Identification of mushrooms subjected to freeze damage using hyperspectral imaging.

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Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, methodology): “Mushroom slice quality was measured in terms of moisture content, colour and texture [31,83,84] using MLR and principal components regression (PCR).”
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, methodology): “Gowen et al. (2009) used the red band for calculating the mean spectral reflectance from 3 × 3 region of interest (ROI) marked on the centre of the mushroom surface to identify the defects caused by thawing and freezing.”
Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves (2017, methodology): “Many studies have been carried out on the potential of using hyperspectral 87 techniques in evaluating the quality and safety attributes of food products (e.g. meat and 88 edible fungi) subjected to freeze damage (Gowen et al., 2009; Thyholt & Isaksson, 1997).”
Hyperspectral Imaging-Based Classification and Wavebands Selection for Internal Defect Detection of Pickling Cucumbers (2013, background): “2009), microbiological and freeze damages of mushroom (Gowen et al. 2009), firmness and soluble solids content of apples and blueberries (Leiva-Valenzuela et al.”