An apparatus, comprising: a spectroscopy arrangement to obtain a plurality of spectral data sets for a plurality of respective regions of a 3D object, the spectroscopy arrangement including a source of electromagnetic radiation configured for illuminating at least part of the 3D object; one or more processors; and one or more computer-readable media storing: a trained machine learning model configured to process the obtained spectral data sets to generate a plurality of respective model outputs from which an identification result is derived, each model output comprising a latent space position for a respective spectral data set, the trained machine learning model comprising a neural network model which has been trained to minimise a latent space distance between measurements for the same object and to maximise a latent space distance between measurements for different objects, and computer readable instructions, which when executed by the one or more processors, generate the identification result using the trained machine learning model, wherein generating the identification result comprises, for each of the plurality of obtained spectral data sets, comparing the latent space position for the obtained spectral data set with latent space positions for a plurality of stored spectral data sets for a plurality of regions of a reference 3D object, wherein the identification result thereby quantifies an extent to which the 3D object is related to the reference 3D object. a spectroscopy arrangement to obtain a plurality of spectral data sets for a plurality of respective regions of a 3D object, the spectroscopy arrangement including a source of electromagnetic radiation configured for illuminating at least part of the 3D object; one or more processors; and one or more computer-readable media storing: a trained machine learning model configured to process the obtained spectral data sets to generate a plurality of respective model outputs from which an identification result is derived, each model output comprising a latent space position for a respective spectral data set, the trained machine learning model comprising a neural network model which has been trained to minimise a latent space distance between measurements for the same object and to maximise a latent space distance between measurements for different objects, and computer readable instructions, which when executed by the one or more processors, generate the identification result using the trained machine learning model, wherein generating the identification result comprises, for each of the plurality of obtained spectral data sets, comparing the latent space position for the obtained spectral data set with latent space positions for a plurality of stored spectral data sets for a plurality of regions of a reference 3D object, wherein the identification result thereby quantifies an extent to which the 3D object is related to the reference 3D object. a trained machine learning model configured to process the obtained spectral data sets to generate a plurality of respective model outputs from which an identification result is derived, each model output comprising a latent space position for a respective spectral data set, the trained machine learning model comprising a neural network model which has been trained to minimise a latent space distance between measurements for the same object and to maximise a latent space distance between measurements for different objects, and computer readable instructions, which when executed by the one or more processors, generate the identification result using the trained machine learning model, wherein generating the identification result comprises, for each of the plurality of obtained spectral data sets, comparing the latent space position for the obtained spectral data set with latent space positions for a plurality of stored spectral data sets for a plurality of regions of a reference 3D object, wherein the identification result thereby quantifies an extent to which the 3D object is related to the reference 3D object.