Cassava leaves are a good source of protein. However, their use is limited because of the presence of cyanogenic glucosides. These require a further detoxification process in order to reduce the cyanide to a safe level prior to human consumption. The main objectives of this work are: (i) to demonstrate the effectiveness of solid-state fermentation using Saccharomyces cerevisiae on the cyanide content degradation of cassava leaves; and (ii) to optimize the independent variables for the minimum cyanide content level of cassava leaves by the application of response surface methodology (RSM). The various process parameters investigated for these purposes were sucrose concentration, urea concentration, moisture content, and fermentation time. The degradation of cyanide content was described by the quadratic model, which resulted in an excellent fit of the experimental data (p < 0.01). The statistical tests show that linear terms for sucrose concentration, urea concentration, moisture content and fermentation time had a significant effect on cyanide content (p < 0.01). Moreover, the interaction coefficients between sucrose concentration and fermentation time; urea concentration and moisture content; and nitrogen concentration and fermentation time were significant model terms (p < 0.05). A minimum cyanide content of 0.81 ppm was obtained at 1% (w/w) sucrose concentration, 0.5% (w/w) urea concentration, 60% (v/w) moisture content and with a fermentation time of 78 hours. The optimal level made a significant reduction in cyanide content of 97.96%, which is lower than the toxicity level suggested by the World Health Organization of 10 ppm.

Original languageEnglish
Pages (from-to)624-633
Number of pages10
JournalInternational Journal of Technology
Issue number3
Publication statusPublished - May 2019


  • Cassava leaves
  • Cyanide content
  • Response surface methodology
  • Solid state fermentation


Dive into the research topics of 'Optimization of solid state fermentation conditions for cyanide content reduction in cassava leaves using response surface methodology'. Together they form a unique fingerprint.

Cite this