Chemical Characterization and Determination of the Anti-Oxidant Capacity of Two Brown Algae with Respect to Sampling Season and Morphological Structures Using Infrared Spectroscopy and Multivariate Analyses
| Autor: | Andres Agurto, Juanita Freer, Carlos Peña-Farfal, Rosario Castillo, Cristian Agurto, Nicolás Troncoso, Angelo Beratto |
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| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
Spectrophotometry Infrared Alginates Analytical chemistry Biomass Infrared spectroscopy Phaeophyta 01 natural sciences Antioxidants 0404 agricultural biotechnology Glucuronic Acid Algae Partial least squares regression Fourier transform infrared spectroscopy Instrumentation Spectroscopy Chromatography biology Chemistry Hexuronic Acids 010604 marine biology & hydrobiology Polyphenols 04 agricultural and veterinary sciences biology.organism_classification 040401 food science Brown algae Multivariate Analysis Principal component analysis Regression Analysis Macrocystis pyrifera |
| Zdroj: | Applied Spectroscopy. 71:2263-2277 |
| ISSN: | 1943-3530 0003-7028 |
| Popis: | Brown algae biomass has been shown to be a highly important industrial source for the production of alginates and different nutraceutical products. The characterization of this biomass is necessary in order to allocate its use to specific applications according to the chemical and biological characteristics of this highly variable resource. The methods commonly used for algae characterization require a long time for the analysis and rigorous pretreatments of samples. In this work, nondestructive and fast analyses of different morphological structures from Lessonia spicata and Macrocystis pyrifera, which were collected during different seasons, were performed using Fourier transform infrared (FT-IR) techniques in combination with chemometric methods. Mid-infrared (IR) and near-infrared (NIR) spectral ranges were tested to evaluate the spectral differences between the species, seasons, and morphological structures of algae using a principal component analysis (PCA). Quantitative analyses of the polyphenol and alginate contents and the anti-oxidant capacity of the samples were performed using partial least squares (PLS) with both spectral ranges in order to build a predictive model for the rapid quantification of these parameters with industrial purposes. The PCA mainly showed differences in the samples based on seasonal sampling, where changes were observed in the bands corresponding to polysaccharides, proteins, and lipids. The obtained PLS models had high correlation coefficients (r) for the polyphenol content and anti-oxidant capacity (r > 0.9) and lower values for the alginate determination (0.7 |
| Databáze: | OpenAIRE |
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