This method describes how to classify barley according to size using a laboratory sieving machine.
Barley intended for the production of malt is to be evaluated on the basis of the characteristics described below.
A barley sample is classified into fractions according to kernel size in a sieving machine containing three sieves with defined slot widths.
Prediction of the extract content and predetermination of the processability and value of a lot of barley for brewing purposes
The behavior of barley during the malting process, which is intended for large-scale malt production, must be known.
MEBAK approved and adopted a micromalting procedure on 6 April 1971 as a standard method for predicting the extract content and for determining the suitability of barley varieties for malting. In 2003, MEBAK shortened the procedure by one day for a total of six days for vegetative growth (steeping and germination), the same length of time as the EBC procedure.
This method evaluates the varietal purity of a lot of barley by means of the HCl test.
Barley intended for the production of malt is to be evaluated on the basis of the characteristics described below.
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.
This method describes how to determine not only the variety of barley but whether a lot of barley consists of a mix of varieties.
Barley intended for malt production as well as barley malt
Separation and identification of the protein (hordein) fraction of barley or barley malt by means of gel electrophoresis. The method is suitable for all types of barley, as long as reference substances are available. However, the method cannot be used to identify barley varieties used to produce malt that has been so strongly modified that the protein fraction is almost completely degraded.
This method evaluates the varietal purity of a sample of malting barley with the aid of image processing, artificial intelligence and Internet of Things (IoT) technology.
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.
Barley intended for the production of malt is to be evaluated on the basis of the characteristics described below.
visual assessment