Introduction
In the beer industry, there is a need for in-line analysis of barley for physicochemical and functional properties. The principal quality parameters include protein, moisture, starch, hardness, nitrogen, and β-glucan, a measure of malting resistance.
Barley with too much protein is difficult to process and can result in a higher malting loss as well as effects on foam retention and increased negative haze effects. Once the malting process is complete, the protein content should be from 10% to 11%. Nitrogen level in the grain is critical and must be in between 1.4% and 1.7%. Low nitrogen levels indicate enzyme deficiencies and can lead to problems with yeast nutrition, insufficient beer foam stability, and low enzymatic activity. High nitrogen indicates an excess of carbohydrates and results in low yields and problems with wort filterability and beer turbidity.
Starch is the component with the greatest effect on overall yield because is it the fermentable source for yeast growth and subsequent alcohol production during fermentation. Moisture content is important because of the effects on long-term storage. Harvesting conditions can vary moisture content from 12% in very dry conditions to over 20% in wet conditions. If the moisture content is greater than 15%, it must be dried before long-term storage or the barley will not germinate properly. Hardness determines the suitability of the grain for malting purposes. In general, soft grain is suitable for malting while hard grain is good for feed. High moisture also increases the risk for mold and fungi and reduces value as the dry weight is used when preparing components for brewing. During germination, barley undergoes a complex series of biochemical reactions to produce malt. β-glucans are gums that are produced during the malting process and are products of the breakdowns of the hemicellulosic cell walls. They have a strong effect on extract yield and wort viscosity.
Raw Material and Manufacturing Monitoring
There are numerous advantages to monitoring these parameters using NIR spectroscopy as opposed to traditional methods. The normal advantages of cheaper, faster, and less manpower are certainly applicable, but there are others as well. Yeast strain identification and adulterated products can be determined using NIR spectroscopy. Also, it provides an easy way to measure composite samples from all points in a delivery. Suppliers can place low quality grain at the bottom of a load and high quality at the top. A quick method like NIR spectroscopy can determine if barley is uniform throughout the entire load. Once the malting process starts, NIR spectrometers and calibration models can measure the parameters of interest during germination and assess the malt quality of the final product. Real-time feedback during germination allows the brewers to accelerate or decelerate the process by adjusting humidity and temperature parameters. The commercially acceptable limit for moisture in finished malt is 5%. This parameter is essential in calculating dry matter, which is used as a yardstick by brewers for all other quality parameters. Successful studies in measuring moisture and nitrogen using NIR spectroscopy in both whole and ground malt have been conducted.
Once malt is ground through the mill, the actual brewing process begins with mashing. The main objective of the mashing process is to form maltose and other fermentable sugars from solubilized starch. Parameters tested for malting optimization include maltose, glucose, maltotriose, total carbohydrates (TC), fermentable sugars (FS), total soluble nitrogen (TSN), free-amino nitrogen (FAN), and β-glucan. Studies measuring these parameters have been performed with results acceptable enough for screening purposes and process monitoring. However, many of these studies were performed on filtered wort in transmission mode but did show that at-line monitoring of the wort is possible. Mashing matter is thick enough that a direct transmission measurement was difficult in the past, but advances in fiber optics, transflectance probes, improved mechanisms for diffuse reflectance measurements, and cleaning mechanisms have made the prospect of online mash monitoring more feasible. Some analysis and studies have been conducted on hops and yeast quality as well. For a hop grower, maximum dry matter content at harvest will generally result in higher yield but this does not necessarily result in optimal brewing characteristics. Three parameters in hops that have been studied using NIR spectroscopy are α-acid content, β-acid content, and Hop Storage Index (HSI). α-acids add bitterness to beer after being isomerized when added to boiling wort, β-acids add bitterness from oxidation during long-term storage or lagering of a beer, and HSI measures the amount of α-acid loss when hops are stored at constant room temperature for six months. Correlation between NIR spectra and these parameters was good for all three. Yeast parameters include strain identification, glycocen, trehalose (both major storage carbohydrates), and protein content. Yeast protein content is especially important in determining payment in the sale of spent brewery yeast by-product. In-process monitoring of yeast concentration during brewing has been successfully implemented as well.
Fermentation Optimization
Optimization of fermentation is a complex process because many parameters can affect the final alcohol content and overall yield. While optimum efficiency is achievable if all parameters are kept in control before and during fermentation, this does not always happen because of the time it can take to identify equipment failure. HPLC is usually the method of choice for fermentation monitoring, and while it is effective, there are many drawbacks to using it, such as the users skill level, cost, time required, accuracy, and the required centrifuging of mash samples.
All make HPLC a less than the ideal method for fermentation monitoring. In the case of beer, alcohol content, original gravity, and original extract have all been successfully correlated with NIR spectra using calibration models.
Conclusion
The potential of fermentation optimization using real-time feedback from NIR spectra cannot be understated. Fermentation information can be used to optimize protocols such as changing enzymes, process parameters, and nutritional supplements, optimizing both yield and fermentation time. This results in considerable savings in raw materials, processing fuel, labor, maintenance, and equipment, potentially saving large breweries millions of dollars per year.
References
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https://www.tandfonline.com/doi/pdf/10.1080/10408398.2015.1115954
Near-Infrared Spectroscopy in The Brewing Industry – Sileoni, Marconi, and Perretti, Critical Reviews in Science and Nutrition, 55:1771-1791 (2015)
https://www.tandfonline.com/doi/full/10.1080/10408398.2012.726659
Process Analytical Technology for the Food Industry -O’Donnell, Fagan, Cullen, et al., Springer, Food Engineering Series (2014)