Patent classifications
A23J1/009
METHOD FOR ENRICHING A BIOMASS WITH PROTEINS
The present invention relates to a method for protein enrichment of a biomass of red unicellular algae such as Galdieria and to the biomass thus obtained.
Protein rich food ingredient from biomass and methods of preparation
The present invention provides a protein material and food ingredient from a sustainable and stable source. The sustainable and stable source of the food or food ingredient is biomass, for example an algal or microbial biomass. The invention discloses that the biomass can be subjected to a series of steps to derive the protein material and food or food ingredient, which has high nutritional content without the unacceptable organoleptic properties that typically accompany proteins and food ingredients from these sources.
Sustainable manufacture of foods and cosmetics by computer enabled discovery and testing of individual protein ingredients
This disclosure provides a technology for developing individual proteins for use in industrial processes that include food production. The technology mines sequence data from protein databases by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression, and testing are done to yield potential ingredients suitable for use in the production of foods, cosmetics, agricultural feed, pharmaceutical excipients, and other industrial products.
NON-TOXIC PROTEINS AND OMEGA-3 FROM ALGAE AND METHOD OF MAKING SAME
Disclosed is a non-toxic omega-3 rich extract and non-toxic miscella and a method of producing same. The method may include obtaining an aqueous microalgae slurry comprising at least 65% water; mixing the aqueous microalgae slurry with ethanol for a predetermined duration; separating the aqueous microalgae slurry-ethanol mixture to liquids and miscella; and evaporating the water and the ethanol from the liquid to receive a liquid extract. In some embodiments, the miscella contains organic material and ash at an amount below 15 dry weight %.
System for identifying and developing individual naturally-occurring proteins as food ingredients by machine learning and database mining combined with empirical testing for a target food function
This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology mines natural sources by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression and testing are done to yield potential ingredients suitable for assessment as part of a commercial food product.
PROCESS FOR PURIFYING PHYCOCYANINS
The present invention relates to a novel process for purifying phycocyanins produced by fermenting microalgae, in particular produced by Galdieria sulphuraria, which comprises an enzymatic degradation of glycogen.
System for identifying and developing food ingredients from natural sources by machine learning and database mining combined with empirical testing for a target function
This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology mines natural sources by a process that is done partly in silico. Instead of sampling and testing a vast library of compounds, machine learning and implementation narrows the field of functional candidates by predictive modeling based on known protein structure. Candidate proteins that are selected by this analysis are then produced and screened in a high-throughput manner by recombinant expression and testing to determine whether they have a target function. Multiple cycles of the machine learning, database mining, expression and testing are done to yield potential ingredients suitable for assessment as part of a commercial food product.
RECOMBINANT COMPONENTS AND COMPOSITIONS FOR USE IN FOOD PRODUCTS
Provided are methods for producing food products comprising recombinant components, and compositions used in and food products produced by such methods.
COMPOSITIONS AND METHODS FOR INCORPORATING HEME FROM ALGAE IN EDIBLE PRODUCTS
Provided herein are compositions and processes for producing compositions from an algae to provide heme and a red or red-like color to edible compositions including ingredients and finished food products. Also provided are methods of growing heme-producing algae, methods of producing algae preparations therefrom and methods of making ingredients and food products with algae preparations. Also provided are compositions, including edible compositions that include heme and other nutrient components produced from algae.
Protein rich food ingredient from biomass and methods of production
The present invention provides a protein material and food ingredient from a sustainable and stable source. The sustainable and stable source of the food or food ingredient is cellular biomass, for example an algal or microbial biomass. The invention discloses that the cellular biomass can be subjected to a series of steps to derive the protein material and food or food ingredient, which has high nutritional content and has pleasing organoleptic properties.