Rapid and cost-effective quantification of YAN concentration and composition – vintage and cultivar effect

by | May 1, 2019 | Winetech Technical, Oenology research

PHOTO: Shutterstock.

Due to the complexity of the grape juice matrix and the increasing consumer demand for quality wines, monitoring the grape juice composition becomes essential.

Yeast assimilable nitrogen (YAN), a critical component of the grape juice matrix, is, however, often neglected. This is for the most part due to the lack of rapid, cost-effective and easy-to-use methods available to accurately measure the YAN concentration and composition. Although the formol titration offers a relatively easy method to measure YAN, it is only able to measure the total YAN, and therefore, no knowledge can be gained on the composition of the nitrogen status of the grape juice matrix. As YAN provides the primary nutrients required for the proliferation of yeast during fermentation, YAN deficiencies have been identified as the main cause of stuck/sluggish fermentations. However, due to the complex metabolic activities of yeast, the types of nitrogenous compounds contributing to the total YAN becomes important.

It is known that too little YAN can result in stuck/sluggish fermentations, an increase in hydrogen sulphide which leads to the production of reductive flavours and odours, an increase in higher alcohols, which can result in the production of undesirable aromas, as well as a decrease in the production of fruity esters that can contribute to the production of quality wine. On the other hand, when YAN concentrations are too high, this can lead to microbial instability, as there are enough nutrients to support the growth of unwanted yeasts and bacteria, which can result in the production of certain allergens and carcinogens, such as biogenic amines and ethyl carbamate, as well as the production of protein haze. All of this can compromise the quality of the wine and ultimately lead to the production of a low quality wine.


How do calibrations work?

Three aspects need to be in place: a reference/standard method, an additional method (that will be calibrated), and the appropriate statistical tools to link (calibrate) the two.

  • In the case of YAN, reference methods range from titration (formol, total YAN), to NOPA (for FAN), enzymatic (for ammonia), and even HPLC (for individual amino acids). From a routine perspective, there are many downfalls when it comes to these methods. The sample preparation is often difficult and time-consuming and may require the use of highly trained personnel, the run time on the required machinery may be quite long, they may make use of expensive and hazardous reagents and they may not maintain the integrity of the sample.
  • Therefore, the new method should address these shortcomings. As such, the method to be calibrated should have no/minimal sample preparation and require less highly trained personnel, has a short run-time (high throughput), is cheap to run, makes use of no reagents, and possibly maintains the integrity of the sample. For all these reasons, the alternative method proposed for YAN entails the use of IR spectroscopy.
  • The link between the two methods is done with the help of chemometrics (aka chemical statistics). Chemometrics offer powerful tools that can deal with large amounts of data, can extract useful information and can reduce the noise and irrelevant information for the task at hand. The performance of a calibration model is evaluated by certain statistical parameters that indicate in essence, how good a predicted value is, how close to the real one (accuracy), and how well a model can perform when certain tasks are given (robustness).



Settled juice samples (911) were collected over three vintages (2016 – 2018). A total of 28 cultivars (12 white and 18 red) and 14 grape-growing districts (Sawis) were represented in the set. The reference values for FAN and ammonia were generated using the Arena 20XT (Thermo Fisher Scientific, Waltham, MA) and the Megazyme™ K-PANOPA (Ireland) for FAN and Enzytec™ Fluid Ammonia (R-Biopharm, Germany) kits. For the IR spectroscopy, three benchtop instruments were tested: MPA FT-NIR (Bruker Optics, Germany), Alpha-P ATR FT-MIR (Bruker Optics, Germany), and WineScan™ FT120 (FOSS Electric, Denmark). Two additional instruments were tested for the cultivar effect task, MicroNIR (VIAVI Solutions Inc., USA) and FieldSpec 4 Standard-Res Spectroradiometer (ASD Inc., Malvern Panalytical, USA). The statistical modelling was done on OPUS v. 7.2 for Microsoft (Bruker Optics, Germany).



To illustrate the feasibility of using IR for YAN determinations, we have considered two practical scenarios. It is known that YAN values can be affected by vintage effect. Therefore, in the first scenario, a model based on previous years’ samples (2016 and 2017) was built and used to predict values for a new vintage (2018). This can correspond to a real case scenario, in which the samples arriving for testing have to be measured using calibrations generated in previous years. The results showed that both the WineScan and the MPA performed the tasks to the required level for quantification. The Alpha-P can be used in this scenario only for screening the new samples, not for accurate quantification.

It is known that the cultivar (the genetic make-up) plays a big role in the YAN level and composition. In the second scenario, a model built on data obtained from major cultivars (Sauvignon blanc, Chenin blanc, Chardonnay, Shiraz, Merlot and Cabernet Sauvignon) was tested on minor cultivars (Marsanne, Roussanne, Pinot gris, Verdelho, etcetera). Practically, when analysing samples, certain cultivars are well represented in the data set, while other, more ‘exotic’, will appear only seldom. It can thus happen that a calibration can be set up to include only major cultivars. In this case, again the WineScan and the MPA performed best, but the Alpha-P also gave accurate results. The other two instruments considered, the MicroNIR and the FieldSpec 4, produced unreliable results.


Take home message

Even though the performance vary depending on the IR region and mode in which the instrument does the measurements, the study showed that it is possible to calibrate IR spectroscopic instruments for the accurate measurement of YAN, FAN and ammonia concentrations using various data sets and scenarios. In all cases, transmission FT-IR spectroscopy (FOSS) and FT-NIR spectroscopy (MPA) produced models capable of good to excellent quantification. Both of these instruments showed sufficient robustness against samples originating from different varieties, growing conditions and vintages, addressing the concerns of applying this technology to the wine industry. Given the accuracy, robustness, high throughput and cost-effective nature, the models produced by both FT-IR and FT-NIR spectroscopy provide winemakers with the opportunity to make timelier and informed nutrient supplementation decisions, facilitating the achievement of their desired wine style and quality. As a result, these calibrations have been added to the existing IR method portfolio in the CA Lab of the DVO.


FIGURE 1. Strategy for the evaluation of the models based on cultivar and vintage effect.



The use of infrared (IR) spectroscopy, combined with chemometrics, is gaining traction due to its advantages over traditional methods. Therefore, various IR spectral instruments measuring in different modes and ranges of the IR spectrum (FT-IR, FT-NIR and ATR-MIR), were compared and evaluated for their accuracy to measure total YAN, as well as its components, FAN and ammonia, separately. Furthermore, the robustness of the calibrations against the inherent variability of the grape juice matrix was assessed to ascertain the feasibility of the integration of this technology in an industrial context.


– For more information, contact Astrid Buica at abuica@sun.ac.za.


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