Search: barley screenings

This test detects the presence of most varieties of two-rowed and multi-rowed winter barley possessing a green aleurone layer. This test is based upon the reaction between HCl and the green pigment, which turns red in its presence.

A sample is collected of the cereal to be tested and a hot water extraction is carried out. After cold break separation, the sample is carbonated and bottled. After shaking and then opening the bottle, the volume of liquid that fobs over and out of the bottle is measured. This value is used to determine the gushing potential for the malt or adjunct.

Glucose and fructose are phosphorylated by the enzyme hexokinase (HK) and adenosine 5'-triphosphate (ATP) to glucose 6-phosphate (G-6-P) and fructose 6-phosphate (F-6-P):

Glucose + ATP \(^{\underrightarrow{HK}}\) G-6-P + ADP

Fructose + ATP \(^{\underrightarrow{HK}}\) F-6-P + ADP

In the presence of the enzyme glucose-6-phosphate dehydrogenase (G6P-DH), G-6-P is oxidized from nicotinamide adenine dinucleotide phosphate (NADP) to gluconate-6-phosphate. Reduced nicotinamide adenine dinucleotide phosphate (NADPH) is formed:

\(\text{G-6-P}\hspace{0.2em}+\hspace{0.2em}\text{NADP}\hspace{0.8em}^{\underrightarrow{\text{G6P–DH}}}\hspace{0.8em} \text{glucanate-6-phosphate} + \text{NADP}+\text{H}^+\)

The amount of NADPH formed during the reaction is equivalent to the amount of glucose. NADPH is a measurand and is determined based on its absorbance at 334, 340 or 365 nm.

After the reaction is complete, F-6-P is converted to G-6-P by phosphoglucose isomerase (PGI):

F-6-P \(^{\underrightarrow{PGI}}\) G-6-P

G-6-P reacts in turn with NADP to form gluconate-6-phosphate and NADPH. The additional amount of NADPH formed is equivalent to the amount of fructose and is determined photometrically based on its absorption at 334, 340 or 365 nm.

A scanning device is utilized to obtain a high resolution image of a sample of barley kernels. Algorithms are then applied to detect and segment each individual kernel captured in the image. Subsequently, each individual kernel is analyzed by a Convolutional Neural Network (CNN) with a layer structure that has been specifically selected and developed for analyzing and classifying agricultural commodities. The CNN is trained with verified information (also known as "ground truth") so that it can differentiate barley varieties. The ground truth consists of pure samples of kernels from different barley varieties that were previously digitized using the device and comprises the full data set (artificial intelligence models). In order to obtain accurate artificial intelligence models, the algorithms must be trained to recognize the wide range of variability present in the pure samples, such as those collected from varieties grown in different regions and under varying conditions as well as from various crop years. The purpose of training is to teach the algorithms to understand and detect the patterns unique to each variety that can be used to distinguish it. Once trained, the algorithms are capable of accurately predicting the varietal purity of an unknown sample of barley kernels, provided that the variety has been integrated into the artificial intelligence models.

visual assessment

Visual inspection for the presence of foreign grain or contaminants. Also refer to R-110.22.011 Sortierung von Gerste.