Disclosed herein is a method for harvesting amylose host material comprising enzymatically treating starch after the starch has been chemically modified to uniformly insert a steric hindrance substituent.

Disclosed herein is a method for harvesting amylose host material comprising enzymatically treating starch after the starch has been chemically modified to uniformly insert a steric hindrance substituent.

A method for determining values of processing variables relating to processing steps in a cheese vat arrangement of a processing line for producing cheese. By a control device, obtaining a target moisture content value of the cheese, milk properties comprising pH, protein content, and fat content of milk fed into the cheese vat arrangement, obtaining, coagulant properties comprising type and amount of a coagulant fed into the cheese vat arrangement, obtaining, starter properties comprising type and amount of a starter culture fed into the cheese arrangement, then feeding the target moisture content value, the milk properties, the coagulant properties, and the starter properties into an artificial intelligence (AI) model, and in response, obtaining predictions in the form of values of the processing variables from the AI model comprising at least one number of cuts and at least one cooking time used in the cheese vat arrangement during cheese production.

A method for determining values of processing variables relating to processing steps in a cheese vat arrangement of a processing line for producing cheese. By a control device, obtaining a target moisture content value of the cheese, milk properties comprising pH, protein content, and fat content of milk fed into the cheese vat arrangement, obtaining, coagulant properties comprising type and amount of a coagulant fed into the cheese vat arrangement, obtaining, starter properties comprising type and amount of a starter culture fed into the cheese arrangement, then feeding the target moisture content value, the milk properties, the coagulant properties, and the starter properties into an artificial intelligence (AI) model, and in response, obtaining predictions in the form of values of the processing variables from the AI model comprising at least one number of cuts and at least one cooking time used in the cheese vat arrangement during cheese production.

The present invention generally relates to aspartic proteases (EC 3.4.23) for producing cheese.

The present invention generally relates to aspartic proteases (EC 3.4.23) for producing cheese.

The present invention relates to novel Lactococcus lactis bacteria showing temperature induced cell lysis in the absence of phage release. These bacteria can, for example, provide an improved flavour profile in cheese production. The present invention also relates to methods of selecting such Lactococcus lactis strains and their use for producing, ripening, eliminating the bitterness and/or increasing the amount of free amino acids in cheese. Methods for making food products and food products comprising such Lactococcus lactis strains are also described. Finally, the invention relates to methods for manufacturing the strains of the invention.

The present invention relates to novel Lactococcus lactis bacteria showing temperature induced cell lysis in the absence of phage release. These bacteria can, for example, provide an improved flavour profile in cheese production. The present invention also relates to methods of selecting such Lactococcus lactis strains and their use for producing, ripening, eliminating the bitterness and/or increasing the amount of free amino acids in cheese. Methods for making food products and food products comprising such Lactococcus lactis strains are also described. Finally, the invention relates to methods for manufacturing the strains of the invention.