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Optično zaznavanje razrasta plesni in termična analiza vzorca
ID Lisičić, Belmin (Author), ID Prislan, Iztok (Mentor) More about this mentor... This link opens in a new window, ID Milanič, Matija (Co-mentor)

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Abstract
Namen tega dela je bil preučiti uporabnost optičnih nedestruktivnih metod kot sta hiperspektralno slikanje (HSI) in optična koherenčna tomografija (OCT) za zaznavanje rasti plesni P. expansum na vzorcu jabolk. Tretirane in kontrolne vzorce jabolk sort 'Zlati delišes' in 'Evelina' smo spremljali med inkubacijo s HSI ter z OCT do pojava plesni. Z namenom spremljanja sprememb proteoma plesni smo pri izbranih časih vzorčili jabolka za analizo z diferenčno dinamično kalorimetrijo (DSC) in gelsko elektroforezo. Po osnovnem procesiranju slik smo s pomočjo algoritma metode podpornih vektorjev (SVM) in metode upragovanja po spektralnem razmešanju izvedli klasifikacijo plesni na slikah. Metoda SVM je bila pri sorti 'Zlati delišes' bolj uspešna, pri 'Evelini' pa metoda upragovanja. Kot najbolj uspešna metoda pri zgodnjem zaznavnju plesni je bila OCT, kjer smo že prvi dan inkubacije opazili rast plesni na obeh sortah. Rast plesni je bila počasnejša pri sorti 'Evelina'. Gelska elektroforeza se je izkazala kot neuspešna za spremljanje preteoma plesni. Z uporabo DSC smo lahko zaznali razlike med plesnivimi in zdravimi sadeži, še posebej na podlagi endotermnega vrha pri 66 °C.

Language:Slovenian
Keywords:jabolka, plesni, zaznavanje propada, hiperspektralno slikanje, procesiranje slik, termična analiza, optična koherenčna tomografija, strojno učenje
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:BF - Biotechnical Faculty
Publisher:[B. Lisičić]
Year:2023
PID:20.500.12556/RUL-150190 This link opens in a new window
UDC:543.4:634.11:632.25
COBISS.SI-ID:164599811 This link opens in a new window
Publication date in RUL:15.09.2023
Views:229
Downloads:91
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Secondary language

Language:English
Title:Optical detection of mould growth and thermal analysis of the sample
Abstract:
The aim of this work was to investigate the suitability of optical non-destructive methods such as hyperspectral imaging (HSI) and optical coherence tomography (OCT) for detection of P. expansum growth on apples. Treated and control apple samples of 'Golden Delicious' and 'Evelina' cultivars were monitored with HSI and OCT during incubation until the occurrence of the mould. In order to monitor changes in mould's proteome, apples were sampled at selected times for differential scanning calorimetry (DSC) and gel electrophoresis analysis. After basic image processing we performed the classification of mould in an images using support vector machine (SVM) algorithm and thresholding method after spectral unmixing. SVM method performed better for the 'Golden Delicious' cultivar and the thresholding method performed better for the 'Evelina' cultivar. The most successful method for early detection of mould was OCT, which detected mould growth on both cultivars on first day of incubation. Mould growth was slower on the 'Evelina' cultivar. Gel electrophoresis has proved unsuccessful in monitoring mould's proteome. Using DSC, we were able to detect differences between mouldy and healthy fruit, particularly on the basis of endothermic peak at 66 °C.

Keywords:apples, mould, decay detection, hyperspectral imaging, image processing, thermal analysis, optical coherence tomography, machine learning

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