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 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 generate a flavor profile using artificial intelligence are disclosed. A flavor classifier classifies flavors for a given set of ingredients of a recipe from a set of possible classes of flavors. The flavor classifier may use different deep learning models to allow for different granularity levels corresponding to each flavor based on desired preciseness with classification of a particular flavor. A respective flavor predictor may or may not be used for each granularity level based on output of a certainty level classifier used for determining a preceding level of granularity.

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 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: Systems and methods to mimic a target food item using artificial intelligence are disclosed. The system can learn from open source and proprietary databases. A prediction model can be trained using features of the source ingredients to match those of the given target food item. A formula comprising a combination of most relevant source ingredients and their proportions can be determined using the trained prediction model. A set of existing recipes can be used as a dataset to train a recurrent neural network (RNN) and/or other suitable models. The RNN can be used to determine a recipe to mimic the target food item. The recipe may comprise a cooking process for the set of ingredients in the formula and can be cooked by a chef. The recipe may be further modified as necessary based on human feedback on sensorial descriptors.

Abstract: Systems and methods to mimic a target food item using artificial intelligence are disclosed. The system can learn from open source and proprietary databases. A prediction model can be trained using features of the source ingredients to match those of the given target food item. A formula comprising a combination of most relevant source ingredients and their proportions can be determined using the trained prediction model. A set of existing recipes can be used as a dataset to train a recurrent neural network (RNN) and/or other suitable models. The RNN can be used to determine a recipe to mimic the target food item. The recipe may comprise a cooking process for the set of ingredients in the formula and can be cooked by a chef. The recipe may be further modified as necessary based on human feedback on sensorial descriptors.

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 generate a flavor profile using artificial intelligence are disclosed. A flavor classifier classifies flavors for a given set of ingredients of a recipe from a set of possible classes of flavors. The flavor classifier may use different deep learning models to allow for different granularity levels corresponding to each flavor based on desired preciseness with classification of a particular flavor. A respective flavor predictor may or may not be used for each granularity level based on output of a certainty level classifier used for determining a preceding level of granularity.

Abstract: A color predictor is provided to predict the color of a food item given its formula comprising the ingredients and its quantities. The color predictor may utilize machine learning algorithms and a set of recipe data to train the color predictor. The color predictor can also be used by a color recommender to recommend changes in the given formula to achieve a target color.

Abstract: Techniques to generate a flavor profile using artificial intelligence are disclosed. A flavor classifier classifies flavors for a given set of ingredients of a recipe from a set of possible classes of flavors. The flavor classifier may use different deep learning models to allow for different granularity levels corresponding to each flavor based on desired preciseness with classification of a particular flavor. A respective flavor predictor may or may not be used for each granularity level based on output of a certainty level classifier used for determining a preceding level of granularity.

Abstract: A color predictor is provided to predict the color of a food item given its formula comprising the ingredients and its quantities. The color predictor may utilize machine learning algorithms and a set of recipe data to train the color predictor. The color predictor can also be used by a color recommender to recommend changes in the given formula to achieve a target color.