Abstract
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.
Highlights
A well-formulated definition of food science tells us that “Food sci ence is the study of the biological, chemical and physical properties of foods and their effects on the culinary, nutritional, sensory, storage and safety aspects of foods and beverages.” (Marcus, 2014)
The high number of different measurements comes from several subfields of food science, such as food engineering, food microbiology, or sensory anal ysis, just to name a few
The resulting “sum of ranking differences” values measure the closeness of the individual rankings to the reference, SRD can solve method-comparison problems in a fast and easy way: the smaller the sum, the better the method
Summary
A well-formulated definition of food science tells us that “Food sci ence is the study of the biological, chemical and physical properties of foods and their effects on the culinary, nutritional, sensory, storage and safety aspects of foods and beverages.” (Marcus, 2014). It is easy to accept even from this definition, or from personal experience, that food scientists use a diverse set of instruments to measure various food properties. The high number of different measurements comes from several subfields of food science, such as food engineering, food microbiology, or sensory anal ysis, just to name a few. The measurements originate from different sources, they all need to be analyzed with some kind of a data analysis method. The specific characteristics of the datasets from the different fields can be analyzed by chemometric, sensometric, biometric approaches, etc. One specific problem that emerges in all food science subfields is optimization. Optimization is one of the most com mon processes within the sub-disciplines of food science. Food scientists regularly face the question of how to choose the best process/model/pa rameters, etc. Food scientists regularly face the question of how to choose the best process/model/pa rameters, etc. from the many available alternatives
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