ZYMON at 37th FEW

ZYMON at 37th FEW

Interested in knowing more about Actionable In-line Monitoring of Bioethanol Fermentation with ZYMON? Find us on Booth 606 at FEW 2021 (13-15th July, Des Moines). Our VP of Bioethanol Business Unit Steen Skjold-Jørgesen will also be presenting the "Experience with online actionable process monitoring based on MIR", July 14th 3:30 PM.

Experience  with online  actionable process monitoring based  on  Mid Infrared Spectroscopy     

Several attempts have been made to apply IR spectroscopy to online monitoring of fermentation processes. Socalled 'Near Infrared (NIR)' generally fails due to need for frequent recalibration to the specific system being measured. Mid Infrared Spectroscopy provides very distinct information about the molecular species at hand, but the technology has so far been deemed too fragile for industrial application. 

Specshell has developed ZYMON - a robust precalibrated instrument tailored for grain to ethanol fermentation. The instrument provides online actionable information about the early phases of fermentation where intervention is still possible. This paper provides an overview of the technology behind ZYMON, as well as an account of the industrial experience so far. 

The instrument went to work day 1 without a need for plant specific calibration, it measured for months in a row without interruption, and it provided thorough insights with the performance of the process as well as the added ingredients such as yeast and enzymes. Infections were spotted early on as well as deviations from the production protocol around filling, ingredient addition as well as cleaning cycle.

Fermentability Predictor for brewing QC with SIBA

Fermentabiliyt Predictor - Inline RDF/ADF estimation at the mash-tun

Using the Specshell Inline Brewing Analyzer (SIBA), not only can you measure standard parameters such as extract formation and carbohydrate profile without manual sampling processes, but you can now also determine wort fermentability as both unit of apparent and real degrees of fermentability!

The SIBA system will automatically quantify batch-to-batch variation in wort fermentability, preventing labor intensive methods with slow turnarounds. This breakthrough technology now gives brewers the tools to deliver to-spec wort even more easily, improving recipe and mash schedules optimization.

 Equip your brewhouse with the Fermentability Predictor as part of your robust stand-alone real-time process monitoring system SIBA and yield the benefits of lab-free control.


The fermentability of a given batch of wort is a function of grist composition, ingredient quality, brewing configuration (infusion / decoction etc.), mash schedule, and the introduction of endogenous enzymes. Controlling the mashing process to maximize extract formation while controlling fermentability is essential for brewers to guarantee that the wort is produced up to specification.

Yeast mediates the conversion of fermentable sugars (maltotriose, maltose, glucose, fructose, and sucrose) to alcohol, leaving behind the longer maltodextrins and other minor components. Fermentability describes proportion of wort extract that yeast are able to ferment to alcohol, or effectively the percentage of sugars that are convertible to alcohol in a given wort. Several parameters other than wort sugar composition influence a yeast's contribution to fermentability, such as wort free amino acid content, yeast strain and fermentation conditions, but predominantly in most barley based worts, it is the sugars that determine this parameter.

Wort fermentability is measured in the brewing laboratory through forced fermentations of overpitched wort with differing methods described by the American Society of Brewing Chemists and European Brewing Chemists. This process is time consuming, labor intensive, and can be subject to a number of user and systematic errors. However, this value is critical for improving recipes and mash schedules when faced with variable raw material quality – this delay in data generation is subsequently limiting to brewhouse optimization and daily QC, potentially resulting several off-specification batches before issues can be addressed.

The Fermentability Predictor integrated into the SIBA platform now offers brewers inline and automated quantification of the wort fermentability (apparent and real fermentability) at the end of each batch. Inline measurement of wort fermentability prediction allows digitalized QC for daily brewhouse operation and batch-to-batch optimizations to be made while eliminating laboratory workload. Improvements in wort fermentability can be translated into greater product yields, time savings in the brewhouse, or increase in adjunct use.

Fig1. Fermentability Prediction historic data for the several batches of a specific recipe. Recipe upper and lower limits set to recipe requirements by the brewery, showcasing the visual quality control feature of SIBA.

Apparent and Real Fermentability

There are two main ways to consider the degree of fermentability of a wort or beer, 'apparent' and 'real', the main difference being how the final extract is considered with regards to the concentration of ethanol in this sample [1] [2 ] [3].

The Apparent Degree of Fermentation is calculated from the apparent extract after final attenuation (AEFA) and the original extract (OE), and is widely used to compare batches of the same recipe with similar original extracts; however, this parameter should be handled with care when comparing different OEs. AEFA is directly calculated from the density of the product after fermentation and converted to a value in ° Plato using the same scale as before fermentation. This assumes that the starting matrix (binary mixture of carbohydrate and water) pre-fermentation have the same composition as after fermentation; however, the fermented product is a ternary mixture as it contains ethanol. The lower density of the ethanol significantly reduces the measured final extract compared to a comparable mixture of sugars and water.

