Document Type

Conference Paper


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence



Publication Details

Oral Presentation MCF1004 at the 11th International Conference of Engineering and Food, May, 2011, Athens, Greece.


Brown blotch, caused by pathogenic Pseudomonas tolaasii, is the most problematic bacterial disease in Agaricus bisporus mushrooms; it reduces their consumer appeal in the market place, thus generating important economical losses worldwide. The mushroom industry is in need of fast and accurate evaluation methodologies to ensure that only high quality produce reaches the market. Hyperspectral imaging (HSI) is a non-destructive technique that combines imaging and spectroscopy to obtain spatial and spectral information from an object. The aim of this study was to investigate the potential of Vis-NIR HSI to identify microbiological damage in mushrooms and to discriminate it from mechanical damage. Hyperspectral images of mushrooms subjected to i) no treatment, ii) microbiological spoilage and iii) mechanical damage were taken during storage and spectra representing each of the classes were selected. Partial least squares- discriminant analysis (PLS-DA) was carried out in two steps: i) discrimination between undamaged and damaged mushrooms and ii) discrimination between damage sources (i.e. microbiological or mechanical). The models were applied at a pixel level and a decision tree was used to classify mushrooms into one of the aforementioned classes. A correct classification of >95% was achieved. This was the first reported study to employ HSI for the detection of damage of bacterial origin in horticultural products. The industry could incorporate the knowledge gained in this study towards the development of a HSI sensor to detect and classify mushroom damage of microbial and mechanical origin, enabling the rapid and automated identification of mushrooms of reduced marketability.



This research was funded by the Irish Government Department of Agriculture, Fisheries and Food under the Food Institutional Research Measure (FIRM).