Hyperspectral NIR imaging for calibration and prediction:a comparison between image and spectrometer data for studying organic and biological samples

摘要

A hyperspectral image in the near infrared contains thousands of position-referenced spectra.After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra.In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers:a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe.The raw spectra and the quality of the PLS calibration models and predictions are compared.Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses.The prediction error from the hyperspectral image spectra is between that of the two spectrometers.For a typical food sample,the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat.Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat.Some of the advantages of working with hyperspectral images are highlighted:the simultaneous exploration of representations of both spectral and spatial data,and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.