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Development of ANN models based on combined UV‐vis‐NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts
Author(s) -
Valinger Davor,
Jurina Tamara,
Šain Adela,
Matešić Nikolina,
Panić Manuela,
Benković Maja,
Gajdoš Kljusurić Jasenka,
Jurinjak Tušek Ana
Publication year - 2020
Publication title -
phytochemical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 72
eISSN - 1099-1565
pISSN - 0958-0344
DOI - 10.1002/pca.2979
Subject(s) - chemistry , extraction (chemistry) , yield (engineering) , chromatography , polyphenol , spectral line , solvent , analytical chemistry (journal) , organic chemistry , antioxidant , materials science , metallurgy , physics , astronomy
Objectives 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. Results 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. Conclusions Results showed that reliable ANN models ( R 2 >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.