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Primerjava metod za redukcijo prostora in iskanje konsenza na podatkih senzoričnega profiliranja
ŽIGON, UROŠ (Author), Žiberna, Aleš (Mentor) More about this mentor... This link opens in a new window

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Abstract
Senzorične analize predstavljajo nepogrešljivo orodje v prehrambni industriji. Uporabijo se za iskanje razlik med izdelki kot tudi za njihov opis. V zadnjih desetletjih so dobile razmah metode hitrega profiliranja, ki omogočajo, da se izdelki opišejo brez uporabe poenotenih deskriptorjev (spremenljivk). V magistrskem delu obravnavamo metodo, imenovano senzorično profiliranje po lastni izbiri (ang. Free Choice Profiling). Metoda omogoča ocenjevalcem uporabo različnih opisnih spremenljivk tako v kvalitativnem kot v kvantitativnem smislu. Statistični metodi, ki se najpogosteje uporabljata pri analizi tako dobljenih podatkov, sta GPA (posplošena Prokrustova analiza) ter MFA (multipla faktorska analiza). Gre za metodi, ki delujeta na principu redukcije dimenzionalnosti podatkov, ki pa, za razliko od metode glavnih komponent, ohranjata informacijo posameznega ocenjevalca. Omenjeni metodi združita vhodne informacije (ocene ocenjevalcev) na osnovi konsenza, povprečne zaznave razlik med vzorci. V magistrskem delu sem preveril podobnost dobljenih rezultatov obeh metod s pomočjo dveh statistik (RV2 in ARI) ob različnih pogojih (dejavnikih); variabilnost ocen, število ocenjevalcev, število izbranih deskriptorjev, dimenzionalnost GPA/MFA-rešitve, narava ocenjevanja. Simulacije so pokazale, da je RV2 statistika nekoliko pristranska ob pogojih neodvisnosti med simuliranimi izvornimi matrikami in simuliranimi matrikami ocenjevalcev. Simulacije so tudi potrdile, da sta metodi GPA in MFA zelo primerljivi v vseh pogojih ter da sta izbrani statistiki za merjenje podobnosti relativno slabo korelirani v direktni primerjavi GPA in MFA rešitev. Dodatno sem modeliral vpliv posameznih dejavnikov na povprečne vrednosti (MFA+GPA) merjenih statistik. Pridobljene informacije so izredno koristne, ker nam podajo oceno vplivov posameznih kombinacij dejavnikov in s tem omogočajo optimalno izvedbo bodočih testov s preučevano metodo hitrega profiliranja.

Language:Slovenian
Keywords:GPA, MFA, senzorične analize, profiliranje po lastni izbiri, RV2, ARI
Work type:Master's thesis/paper (mb22)
Organization:FE - Faculty of Electrical Engineering
Year:2019
Views:463
Downloads:147
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Secondary language

Language:English
Title:Comparison of space reduction and consensus search techniques on sensory profiling data
Abstract:
Sensory analysis is a very important set of procedures in food industry. It helps us in finding differences between samples and to describe those samples (differences). In the last fifteen years or so, the quick profiling techniques gained popularity. Those methods allow a descriptive analysis without the usage of the same vocabulary between assessors. My actual work is based on the Free Choice Profiling technique, which allows assessors to freely choose the number of descriptors and their meaning to describe a product. GPA (generalized Procrustes analysis) and MFA (multiple factor analysis) are the two most popular statistical techniques used to analyze the obtained data. They are both based on dimensionality reduction of the data (matrices) and unlike the PCA (principal component analysis), they retain individual assessor information. These methods process the data by creating a consensual (average) space of products (samples), which reflects the average perception of all assessors. My work was focused in comparing the similarity of GPA and MFA outputs by measuring two statistics (RV2 coefficient and ARI-Adjusted Rand index) under different conditions; score variability, number of assessors, number of selected descriptors, GPA/MFA dimensionality output, nature (focus) of assessment. The simulations showed that RV2 is slightly biased under the assumption of independence between the simulated main data matrix and the simulated assessors' ones. The simulation confirmed that the GPA and MFA are indeed very comparable under all conditions and that the measured statistics are not highly correlated in a direct GPA / MFA comparison. Additionally, I modelled the influence of the selected factors on the averaged (MFA+GPA) measured statistics. The obtained information is very valuable because it provides an insight about the influence of the selected factors. This allows us to properly combine them to optimize the execution of future assessments with the assessed Free Choice profiling technique.

Keywords:GPA, MFA, sensory analysis, Free Choice Profiling, RV2, ARI

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