). TCC was strongly correlated with pulp color (r = 0.70), but null significance for DMC. The network analysis identified a clear separation between the agronomic and quality attributes of cassava roots. The selection of the 30 genotypes for recombination in the breeding program has the potential to raise TCC by 27.05% and reduce the cyanogenic compounds content by 23.03%, in addition to increasing FRY and DRY by 22.72% and 22.95%, respectively. This is the first consolidated study on the potential of germplasm for the development biofortified cassava cultivars in Brazil.

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction

Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.