Multicriteria decision making for evergreen problems in food science by sum of ranking differences

Finding optimal solutions usually requires multicriteria optimization. The sum of ranking differences (SRD) algorithm can efficiently solve such problems. Its principles and earlier applications will be discussed here, along with meta-analyses of papers published in various subfields of food science, such as analytics in food chemistry, food engineering, food technology, food microbiology, quality control, and sensory analysis. Carefully selected real case studies give an overview of the wide range of applications for multicriteria optimizations, using a free, easy-to-use and validated method. Results are presented and discussed in a way that helps scientists and practitioners, who are less familiar with multicriteria optimization, to integrate the method into their research projects. The utility of SRD, optionally coupled with other statistical methods such as ANOVA, is demonstrated on altogether twelve case studies, covering diverse method comparison and data evaluation scenarios from various subfields of food science.

Automated summary

This paper discusses the application and advantages of the sum of ranking differences (SRD) algorithm in multicriteria optimization, showcasing its wide range of applications through carefully selected case studies and providing guidance for scientists and practitioners unfamiliar with multicriteria optimization on how to integrate the method into their research projects.