OENO IVAS 2019 banner
IVES 9 IVES Conference Series 9 OENO IVAS 9 OENO IVAS 2019 9 Analysis and composition of grapes, wines, wine spirits 9 Comparison of tannin analysis by protein precipitation and normal-phase HPLC

Comparison of tannin analysis by protein precipitation and normal-phase HPLC


Tannins are a heterogenous class of polymeric phenolics found in grapes, oak barrels and wine. In red wine tannins are primarily responsible for astringency, though they also have an important role in reacting with and stabilizing pigments. There are numerous sub-classes of tannins found in wine but they all share structural heterogeneity within each sub-class, with varied polymer composition, configuration and length. 

Numerous methodologies exist for the quantification of tannins, however, protein precipitation using bovine serum albumin has proved itself useful due to its strong correlation to the sensory perception of astringency and the basic instruments required for the method. Though the method can yield valuable insights into tannin composition, it cannot be automated easily and necessitates well-trained personnel. 

RP-HPLC analysis has been used for the quantification of low molecular phenolic compounds for a long time, but it is not suitable for the quantification of tannins. A normal-phase (NP)-HPLC method using a ternary solvent system is suggested, which is able to separate the phenolic compounds from red wine into three major fractions. Comparison with standard phenolic compounds allowed the characterization and quantification of these fractions and the results were compared to those obtained by protein precipitation.


Publication date: June 23, 2020

Issue: OENO IVAS 2019

Type: Article


Jan-Peter Hensen, Ingrid Weilack, Fabian Weber, Andreas Schieber, James Harbertson

University of Bonn Institute of Nutritional and Food Sciences, Molecular Food Technology Endenicher Allee 19b D-53115 Bonn Germany 

Contact the author


Tannin analysis, Protein Precipitation Assay, NP-HPLC


IVES Conference Series | OENO IVAS 2019


Related articles…

Characterization and application of silicon carbide (SiC) membranes to oenology

After fermentations, the crude wine is a turbid medium not accepted by the consumer therefore, it needs to be filtered

Temperature variations in the Walla Walla valley American Viticultural Area

Variations in average growing season and ripening season temperatures within the Walla Walla Valley American Viticultural Area are related to elevation and regional and local topography.

Evaluation of intra-vineyard spatial and temporal variability of leaf area index using multispectral images obtained by satellite (Landsat 8, Sentinel-2) and unmanned aerial vehicle platforms

Estimation of vineyard leaf area index (LAI) is an important aspect for the winegrowers. However, tracking and monitoring are difficult tasks due to time constraints. Satellite and unmanned aerial vehicle (UAV) imaging have become a practical monitoring method for LAI. Nevertheless, for a proper LAI determination, the image’s spatial resolution is a key factor, since low-resolution images are incapable of distinguishing between adjacent vines due to the large area covered in each pixel, this leads to misinterpretation or generalisation of vineyard information.

The kinetics of grape aromatic precursors hydrolysis at three different temperatures

In neutral grapes, it is known that most aroma compounds are present as non-volatile

Multivariate characterization of Italian monovarietal red wines using FTIR spectroscopy

The assessment of wine authenticity is of great importance for consumers, producers and regulatory agencies to guarantee the geographical origin of wines and grape variety as well. Since mid-infrared (MIR) spectroscopy with chemometrics represent a suitable tool to ascertain the wine composition, including features associated with the polyphenolic compounds, the aim of this study was to generate MIR spectra of red wines to be exploited for classification of red wines based on the relationship between grape variety and wine composition. Several multivariate data analyses were used, including Principal Component Analysis (PCA), Discriminant Analysis (DA), Support Vector Machine (SVM), and Soft Intelligent Modelling of Class Analogy (SIMCA).