Jednostavni i parcijalno-linearni regresijski modeli primijenjeni na različitim svojstvima vodenih biljnih ekstrakata

master's thesis

• Title (english): Simple and partially-linear regression models applied to different properties of aqueous plant extracts
• Author: Marija Jakelić
• Mentor: Jasenka Gajdoš Kljusurić (mentor)
• Granter: University of Zagreb; Faculty of Food Technology and Biotechnology; (Department of Process Engineering); (Laboratory for Measurement, Regulation and Automatisation); Zagreb
• Defense date and country: 2016-09-29, Croatia
• Scientific / art field, discipline and subdiscipline: BIOTECHNICAL SCIENCES; Nutrition

Abstract

Iako se biljni čajevi, odnosno vodeni ekstrakti samoniklog bilja koriste u ljudskoj prehrani još od davnina, njihova svojstva još uvijek nisu dovoljno istražena. Posljednjih godina sve se više istraživanja na području nutricionizma bavi proučavanjem antioksidansa, a upravo samoniklo bilje bogato je polifenolima koji čine veliku skupinu spojeva s antioksidacijskom aktivnosti. U ovom radu ispitivani su ekstrakti 6 biljaka: Gospina trava, Kadulja, Lavanda, Majčina dušica, Melisa i Neven. Provedene su po dvije ekstrakcije za svaku biljku od kojih su uvjeti ekstrakcije provedeni kroz dva tretmana, a tretman 1 proveden je pri 40ºC, 90 min i 750 o/min te tretman 2: 80ºC, 60 min pri 250 o/min. Cilj ovog istraživanja bio je odrediti fizikalna svojstva vodenih biljnih ekstrakata, udio ukupnih polifenola (UF) i antioksidacijsku aktivnost (AOA) DPPH metodom i metodom po Briggs-Rauscheru te pomoću jednostavnih regresijskih modela vidjeti u kakvom su odnosu promatrani parametri ovisno o različitim uvjetima ekstrakcije. Rezultati regresijskih modela pokazuju različitu ovisnost AOA o koncentraciji UF, obzirom na tretman ekstrakcije i mjernu metodu. Provela se i NIR spektroskopska analiza uzoraka čiji spektri su korišteni u modelima parcijalne linearne regresije. Primjenom modela parcijalne linearne regresije (PLS) u NIR spektroskopskoj analizi dobiven je dobar model za predikciju udjela polifenola u uzorku (R²>0,89).

Abstract (english)

Although herbal teas or aqueous extracts of medical plants are used in the human diet since ancient times, their properties have not yet been sufficiently explored. In recent years the focus of nutrition studies is based on antioxidant contribution, and medical plants are rich in polyphenols that are a group of compounds with antioxidant activity. In this study the extracts of six medical plants are investigated (St. John's Wort, Sage, Lavender, Thyme, Lemon balm and Marigold). Two treatments conditions were used in preparation of aqueous extracts, treatment 1: was carried out at 40°C, 90 min & 750 r/min; while treatment 2 conditions were: 80°C, 60 minutes at 250 r/min. The aim of this study was to determine the physical properties of aqueous plant extracts, the share of total polyphenols (UF) and antioxidant activity (AOA) which was determined by use of two methods: DPPH method and the Briggs-Rauscher method. Simple regression models were used to determine the relationship of the observed parameters depending on different extraction conditions. Results of simple regression models showed different trends in the relationship of AOA vs. UF, due to the treatment of extraction and measurement method. NIR spectroscopic analysis of samples was also conducted and used in the partial linear regression modeling (PLS). Using the model of partial linear regression (PLS), the NIR spectra gave a good model for predicting the content of polyphenols in the sample (R²> 0,89).

• Keywords: samoniklo ljekovito bilje; biljni čajevi; antioksidacija; polifenoli; NIR spektroskopija
• Keywords (english): wild medical plants; herbal teas; antioxidant; polyphenols; NIR spectroscopy
• Language: croatian
• URN:NBN: urn:nbn:hr:159:177942
• Study programme: Title: Nutrition; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra nutricionizma (magistar/magistra nutricionizma)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2017-04-25 08:59:40