Differentiation of blonde beers according to chemical quality indicators by means of pattern recognition techniques

A study on the differentiation of three types of blonde beer, such as lager, pilsner and low-alcohol, has been carried out. Several chemical descriptors, such as chloride, phosphate, sulphate, total amino acids, total polyphenols, pH, dry extract and absorbance at 430 nm, have been selected according to their significance on the brewing process as well as their ability as quality indicators. Principal component analysis has been used to visualise data trends. Lager and low-alcohol beers appeared separated in the plane of the first two principal components, but pilsner are mixed with these two types. Linear discriminant analysis was applied in order to construct a suitable classification model. The model was validated by applying a stratified delete-a-group jackknife resampling procedure obtaining an overall prediction ability of 92.7% and a specificity of 96.3%. This work solves the classification problem with results that are comparable to those obtained in previous studies. The main advantage of the proposed model is that it is built with chemical descriptors that are considered as quality indicators in brewery, being easily determined in routine analyses by using inexpensive equipment and common procedures.