Preliminary Study of Australian Pinot Noir Wines by Colour and Volatile Analyses, and the Pivot© Profile Method Using Wine Professionals

Study on the differentiation of sensory quality of mainstream Jiang-flavor baijiu in the Chinese market based on Pivot Profile

Jiang-flavor baijiu (JFB) is a prominent type of Chinese baijiu, known for its unique flavor attributes, sensory experience, and high tasting value. Previous research has mainly focused on the detection and identification of its flavor substances, but in-depth studies on the precise sensory description and differentiation of its flavor qualities are still lacking. In this study, a rapid sensory analysis method, Pivot Profile (PP), was applied to 30 mainstream JFBs in the Chinese market, generating 91 sensory attributes with independent definitions, from which 29 main sensory attributes were established that were easy to perceive and descriptive, as well as convenient for transmitting their sensory qualities and distinguishing differences in price and production region, including color (one descriptor), aroma (21 descriptors), taste, and mouthfeel (seven descriptors). The nine key sensory attributes that distinguish JFB quality are as follows: Jiang, Grain, Chen, Qu, Rancid, Acid, Sweet, Fullness, and Harmony. It was found that price was positively correlated with sensory quality, with greater variation in the quality of samples within the medium price range (RMB 500-1000). All samples from MTCQ1 (the core production area of Maotai Town) performed better in sensory quality. In addition, salted vegetable showed a high degree of regional characteristics, concentrated in most of the production regions of Guizhou Province. Aroma attributes were more suitable than taste and mouthfeel as sensory indicators for distinguishing production regions. This study has opened the direction of systematic construction of sensory description of JFB and provided a successful case for the evaluation of Chinese baijiu using novel sensory analysis techniques.

Chemical Profiling of Wines Produced in Bulgaria and Distinction from International Grape Varieties

Distinguishing the botanical and geographical origin of wine is important to prevent wine adulteration and to determine its quality. The combined use of ¹H NMR profiling and chemometrics allows the quantification of 31 common organic components in the NMR spectra of 70 wines from different sources. Using the NMR metabolomics approach, a successful differentiation of wines produced from Bulgarian and international grape varieties is achieved using linear discriminant analysis. Wines produced from typical local grape varieties contain higher average amounts of galacturonic, malic, tartaric, and succinic acid, alanine, choline, several alcohols, and saccharides arabinose, galactose, and sucrose than imported wine assortments. A practical decision tree is proposed for distinguishing 15 different grape varieties based on the amounts of the common wine components. An example of distinction of real from diluted wine via creation of a PLS-DA model is presented. Wines from the two subregions officially recognized by the EU at the Protected Geographical Indication (PGI) level are unequivocally recognized.

A Preliminary Study of Yeast Strain Influence on Chemical and Sensory Characteristics of Apple Cider

During the fermentation of apple juice, yeast metabolism creates complex biosynthetic pathways which produce a range of compounds responsible for the organoleptic qualities of cider. In this study, basic cider quality parameters were measured to investigate the influence of six yeast strains on cider made from three apple varieties (‘Pink Lady’, ‘Sturmer’, and ‘Bulmer’s Norman’). Measurement of pH, titratable acidity, and total phenolic content revealed that yeast can influence cider attributes, albeit variety and season dependent. Descriptive sensory analysis using a trained sensory panel was conducted on cider made from ‘Pink Lady’ apples and the same six yeast strains. The sensory panel significantly differentiated the yeast strains on the attributes of ‘fresh apple’, ‘earthy’ and ‘pear’. Identifying the variety specific influence of individual yeast strains on chemical and sensory characteristics of apple cider will provide cider makers with an enhanced understanding when choosing yeast strains.

Flavor Chemical Profiles of Cabernet Sauvignon Wines: Six Vintages from 2013 to 2018 from the Eastern Foothills of the Ningxia Helan Mountains in China

The eastern foothills of the Helan Mountains in the Ningxia region (Ningxia), is a Chinese wine-producing region, where Cabernet Sauvignon is the main grape cultivar; however, little compositional or flavor information has been reported on Ningxia wines. Oenological parameters, volatile profiles, and phenolic profiles were determined for 98 Ningxia Cabernet Sauvignon wines from the 2013–2018 vintages, as well as 16 from Bordeaux and California, for comparison. Ningxia wines were characterized by high ethanol, low acidity, and high anthocyanin contents. Multivariate analysis revealed that citronellol and 12 characteristic phenolic compounds distinguish Ningxia wines from Bordeaux and California wines. The concentrations of most phenolic compounds were highest in the 2018 Ningxia vintage and decreased with the age of the vintage. To our knowledge, this is the first extensive regionality study on red wines from the Ningxia region.

Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine

Fluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.

Sensory and Volatile Flavor Analysis of Beverages

Humans have used their senses to evaluate food for several thousands of years [...].