Classification of different types of beer according to their colour characteristics

Abstract

Abstract. Twenty-two samples from different beers have been investigated in two colour systems -XYZ and SIELab -and have been characterised according to their colour parameters. The goals of the current study were to conduct correlation and discriminant analysis and to find the inner relation between the studied indices. K-means cluster has been used to compare and group the tested types of beer based on their similarity. To apply the K-Cluster analysis it is required that the number of clusters be determined in advance. The variant K = 4 was worked out. The first cluster unified all bright beers, the second one contained samples with fruits, the third one contained samples with addition of lemon, the fourth unified the samples of dark beers. By applying the discriminant analysis it is possible to help selections in the establishment of the type of beer. The proposed model correctly describes the types of beer on the Bulgarian market and it can be used for determining the affiliation of the beer which is not used in obtained model. One sample has been chosen from each cluster and the digital image has been obtained. It confirms the color parameters in the color system XYZ and SIELab. These facts can be used for elaboration for express estimation of beer by color.

The visual appearance of beer: A review concerning visually-determined expectations and their consequences for perception

“…In what follows, the authors outline the research findings exploring those visual appearance cues that influence people's expectations associated with, and the multisensory flavour perception of, beer. Although there is research exploring, for example, how (a) participants use beer colour to classify different types of beer (e.g., Nikolova, Gabrovam, Boyadzhiev, Pisanova, Ruseva, & Yanakiev, 2017), (b) training influences people's ability to sort beers (e.g., Honoré-Chedozeau, Desmas, Ballester, Parr, & Chollet, 2019;Lelièvre, Chollet, Abdi, & Valentin, 2009 1 ;Spence, 2019a), and (c) colour influences the perception of non-alcoholic birch beer (i.e., root beer) (Hyman, 1983), it is surprising how few published studies have actually assessed the expectations associated with beer, and the influence of visual appearance cues including colour on the perception of beer.…”

Brewing and Craft Beer

“…Rather than focus on such hypothetical scenarios, our measures detail the contributions of Michigan's beer production value chains to existing economic activities. Our findings suggest that Michigan-produced beers generate a sizeable contribution to the state economy, contributing just under $500 million to the annual Gross State Product (a measure of total income generated in the state), or alternatively, 8.4 percent of the value of Michigan's food and beverage and tobacco products manufacturing sector's annual Gross State Product [45].…”

“…Before the end of fermentation and maturation, a large amount of the polyphenolic material and protein had been removed by adhesion and coagulation, and it had then sunk to the bottom of the fermenter, hence the drop in total polyphenols of beer. However, they were still present in the final beer where they contributed in determining its quality since polyphenols contribute to flavor, astringency, the perception of bitterness, haze, oxidative effects, and antioxidative effects [43][44][45].…”

Computer Vision Method in Beer Quality Evaluation—A Review

“…Along with conventional analytical methods of color determination, digital image analysis has been demonstrated to be a suitable method for classifying beer of the same type or category. Nikolova et al [45] reported the use of digital image analysis and spectrophotometer for beer type classification according to their color. For this purpose, several beer samples were chosen to obtain digital images: light beer, dark beer, a beer with lemon and beer with fruits.…”