Hyperspectral imaging combined with principal component analysis for bruise damage detection on white mushrooms (Agaricus bisporus)

Abstract

AbstractHyperspectral imaging (HSI) combines conventional imaging and spectroscopy to simultaneously acquire both spatial and spectral information from an object. This technology has recently emerged as a powerful process analytical tool for rapid, non‐contact and non‐destructive food analysis. In this study, the potential application of HSI for damage detection on the caps of white mushrooms (Agaricus bisporus) was investigated. Mushrooms were damaged by controlled vibration to simulate damage caused by transportation. Hyperspectral images were obtained using a pushbroom line‐scanning HSI instrument, operating in the wavelength range of 400–1000 nm with spectroscopic resolution of 5 nm. The effective resolution of the CCD detector was 580 × 580 pixels by 12 bits. Two data reduction methods were investigated: in the first, principal component analysis (PCA) was applied to the hypercube of each sample, and the second PC (PC 2) scores image was used for identification of bruise‐damaged regions on the mushroom surface; in the second method PCA was applied to a dataset comprising of average spectra from regions normal and bruise‐damaged tissue. In this case it was observed that normal and bruised tissue were separable along the resultant first principal component (PC 1) axis. Multiplying the PC 1 eigenvector by the hypercube data allowed reduction of the hypercube to a 2‐D image, which showed maximal contrast between normal and bruise‐damaged tissue. The second method performed better than the first when applied to a set of independent mushroom samples. The results from this study could be used for the development of a non‐destructive monitoring system for rapid detection of damaged mushrooms on the processing line. Copyright © 2008 John Wiley & Sons, Ltd.