Statistical applications in food science

In our first post we mentioned the importance of statistics in food science and technology. Here, we present the main statistical applications in food science and technology. The table shown bellow (taken from: Bower (2009)) summarizes the main statistical applications found in food science:

Perhaps one of the most used applications of statistics is to summarize the enormous amount of data that is collected in any field. Food science is no exception. As methods of analysis of food characteristics (physical, chemical, sensorial, etc) improve more data is available. To make sense this data is summarized in easy graphs, tables and figures that can translate data into a more comprehensible information.

The use of graphs can be applied to food processes to visualize if they are under control. These are known as control charts. Control charts graph in real time a parameter that should be watched (for example, temperature). Limits are also placed on the graph so food plant personnel can easily see if the process is under "statistical control" and make the necessary adjustments if the process is "out of control".

Food science also devise research application in which the analysis of differences and relationships (cause-effect) are studied. Hypotheses are proposed on the basis of previous work or new ideas of the effects in sample statistics can be assessed for significance, for instance, examination of the change in viscosity when a starchy suspension is heated with many different additives.

Progress in food science and technology is the result of cerefully planned research activity. New information is obtained experiments designed to asses cause-effects relationship or  effect of multiple factors. Here statistics can be very helpful (and necessary) to avoid costly or non-meaningul experiments that get reseaarch to nowhere.

Why write about food science and statistics?

Why this site?? Why writing about food science and statistics?

As in many areas of science (and in many areas of life) it is necessary to analyze data to draw appropriate conclusions and recommendations. Statistics can aid in this regard, especially in food science and nutrition where there is an overabundance of raw and often misinterpreted data.

The area of food science and nutrition is not all about cultural practice and tradition, cooking, preservation methods, food analysis, food microbiology, stability of foods, etc,  but an area where analyzing data from existing databases, interviewing consumers, or developing food prototypes in controlled experimental conditions. 

Quantitative research involves the collection of relevant data and the analysis of the data. Conclusions drawn from this data will depend largely on the methods applied for their analysis and of the interpretation of the analytical output.  Food science and nutrition are interdisciplinary areas of knowledge with data collected from physical, microbial, chemical, sensory, and commercial analysis. Statistics is at the center of such data collection, data processing, data analysis, and data interpretation.

That is why a site like this is needed. A basic knowledge of statistical analysis and experimental design is more important than ever, especially when researching the relationship between food, food quality, food habits, health, nutrition, and lifestyle