Method for training a machine learning model configured to perform characterization of components in a heterogeneous material stream with a plurality of unknown components, the method comprising: scanning the material stream by means of a sensory system configured to perform imaging of the material stream with the plurality of unknown components; predicting one or more prediction labels and associated label prediction probabilities for each of the unknown components in the material stream by means of a machine learning model which is configured to receive as input the imaging of the material stream and/or one or more features of the unknown components extracted from the imaging of the material stream; determining a training reward associated with each unknown component within the plurality of unknown components in the material stream; selecting at least one unknown component from the plurality of unknown components in the material stream based at least partially on the training reward associated with the unknown components, analyzing the isolated at least one unknown component for determining the ground truth label thereof, wherein the determined ground truth label of the isolated at least one unknown component is added to a training database; and training an incremental version of the machine learning model using the determined ground truth label of the physically isolated at least one unknown component; characterized in that determining a ground truth for said at least one unknown component requires analysis in physical isolation; the selected at least one unknown component is physically isolated from the material stream by means of a separator unit, wherein the separator unit is configured to move the selected unknown component to a separate accessible compartment;and the at least one unknown component which is isolated from the material stream is subjected to chemical analysis for determining the ground truth label at least partially based thereon. scanning the material stream by means of a sensory system configured to perform imaging of the material stream with the plurality of unknown components; predicting one or more prediction labels and associated label prediction probabilities for each of the unknown components in the material stream by means of a machine learning model which is configured to receive as input the imaging of the material stream and/or one or more features of the unknown components extracted from the imaging of the material stream; determining a training reward associated with each unknown component within the plurality of unknown components in the material stream; selecting at least one unknown component from the plurality of unknown components in the material stream based at least partially on the training reward associated with the unknown components, analyzing the isolated at least one unknown component for determining the ground truth label thereof, wherein the determined ground truth label of the isolated at least one unknown component is added to a training database; and training an incremental version of the machine learning model using the determined ground truth label of the physically isolated at least one unknown component; characterized in that determining a ground truth for said at least one unknown component requires analysis in physical isolation; the selected at least one unknown component is physically isolated from the material stream by means of a separator unit, wherein the separator unit is configured to move the selected unknown component to a separate accessible compartment;and the at least one unknown component which is isolated from the material stream is subjected to chemical analysis for determining the ground truth label at least partially based thereon.