Uporaba analiznih metod v kombinaciji z metodo glavnih osi pri določevanju lastnosti vina

Šegina, Tina (avtor), Marolt, Gregor (mentor)

Vino je tradicionalna alkoholna pijača, ki je že tisočletja globoko ukoreninjena v človeški kulturi po vsem svetu. Predstavlja zapleteno mešanico več sto spojin, kjer vsaka komponenta oz. kombinacija le-teh igra pomembno vlogo pri oblikovanju kakovosti vina. V njegovi sestavi se prepletajo elementi iz grozdja, produkti kvasnega vrenja ter spojine, ki nastanejo med dolgotrajnim zorenjem in skladiščenjem. Glavni sestavini vina sta voda in etanol, ostale prisotne komponente kot so sladkorji, kisline, drugi alkoholi in fenoli, pa imajo pomembno vlogo pri izoblikovanju okusa, sladkosti in kislosti ter barve vina, čeprav so prisotne v nižjih koncentracijah. Medtem ko so se pretekle raziskave osredotočale na določanje mejnih koncentracij posameznih komponent, ki razlikujejo kakovostno vino od nizkokakovostnega, je bil cilj mojega diplomskega dela raziskati, kako koncentracije več različnih komponent medsebojno vplivajo na končni izdelek. Za ta namen sem uporabila metodo glavnih osi (PCA), da bi raziskala povezave med devetimi izbranimi organskimi kislinami in osmimi anorganskimi anioni. V sklopu svojega dela sem razvila metodo za ločevanje izbranih analitov s pomočjo ionske kromatografije (IC) z detektorjem na električno prevodnost. Analizirala sem 105 vzorcev vin ter določila koncentracije sedemnajstih izbranih analitov. PCA analize sem izvedla glede na različne značilnosti vina, kot so sorta, pokrajina pridelave, sladkorna stopnja, vrsta in kakovost. Optimalno združevanje sem razkrila glede na sorto vina, ki je odvisno od vrste uporabljenega grozdja in porekla vina, na katerega vplivata sestava tal in lokalne vremenske razmere. Presenetljivo pa ni bilo zaznati nobenega jasnega združevanja glede na vrsto vina (rdeče/belo/rosé). Za izboljšanje prihodnjih raziskav predlagam razširitev obsega analitov. Najdene povezave med lastnostmi vina in izbranimi analiti, določenimi z ionsko kromatografijo, predstavljajo prvi korak k dolgoročnemu cilju, kar je razvoj someljeja na podlagi umetne inteligence, ki bo temeljil na izmerjenih koncentracij komponent v vinu.

• Jezik: Slovenski jezik
• Ključne besede: lastnosti vina, ionska kromatografija, metoda glavnih osi, umetna inteligenca
• Vrsta gradiva: Diplomsko delo/naloga
• Tipologija: 2.11 - Diplomsko delo
• Organizacija: FKKT - Fakulteta za kemijo in kemijsko tehnologijo
• Leto izida: 2023
• PID: 20.500.12556/RUL-149730
• COBISS.SI-ID: 171674115
• Datum objave v RUL: 08.09.2023

Sekundarni jezik

• Jezik: Angleški jezik
• Naslov: The use of analytical methods in combination with Principal Component Analysis for determination of wine characteristics

Izvleček

Wine is a traditional alcoholic beverage deeply ingrained in human culture worldwide for millennia. It is a complex mixture of several hundred compounds, each playing an important role for its final quality. It consists of elements from grapes, products of yeast fermentation, and compounds that develop during extended maturation and aging. The two main ingredients of wine are water and ethanol, while other present components, such as sugars, acids, other alcohols and phenols contribute significantly to the formation of flavour, including sweetness and acidity, and colour of wine, despite their lower concentrations. While previous studies have been focused on determining the threshold concentrations of specific components that distinguish high-quality from lower-quality wine, the aim of my thesis research work was to investigate how concentrations of various components interact with each other and influence the final product. To achieve this, I employed Principal Component Analysis (PCA) to establish correlations between nine organic acids and eight inorganic anions. As part of my study, I developed a separation program using ion chromatography (IC) with conductivity detector. I analysed 105 wine samples, meticulously determining the concentrations of seventeen selected analytes. I performed PCA analyses based on various wine characteristics, such as variety, production region, sugar level, type and wine quality. I uncovered optimal grouping based on wine variety, which depends on the type of grape used, as well as the wine origin, which is influenced by soil composition and local weather conditions. Surprisingly, no significant grouping based on the type of wine (red/white/rosé) was detected. To improve future research, I propose determination of additional analytes. The discovered correlations between wine characteristics and selected analytes determination by ion chromatography represent the first step towards the long-term goal, i.e. the development of an artificial sommelier based on measured component concentrations in wine and machine learning.

• Ključne besede: Wine Properties, Ion Chromatography, Principal Component Analysis, Artificial Intelligence