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 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 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: 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.