A chemical sensing system, comprising: a plurality of sensors arranged on at least one substrate, wherein a first sensor and a second sensor of the plurality of sensors have different sensitivities to sense at least one analyte in a sample, each of the plurality of sensors being configured to output a signal in response to sensing the at least one analyte; and a computer processor programmed to: receive the signals output from the plurality of sensors; and determine a concentration of the at least one analyte in the sample based, at least in part, on the received signals and a model trained to relate the signals or information derived from the signals to an output representation having bases corresponding to analytes, the analytes including the at least one analyte, wherein the concentration is a scalar value in conjunction with a particular unit of measurement, wherein: the model comprises: a first model relating the signals output from the plurality of sensors to a feature representation; and a second model relating the feature representation to the output representation; and wherein: the second model relating the feature representation to the output representation comprises: a first sub-model relating the feature representation to a latent representation characterized in that the second model further comprises a second sub-model relating said latent representation to a straightened orthogonal representation, wherein: one-dimensional manifolds in the straightened orthogonal representation corresponding to varying concentrations of a same analyte have zero angle between them; and one-dimensional manifolds in the straightened orthogonal representation corresponding to different analytes are orthogonal. a plurality of sensors arranged on at least one substrate, wherein a first sensor and a second sensor of the plurality of sensors have different sensitivities to sense at least one analyte in a sample, each of the plurality of sensors being configured to output a signal in response to sensing the at least one analyte; and a computer processor programmed to: receive the signals output from the plurality of sensors; and determine a concentration of the at least one analyte in the sample based, at least in part, on the received signals and a model trained to relate the signals or information derived from the signals to an output representation having bases corresponding to analytes, the analytes including the at least one analyte, wherein the concentration is a scalar value in conjunction with a particular unit of measurement, receive the signals output from the plurality of sensors; and determine a concentration of the at least one analyte in the sample based, at least in part, on the received signals and a model trained to relate the signals or information derived from the signals to an output representation having bases corresponding to analytes, the analytes including the at least one analyte, wherein the concentration is a scalar value in conjunction with a particular unit of measurement, wherein: the model comprises: a first model relating the signals output from the plurality of sensors to a feature representation; and a second model relating the feature representation to the output representation; and wherein: the second model relating the feature representation to the output representation comprises: a first sub-model relating the feature representation to a latent representation characterized in that the second model further comprises a second sub-model relating said latent representation to a straightened orthogonal representation, wherein: one-dimensional manifolds in the straightened orthogonal representation corresponding to varying concentrations of a same analyte have zero angle between them; and one-dimensional manifolds in the straightened orthogonal representation corresponding to different analytes are orthogonal. the model comprises: a first model relating the signals output from the plurality of sensors to a feature representation; and a second model relating the feature representation to the output representation; and a first model relating the signals output from the plurality of sensors to a feature representation; and a second model relating the feature representation to the output representation; and wherein: the second model relating the feature representation to the output representation comprises: a first sub-model relating the feature representation to a latent representation characterized in that the second model further comprises a second sub-model relating said latent representation to a straightened orthogonal representation, wherein: one-dimensional manifolds in the straightened orthogonal representation corresponding to varying concentrations of a same analyte have zero angle between them; and one-dimensional manifolds in the straightened orthogonal representation corresponding to different analytes are orthogonal. one-dimensional manifolds in the straightened orthogonal representation corresponding to varying concentrations of a same analyte have zero angle between them; and one-dimensional manifolds in the straightened orthogonal representation corresponding to different analytes are orthogonal.