Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Food Science (PhD)

Degree Level



Food Science


Steven C. Ricke

Committee Member

Philip G. Crandall

Second Committee Member

Ruben Morawicki

Third Committee Member

Michael F. Slavik


Biological sciences, Applied sciences, Antimicrobials, Biofuels, Biotechnology, Lignocellulosic feedstocks, Risk assessment, Yeast fermentation


During the most recent decades increased interest in fuel from biomass in the United States and worldwide has emerged each time petroleum derived-gasoline registered well publicized spikes in price. The willingness of the U.S. government to face the issues of more heavily high-priced foreign oil and climate change has led to more investment on plant-derived sustainable biofuel sources. Biomass derived from corn has become one of the primary feedstocks for bioethanol production for the past several years in the U.S. However, the argument of whether to use food as biofuel has led to a search for alternative non-food sources. Consequently, industrial research efforts have become more focused on low-cost large-scale processes for lignocellulosic feedstocks originating mainly from agricultural and forest residues along with herbaceous materials and municipal wastes. Although cellulosic-derived biofuel is a promising technology, there are some obstacles that interfere with bioconversion processes reaching optimal performance associated with minimal capital investment. This review summarizes current approaches on lignocellulosic-derived biofuel bioconversion and provides an overview on the major steps involved in cellulosic-based bioethanol processes and potential issues challenging these operations. Possible solutions and recoveries that could improve bioprocessing are also addressed. This includes the development of genetically engineered strains and emerging pretreatment technologies that might be more efficient and economically feasible. Future prospects towards achieving better biofuel operational performance via systems approaches such as risk and life cycle assessment modeling are also discussed.