Abstract
Quality evaluation of products is a critical stage in the process of production. It also applies to the production of beer and its main ingredients, i.e., hops, yeast, malting barley and other components. The research described in this paper deals with the multifaceted quality evaluation of malting barley needed for the production of malt. The project aims to elaborate on the original methodology used for identifying grain varieties, grain contamination degree and other visual characteristics of malting barley employing new computer technologies, including artificial intelligence (AI) and neural image analysis. The neural modelling and digital image analysis assist in identifying the quality of barley varieties. According to the study, information concerning the colour of barley varieties presented in digital images is sufficient for this purpose. The multi-layer perceptron (MLP)-type neural network generated using a data set describing the colour of kernels presented in digital images was the best model for recognising the analysed malting barley varieties. The proposed procedure may bring specific benefits to malthouses, influencing the beer production quality in the future.
Highlights
New information technologies are entering different sectors of the food industry
The investigation was carried out following the neural image analysis methodologies concerning corn kernels and rapeseed
The neural modelling and digital image analysis techniques used in this study enable effective identification of the quality of barley varieties
Summary
Computerisation and automation of production processes replace human labour [1,2,3,4] This aims to improve production processes by introducing better efficiency and, at the same time, maintaining the good quality of generated products and reducing expenditure [5,6,7]. These modern solutions are being introduced to the food sector, and beer production is one of the rapidly developing branches of this industry [8,9,10]. The high quality of products determines the improvements in ingredient production technologies (Figure 1)
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