The alternative is to measure or calculate the Real extract (RE, in ° P), which accounts for the concentration of lower density of ethanol effectively estimating the true quantity of dissolved sugars (and proteins) remaining after fermentation. This can be done using labor intensive laboratory techniques or more typically calculated using the formula of Balling, which when used in combination with a factor accounting for sugar uptake by yeast, then allows calculation of the Real Degree of Fermentability (RDF). This parameter allows comparisons between worts of varying original extracts to be more reliable in terms of fermentation performance.

The SIBA Fermentability Predictor estimates both the ADF and RDF of each batch during mash out, giving brewers the resources to control and optimize their processes easily for increased mash efficiency and time savings!

Conventional laboratory fermentability measurements

To measure the fermentability of a given wort there are several prescribed methods from the European Brewing Chemists (EBC, EBC 9.4) and American Society of Brewing Chemists (ASBC, Wort-5) that outline how to perform this technique. A volume of wort is boiled and fermented with a large pitch of fresh yeast, with the extract measured before and after. However, these and other methods in the scientific literature disagree on the duration of fermentation, the fermentation temperature, agitation, and yeast pitch size. All of these parameters significantly influence the results of this test. Too short a fermentation time and the wort will not be fully attenuated, too long a duration or too warm and you will experience ethanol evaporation leading to higher AE.

Specshell fermentation scientists have examined both EBC and ASBC methods, as well as the impact of several parameters and have established a reliable test bed for determining wort fermentability that neither significantly over-nor under-estimates AE measurements. This highly reproducible method was applied to worts brewed and measured using SIBA on our in-house pilot system, to produce reliable calibration samples applicable to a wide range of OE and fermentabilities.

Fig2. Validation of the multivariate models using independent data set measured in-line using the SIBA

Model performance

Specshell data scientists utilized the in-house produced calibration samples to train multivariate models for prediction of the RDF. The models use advanced processing steps to highlight the key spectral features that contain information about the RDF. The trained models were tested with an independent validation data set consisting of mashes that were conducted and measured in-line using a SIBA instrument in our in-house pilot facility. The validation set contained samples with a wide range of RDF.

Model performance is robust, enabling accurate prediction of wort RDF across the whole validation range, with a very high accuracy (correlation coefficient R 2 = 0.999 and a root mean squared error in validation (RMSEV) = 0.0145 RDF units) and a high precision ( the standard deviation of the residuals was 0.56 RDF units).

Limits of Use

Since the SIBA is measuring inline at the mash-tun, processes that influence fermentability that take place after wort is transferred from this vessel are not captured with the measurement system. This for instance might include the use of thermostable enzymes that are still active during and can increase the proportion of fermentable sugars. Also, the addition of syrups and adjuncts post mashing will likely increase the fermentability of the wort beyond what was predicted by the Fermentability Predictor during mashing. In these cases, the predicted the fermentability value still has significant value as a benchmarking tool to assess mash performance or ingredient contributions between batches.


European Brewing Chemists (EBC) 8.6.1 Fermentability, Attenuation Limit of Wort Reference Fermentation - 2002

European Brewing Chemists (EBC) 9.4 Original, real and apparent extract and original gravity of beer - 2004

American Society of Brewing Chemists (ASBC) Wort-5: Yeast Fermentable Extract

Main contributors

Application Scientist: MsC Jabob Grønberg and Dr Joshua Mayers

Data Scientist: Dr. Pau Cabaneros

This project was run under the Innobooster program by the Innovationfonden, part of the Intelligent Brewing program.

Next Generation Carbohydrate Analysis

Next Generation Carbohydrate Analysis

In the current Horizon2020 SME project, Specshell is developing new robust inline analyzers for the ethanol and starch industries; where part of our Chemistry team, Joshua Mayers, Katrin Pontius and Pernille Christensen, is pushing the existing technological and scientific limits of carbohydrate analysis. With the development of a new quantitative method combining HPAEC (High Performance Anion Exchange Chromatography) and HPLC with high field 2D-NMR, Specshell is now able to accurately characterize complex carbohydrate matrices from starch-based industries

In the Horizon2020 project the Specshell team has discovered several limitations and shortcomings in the industry's analytical methods for characterizing the complex fermentation slurries. Today the industry largely relies on HPLC protocols which are inherently poor for characterizing and quantifying dextrins, which leaves large parts of the hydrolyzate sample matrix poorly characterized. As chemometric models can never be better than the analytical reference methods they rely on, the efforts in analytical reference methods are a key element in Specshell's mission to become the new standard must-have equipment in every bioethanol plant and starch based industries.