Faba beans (Vicia faba L.) are an excellent protein source and should be more present in human diet, but due to their long cooking time, they are rarely used. So, in order to increase the accessibility of faba beans (Vicia faba L.), experiments were carried out to obtain new food products. Extrusion-cooking was chosen as the potential thermal cooking process and two different products were obtained. One was made exclusively from faba bean (Vicia faba L.) flour, while the second one was produced with a 50% addition of wheat flour to bean flour. Protein content, size, volume mass, pH and starch content for obtained products were analysed in order to characterise the products, as well as to see the differences from non-extruded faba beans (Vicia faba L.) and obtained samples. The experiments were carried out using faba beans (Vicia faba L.) obtained at Ltd. “Pure Horticultural Research Centre”. They were milled at Ltd. “Grauda spēks” and extruded with a twin screw extruder at Ltd. “Milzu”. The experiments showed that the extrusion process had decreased the protein content by 9%, but the starch content had risen by 13% with the addition of wheat flour. But, for sample, without added wheat flour differences were not significant. No significant size differences were observed in the obtained samples and pH values had no significant differences between extruded samples.

Faba beans (Vicia faba L.) are an excellent protein source and should be more present in human diet, but due to their long cooking time, they are rarely used. So, in order to increase the accessibility of faba beans (Vicia faba L.), experiments were carried out to obtain new food products. Extrusion-cooking was chosen as the potential thermal cooking process and two different products were obtained. One was made exclusively from faba bean (Vicia faba L.) flour, while the second one was produced with a 50% addition of wheat flour to bean flour. Protein content, size, volume mass, pH and starch content for obtained products were analysed in order to characterise the products, as well as to see the differences from non-extruded faba beans (Vicia faba L.) and obtained samples. The experiments were carried out using faba beans (Vicia faba L.) obtained at Ltd. “Pure Horticultural Research Centre”. They were milled at Ltd. “Grauda spēks” and extruded with a twin screw extruder at Ltd. “Milzu”. The experiments showed that the extrusion process had decreased the protein content by 9%, but the starch content had risen by 13% with the addition of wheat flour. But, for sample, without added wheat flour differences were not significant. No significant size differences were observed in the obtained samples and pH values had no significant differences between extruded samples.

The goal of this research is to explore sentence structures expressed by parts of speech. Due to a small amount of data, a problem of sparse data has arisen, which was solved by recording the annotated sentences and considering a “framework” of a sentence made up from a verb and a noun, which was conditionally called a code. The code of a sentence is created by changing each word of a sentence by a symbol (letter or number) that encodes one or other property of that word as a constituent of the sentence. Zipf’s law describes sentences, encoded like that, rather well. If we ‘learn’ well to identify and analyze (annotate, translate, etc.) sentences of the simplest structure, we can automatically process quite a large part of text sentences. It is possible to identify at least 17% of sentences consisting of the simplest structure.

The goal of this research is to explore sentence structures expressed by parts of speech. Due to a small amount of data, a problem of sparse data has arisen, which was solved by recording the annotated sentences and considering a “framework” of a sentence made up from a verb and a noun, which was conditionally called a code. The code of a sentence is created by changing each word of a sentence by a symbol (letter or number) that encodes one or other property of that word as a constituent of the sentence. Zipf’s law describes sentences, encoded like that, rather well. If we ‘learn’ well to identify and analyze (annotate, translate, etc.) sentences of the simplest structure, we can automatically process quite a large part of text sentences. It is possible to identify at least 17% of sentences consisting of the simplest structure.