Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples’ classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

Barley has a wide range of end uses. However, the technological characteristics expected from barley present different standards according to the destination of the cereal. Grain β-glucan content is the most important attribute for varieties destined for the food market due to blood glucose and cholesterol-reducing properties. High protein content, test weight, and huller rate may also add value to different end uses. In Brazil, the main destination for barley is malt production; however, not every lot achieves malting standards. To determine the quality of Brazilian barley for food industries, 9 covered barley cultivars and 8 hull-less barley breeding lines were studied. Thousand kernel weight (TKW), hectoliter weight (HW), huller rate (HR), protein, and β-glucan contents were analyzed. The hull-less breeding lines presented higher averages when compared to the covered group, except in protein content. Correlations between "β-glucan and HW", "β-glucan and TKW", and "TKW and HW" were positive. On the other hand, "HW and protein content" and "β-glucan and protein content" presented a negative correlation. There are bromatological quality differences between Brazilian hull-less breeding lines and covered varieties. Brazilian barley germplasm presents great industrial potential, not only for malt production and animal feed but also for human food applications.