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Flavonoid and Antioxidant Capacity of Propolis Prediction Using Near Infrared Spectroscopy.

ABSTRACT: The use of propolis as a dietary supplement or as an ingredient in different food products is increasing, due to its antioxidant and bactericidal properties. These nutritional properties directly depend on its phenolic composition. For this reason, this study analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols, and the antioxidant capacity by using the methods of ABTS and linoleic acid/β-carotene in 99 samples of propolis from Spain and Chile. A rapid method was developed for quantifying these parameters in raw propolis using near infrared (NIR) spectroscopy with a remote reflectance fibre-optic probe applied directly to the ground-up sample. The models developed allow for the determination of the total flavones and flavonols (0-183 mg quercetin/g propolis and 0-72 mg rutin/g propolis), of the total flavanones and dihydroflavonols (9-109 mg pinocembrin/g propolis extract), and of its antioxidant capacity by the ABTS method based on the reduction of the 2.2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation(0-3212.6 nmol Trolox/mg of propolis) and of linoleic acid/β-carotene (22-86% inhibition). The NIR spectroscopy models were applied in external validation to different samples of the calibration group, which led to the conclusion that the methods developed provide significantly identical data to the initial chemical data of reference.

SUBMITTER: Betances-Salcedo E

PROVIDER: S-EPMC5539627 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

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Flavonoid and Antioxidant Capacity of Propolis Prediction Using Near Infrared Spectroscopy.

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