title: Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples.
abstract: 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.
authors.affiliation: Unit of Biomass Technology and Chemistry, Swedish University of Agricultural Sciences, SLU Röbäcksdalen, PO Box 4097, SE 90403 Umeå, Sweden. jim.burger@btk.slu.se
authors.forename: James
authors.initials: J
authors.lastname: Burger
authors.forename: Paul
authors.initials: P
authors.lastname: Geladi
mesh_terms: D002138:Calibration; D002611:Cheese / Q000032:analysis; D004041:Dietary Fats / Q000032:analysis; D004044:Dietary Proteins / Q000032:analysis; D016018:Least-Squares Analysis; D013058:Mass Spectrometry / Q000379:methods; D008956:Models, Chemical; D015203:Reproducibility of Results; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D004041:Dietary Fats; D004044:Dietary Proteins