Primjena blisko infracrvene spektroskopije u praćenju promjena tijekom skladištenja kakao mješavina na bazi meda i zobi

master's thesis

• Title (english): Application of near infrared spectroscopy in monitoring of changes during storage of cocoa mixtures based on honey and oats
• Author: Ivana Furčić
• Mentor: Davor Valinger (mentor)
• Committee member: Ana Jurinjak Tušek (predsjednik povjerenstva)
• Committee member: Maja Benković (član povjerenstva)
• Committee member: Danijela Bursać Kovačević (član povjerenstva)
• 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: 2024-09-30, Croatia
• Scientific / art field, discipline and subdiscipline: BIOTECHNICAL SCIENCES; Food Technology

Abstract

Prahovi su materijali koji se sastoje od sitnih čestica, obično manjih od jednog milimetra. Dobivaju se drobljenjem, mljevenjem ili kemijskim procesima. Cilj ovog rada bio je istražiti može li NIR spektroskopija u kombinaciji s analizom glavnih komponenata (engl. Principal Component Analysis, PCA) te umjetnim neuronskim mrežama odrediti razlike ne samo među uzorcima različitog sastava koji su pripremljeni pri različitoj temperaturi, već i prema vremenu skladištenja. Za uzorke se koristilo 9 ekstrakata i 9 prahova kakaa s dodatkom meda i zobi u različitim omjerima koji su bili sušeni pri tri različite temperature (50, 60, 70 °C) te skladišteni kroz šest mjeseci. Iako je PCA analiza dala dobre rezultate u smislu razdvajanja uzoraka, umjetne neuronske mreže pokazale su se kao idealne za razlučivanje uzoraka ovisno o temperaturi pripreme i mjesecu skladištenja sa R2 vrijednostima većima od R² = 0,96 za praškaste uzorke te R² = 0,93 za tekuće uzorke.

Abstract (english)

Powders are materials consisting of small particles, usually smaller than one millimeter. They are obtained by crushing, grinding or chemical processes. The goal of this work was to investigate whether NIR spectroscopy, in combination with Principal Component Analysis (PCA) and artificial neural networks, can determine differences not only among samples of different composition prepared at different temperatures, but also according to storage time. Nine cocoa powders and nine extracts with the addition of honey and oats in different proportions were used as the samples, which were dried at three different temperatures (50, 60, 70 °C) and stored for six months. Although PCA analysis gave good results in terms of sample separation, artificial neural networks proved to be ideal for sample discrimination depending on preparation temperature and month of storage with R² values greater than 0.96 for powder samples and 0.93 for liquid samples.

• Keywords: kakao prah; analiza glavnih komponenata; umjetne neuronske mreže
• Keywords (english): cocoa powders; principal component analysis; artificial neural networks
• Language: croatian
• URN:NBN: urn:nbn:hr:159:246783
• Study programme: Title: Food Engineering; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka prehrambenog inženjerstva (magistar/magistra inženjer/inženjerka prehrambenog inženjerstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2024-10-16 09:21:40