Abstract

Previous research has demonstrated the potential use of near infrared (NIR) hyperspectral imaging for non-destructive monitoring of mushroom quality. The mushroom industry demands economical and high-throughput imaging systems that can reliably classify groups of mushrooms according to quality parameters. Multispectral imaging systems based on the acquisition of just a few (2–10) wavelengths fulfil these criteria. This research concerns the development of a low-cost robust multispectral system for mushroom quality control which can identify slightly damaged mushroom tissue using NIR spectral images. A three step approach was employed: HI the most suitable pre-treatment was selected; (2) wavelengths with the most stable normalised regression coefficients were identified using ensemble Monte Carlo variable selection (EMCVS); and (3) partial least square discriminant analysis (PLS-DA) models were built using the selected regions (49 nm bandwidth) to simulate a multispectral system. Minimum scaled reflectance spectra produced better results than maximum scaled, mean scaled, median scaled or raw spectra. Five key spectral regions were identified, centred around 971 nm, 1090 nm, 1188 nm, 1384 nm and 1454 nm. A PLS-DA model built using three spectral regions (1090 nm, 1188 nm, 1384 nm) and scaled by the 1454 nm band (minimum reflectance) correctly classified 100% of the physically damaged mushrooms.

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