| Title: | Volatile-Based Prediction of Sauvignon Blanc Quality Gradings with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS) and Interpretable Machine Learning Techniques |
| Author(s): | Zhu W; Benkwitz F; Kilmartin PA; |
| Address: | "Wine Science Programme, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand. Drylands Winery, Constellation Brands NZ, 237 Hammerichs Road, Blenheim 7273, New Zealand" |
| ISSN/ISBN: | 1520-5118 (Electronic) 0021-8561 (Linking) |
| Abstract: | "The analytical scope of static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) was applied to wine aroma analysis for the first time. The method parameters were first fine-tuned to achieve optimal analytical results, before the method stability was demonstrated, in terms of repeatability and reproducibility. Succinct qualitative identification of compounds was also realized, with the identification of several volatiles that have seldom been described previously in Sauvignon Blanc wine, such as methyl acetate, ethyl formate, and amyl acetate. Using the SHS-GC-IMS data in an untargeted approach, computer modeling of large datasets was applied to link aroma chemistry via prediction models to wine sensory quality gradings. Six machine learning models were compared, and artificial neural network (ANN) returned the most promising performance with a prediction accuracy of 95.4%. Despite its inherent complexity, the ANN model offered intriguing insights on the influential volatiles that correlated well with higher and lower sensory gradings. These findings could, in the future, guide winemakers in establishing wine quality, particularly during blending operations prior to bottling" |
| Keywords: | Gas Chromatography-Mass Spectrometry Ion Mobility Spectrometry Machine Learning Odorants/analysis Reproducibility of Results *Volatile Organic Compounds/analysis *Wine/analysis Sauvignon Blanc artificial neural network (ANN) model explanation quality grad; |
| Notes: | "MedlineZhu, Wenyao Benkwitz, Frank Kilmartin, Paul A eng 2021/03/05 J Agric Food Chem. 2021 Mar 17; 69(10):3255-3265. doi: 10.1021/acs.jafc.0c07899. Epub 2021 Mar 4" |
