Wine varietal identification: solution of an uneasy task

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22330%2F21%3A43922877" target="_blank" >RIV/60461373:22330/21:43922877 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Wine varietal identification: solution of an uneasy task

Popis výsledku v původním jazyce

One of the major challenges in wine analysis [1] is authentication of grape variety from which the wine is produced. Here, we have developed and validated an authentication method based on metabolomic fingerprinting, using ultra-high-performance liquid chromatography coupled with quadrupole orbitrap high resolution mass spectrometry (U-HPLC-HRMS/MS). In total, 101 red and 97 white wines, five varieties in each group (all authentic), were analysed within our study. Filtered samples, without any other processing, were directly injected into system. After the data mining and data pre-treatment steps, principal component analysis (PCA) was used to explore the data structure. The obtained unsupervised PCA models revealed a notable clustering according to the grape varieties. Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to create supervised binary models. To obtain the best models possible, an in-house R script for optimization of data filtration parameters was developed and implemented. From over 85 000 models calculated, those with the highest prediction abilities were chosen, validated, and arranged into a Rooted Binary Decision Directed Acyclic Graph (RBDDAG), which was used for wine variety authentication. In case of white wines, 96 % of samples in positive (ESI+) and 94 % of samples in negative ionization mode (ESI-) were correctly classified. Regarding red wines, 95 % of samples in ESI+ and 94 % of samples in ESI- were correctly classified. Worth mentioning that the aforementioned accuracies are calculated for the whole RBDDAG structure. Multiple individual binary models achieved prediction ability as high as 100%, however, for some of the binary models present in the RBDDAG structure, correct classification of the grape variety appeared to be more problematic due to a higher genetic (and therefore metabolomic) similarity between varieties. The results obtained within our study indicate the potential suitability of the developed method for official control purposes. [1] https://ec.europa.eu/jrc/en/research-topic/food-authenticity-and-quality

Název v anglickém jazyce

Wine varietal identification: solution of an uneasy task

Popis výsledku anglicky

One of the major challenges in wine analysis [1] is authentication of grape variety from which the wine is produced. Here, we have developed and validated an authentication method based on metabolomic fingerprinting, using ultra-high-performance liquid chromatography coupled with quadrupole orbitrap high resolution mass spectrometry (U-HPLC-HRMS/MS). In total, 101 red and 97 white wines, five varieties in each group (all authentic), were analysed within our study. Filtered samples, without any other processing, were directly injected into system. After the data mining and data pre-treatment steps, principal component analysis (PCA) was used to explore the data structure. The obtained unsupervised PCA models revealed a notable clustering according to the grape varieties. Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to create supervised binary models. To obtain the best models possible, an in-house R script for optimization of data filtration parameters was developed and implemented. From over 85 000 models calculated, those with the highest prediction abilities were chosen, validated, and arranged into a Rooted Binary Decision Directed Acyclic Graph (RBDDAG), which was used for wine variety authentication. In case of white wines, 96 % of samples in positive (ESI+) and 94 % of samples in negative ionization mode (ESI-) were correctly classified. Regarding red wines, 95 % of samples in ESI+ and 94 % of samples in ESI- were correctly classified. Worth mentioning that the aforementioned accuracies are calculated for the whole RBDDAG structure. Multiple individual binary models achieved prediction ability as high as 100%, however, for some of the binary models present in the RBDDAG structure, correct classification of the grape variety appeared to be more problematic due to a higher genetic (and therefore metabolomic) similarity between varieties. The results obtained within our study indicate the potential suitability of the developed method for official control purposes. [1] https://ec.europa.eu/jrc/en/research-topic/food-authenticity-and-quality

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

<a href="/cs/project/LM2018100" target="_blank" >LM2018100: Infrastruktura pro propagaci metrologie v potravinářství a výživě v České republice</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů