Abstract: Techniques to suggest chemical compounds with a desired flavor profile or that can be used to recreate functional properties of a target chemical compound, using artificial intelligence, are disclosed. An artificial intelligence model is trained on source chemical compounds with known flavors. The artificial intelligence model learns relationships between the source chemical compounds and their known flavors and generates source chemical compound projected embeddings and true flavor projected embeddings. From either the source chemical compound projected embeddings or the true flavor projected embeddings, one or more chemical compounds for the identified target chemical compound or the identified desired flavor profile may be determined based on a similarity search.

Abstract: Techniques to suggest one or more sets of ingredients that can be used to recreate or mimic a target sensory description using artificial intelligence are disclosed. An artificial intelligence model includes a transformer inspired neural network architecture that learns from ingredients, recipes, and sensory profiles. The artificial intelligence model includes a sensory transformer model that generates a probability distribution of source ingredients based on an embedding associated with first digital data representing ingredients and the second digital data representing sensory description, and a selector that selects at least one candidate ingredient from the probability distribution of source ingredients for the embedding. A complete set of ingredients generated based on the at least one candidate ingredient when combined become a food product that has or achieves the sensory description.

Abstract: Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Abstract: An artificial intelligence model receives a FTIR spectrum of a given ingredient to predict its protein secondary structure. The model includes three artificial modules, which generate three predicted values corresponding to structural categories (e.g., ?-helix, ?-sheet, and other) of the predicted secondary structure. Proteins may be compared for similarity based on predicted values corresponding to the structural categories of the predicted secondary structure.

Abstract: Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Abstract: Techniques to suggest chemical compounds with a desired flavor profile or that can be used to recreate functional properties of a target chemical compound, using artificial intelligence, are disclosed. An artificial intelligence model is trained on source chemical compounds with known flavors. The artificial intelligence model learns relationships between the source chemical compounds and their known flavors and generates source chemical compound projected embeddings and true flavor projected embeddings. From either the source chemical compound projected embeddings or the true flavor projected embeddings, one or more chemical compounds for the identified target chemical compound or the identified desired flavor profile may be determined based on a similarity search.

Abstract: An artificial intelligence model receives a FTIR spectrum of a given ingredient to predict its protein secondary structure. The model includes three artificial modules, which generate three predicted values corresponding to structural categories (e.g., ?-helix, ?-sheet, and other) of the predicted secondary structure. Proteins may be compared for similarity based on predicted values corresponding to the structural categories of the predicted secondary structure.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to generate experiment trials using artificial intelligence are disclosed. A training set for an experiment generator is continuously built up by using assessed experiment trials. The experiment generator is optimized using one of a plurality of optimization algorithms, depending on which mode experiment generator is to run in for an experiment. The mode is dependent on the experiment mode of the experiment. The experiment generator generates a batch of one or more experiment trials for the experiment. Any of the generated experiment trials may be tried or experimented by a user and may be updated with assessment data as an assessed experiment trial.

Abstract: Techniques to suggest chemical compounds with a desired flavor profile or that can be used to recreate functional properties of a target chemical compound, using artificial intelligence, are disclosed. An artificial intelligence model is trained on source chemical compounds with known flavors. The artificial intelligence model learns relationships between the source chemical compounds and their known flavors and generates source chemical compound projected embeddings and true flavor projected embeddings. From either the source chemical compound projected embeddings or the true flavor projected embeddings, one or more chemical compounds for the identified target chemical compound or the identified desired flavor profile may be determined based on a similarity search.

Abstract: Techniques to suggest a set of source ingredients that can be used to recreate functional properties of a target food item, using artificial intelligence, are disclosed. A computer model determines, for the target food item, an ingredient quantities vector and an ingredient inclusion vector based on a matrix of chemical compound source ingredient vectors of all source ingredients. The ingredient inclusion vector indicates which source ingredients from a plurality of source ingredients to include in the ingredient set, and the ingredient quantities vector indicates the quantity or amount of each source ingredient such that a corresponding volatile profile of the ingredient set is similar to that of the target food item. A volatile profile for the ingredient set, which is determined from the matrix of chemical compound source ingredient vectors, the ingredient inclusion vector, and the ingredient quantities vector, mimics the target food item's volatile profile.

Abstract: Techniques to suggest alternative chemical compounds that can be used to recreate or mimic a target flavor using artificial intelligence are disclosed. A neural network based model is trained on source chemical compounds and their corresponding flavors and odors. The neural network-based model learns compound embeddings of the source chemical compounds and a target chemical compound of a food item. From the compound embeddings, one or more chemical compounds that are closest to the target chemical compound may be determined by a distance metric. Each suggested chemical compound is an alternative that can be used to recreate functional features of the target chemical compound.

Abstract: An artificial intelligence model receives a FTIR spectrum of a given ingredient to predict its protein secondary structure. The model includes three artificial modules, which generate three predicted values corresponding to structural categories (e.g., ?-helix, ?-sheet, and other) of the predicted secondary structure. Proteins may be compared for similarity based on predicted values corresponding to the structural categories of the predicted secondary structure.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using combinations of ingredients. A training set may include, for each combination of ingredients, proportions, and features of the ingredients in a respective combination of ingredients. Given a target food item, the formula generator determines a predicted formula that matches the given target food item. The predicted formula includes a set ingredients and a respective proportion of each ingredient in the set of ingredient.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator learns from open source and proprietary databases of ingredients and recipes. The formula generator is trained using features of the ingredients and using recipes. Given a target food item, the formula generator determines a formula that matches the given target food item and a score for the formula. The formula generator may generate, based on user-provided control definitions, numerous formulas that match the given target food item and may select an optimal formula from the generated formulas based on score.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator learns from open source and proprietary databases of ingredients and recipes. The formula generator is trained using features of the ingredients and using recipes. Given a target food item, the formula generator determines a formula that matches the given target food item and a score for the formula. The formula generator may generate, based on user-provided control definitions, numerous formulas that match the given target food item and may select an optimal formula from the generated formulas based on score.

Abstract: An artificial intelligence model receives a FTIR spectrum of a given ingredient to predict its protein secondary structure. The model includes three artificial modules, which generate three predicted values corresponding to structural categories (e.g., ?-helix, ?-sheet, and other) of the predicted secondary structure. Proteins may be compared for similarity based on predicted values corresponding to the structural categories of the predicted secondary structure.

Abstract: Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using combinations of ingredients. A training set may include, for each combination of ingredients, proportions, and features of the ingredients in a respective combination of ingredients. Given a target food item, the formula generator determines a predicted formula that matches the given target food item. The predicted formula includes a set ingredients and a respective proportion of each ingredient in the set of ingredient.