System and method for non-destructive rapid food profiling using artificial intelligence

A system for non-destructive taste profiling of a sample of a food, the system comprising:

• a receptacle configured to move the sample in a path so as to intersect a volumetric sampling space, the sample being in a non-homogenized form;
• a sensor configured to sense reflectance from at least a part of the sample in the volumetric sampling space, the reflectance characterized by visible-to-near infra-red light, the sensor being configured to output a component of the reflectance as captured data, the captured data being characterised by an overtone spectrum of a measure of the reflectance, the overtone spectrum characterized by gradual changes in intensity over a range of wavelengths;
• and
• a computing device configured to apply at least one first machine learning model to the captured data to:
• predictively determine selected wavelengths from the range of wavelengths;
• predict at least one facet corresponding to the selected wavelengths;
• and provide a signature data using the at least one facet, wherein the signature data is characteristic of a taste of the food,
• wherein the computing device is further configured to apply at least one second machine learning model to the signature data to predict at least one descriptor;
• and provide a signature of the sample using the at least one descriptor, wherein the signature comprises a cultivar or an origin of the food.

A system, comprising:

• memory storing instructions;
• and
• a processor coupled to the memory and configured to process the stored instructions to implement:
• a module configured to perform a method of non-destructive taste profiling of a sample of a food, the method comprising:
• providing captured data to a computing device, the captured data being characterised by an overtone spectrum of a measure of a reflectance, the reflectance characterized by visible-to-near infra-red light, the overtone spectrum characterized by gradual changes in intensity over a range of wavelengths, wherein the reflectance is sensed from at least a part of the sample in a volumetric sampling space, the sample including non-homogenized food;
• predicting at least one facet by applying at least one first machine learning model to the captured data, wherein the at least one facet corresponds to at least one selected wavelength predictively determined from the range of wavelengths;
• using the at least one facet to provide a signature data characteristic of the sample, wherein the signature data is characteristic of a taste of the food;
• applying at least one second machine learning model to the signature data, wherein the at least one second machine learning model is configured to:
• predict at least one descriptor;
• and
• provide the signature of the food using the at least one descriptor, wherein the signature comprises a cultivar or an origin of the food.