Abstract
The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid-liquid extraction and microwave-assisted extraction) based on combined UV-VIS-NIR spectra. Spectral data were gathered for 46 samples per extraction method. The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave-assisted extraction. Results showed that reliable ANN models (R2 >0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV-VIS-NIR spectra of industrial hemp extracts without spectra pre-processing.
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