ABSTRACT
The study presents an approach to the analysis and classification of peanuts performed in order to detect kernels with fungi diseases, i.e. kernels prone to contamination with mycotoxigenic Aspergillus flavus (Aspergillus parasiticus). The aim of this study was to evaluate the effectiveness of luminescent spectroscopy with a violet laser (405 nm wavelength) as the excitation source of the fluorescence when applied for real-time detection of mould in peanuts performed by means of multispectral processing based on machine learning methods. We suggest a laboratory unit used to form, register, and process the luminescence spectra of peanuts in visible and near-infrared wavelength ranges in the real-time mode. The study demonstrated that contaminated peanuts have increased luminous intensity and show a redshift in the fluorescence peaks of the contaminated samples as compared to the pure ones. The difference in the fluorescence spectra of pure and contaminated kernels is compatible with the results obtained when traditional UV-light sources are used (365 nm). To classify peanuts by their spectral characteristics, neural network algorithms were used combined with dimensionality reduction methods. The paper presents the probabilities of incorrect recognition of the peanuts’ type depending on the number of relevant secondary features determined when reducing the dimensionality of the initial data. When 10 spectral components were used, the error ratios were 0.7% or 0.3% depending on the method of reducing the dimensionality of the initial data.
Graphical Abstract
Disclosure statement
No potential conflict of interest was reported by the author(s).
Safety statement
The fungi Aspergillus flavus and Aspergillus parasiticus are capable of producing large amounts of potent carcinogens. It is necessary to follow the Directive 2004/37/EC – carcinogens or mutagens in action, as well as additional instructions for handling dangerous fungi and microorganisms. This is a warning to those who might want to repeat this work.