The Taguchi methodology as a statistical tool for biotechnological applications: A critical appraisal

Abstract

Success in experiments and/or technology mainly depends on a properly designed process or product. The traditional method of process optimization involves the study of one variable at a time, which requires a number of combinations of experiments that are time, cost and labor intensive. The Taguchi method of design of experiments is a simple statistical tool involving a system of tabulated designs (arrays) that allows a maximum number of main effects to be estimated in an unbiased (orthogonal) fashion with a minimum number of experimental runs. It has been applied to predict the significant contribution of the design variable(s) and the optimum combination of each variable by conducting experiments on a real‐time basis. The modeling that is performed essentially relates signal‐to‐noise ratio to the control variables in a 'main effect only' approach. This approach enables both multiple response and dynamic problems to be studied by handling noise factors. Taguchi principles and concepts have made extensive contributions to industry by bringing focused awareness to robustness, noise and quality. This methodology has been widely applied in many industrial sectors; however, its application in biological sciences has been limited. In the present review, the application and comparison of the Taguchi methodology has been emphasized with specific case studies in the field of biotechnology, particularly in diverse areas like fermentation, food processing, molecular biology, wastewater treatment and bioremediation.