Beer is the world’s most widely consumed alcoholic beverage. Four raw materials are required for beer production: barley, hops, water, and yeast. The beer market has become especially competitive in recent years with the advent of microbreweries, which market their products based on unique recipes, quality, and distinction from the large-scale breweries. The quality of the raw materials has a significant impact on the final product. Before the brewing process begins, characterization of barley, as well as yeast and hops, can help the brewer optimize the process. Process control feedback during brewing, particularly during the malting and fermentation stages, are critical and fundamental for brewing high-quality beer. Feedback on moisture and nitrogen in barley, germination parameters, sugars during mashing, and alcohol and original gravity during fermentation can help the brewer optimize the process as well as reduce costs and resources for brewing. Moisture and total nitrogen content in barley are critical parameters. Slack malt is defined as too high in moisture content. It can lose aroma in storage and not break up properly during milling. High total nitrogen decreases carbohydrate content and yields a lower extract. The reactions that occur during germination are complex and it is especially important to monitor moisture during this phase because it has a strong effect on the reactions. Sugars formed from starch during mashing can be monitored to optimize yield and minimize cost. Fermentation monitoring for alcohol content, original gravity, and original extract can be used to optimize protocols such as changing enzymes, process parameters, and nutritional supplements. Currently, methods for testing these parameters such as HPLC are expensive, laborious, and time-consuming, especially when implemented in a process setting. There is a need for fast, cost-effective, and real-time monitoring of parameters at all stages of the brewing process. One such method that has been examined is NIR spectroscopy.
- Total Nitrogen Content
- Total Lipid Content
- Hop Storage Index (HSI)
- Total Carbohydrates (TC)
- Fermentable Sugars (FS)
- Total Soluble Nitrogen (TSN)
- Free-Amino Nitrogen (FAN)
- Hot Water Extract (HWE)
- Soluble Protein
- Original Extract
- Real Extract
- Soluble Solids Content (SSC expressed as °Brix and °Plato)
- Maximum Volume of Foam (MaxVol)
Summary of Published Papers, Articles, and Reference Materials
Measurement of chemical parameters in all significant constituents of beer for quality control purposes has been studied using NIR spectroscopy under both at-line and online conditions. The results of most studies have been promising. A comprehensive review of multiple studies is presented measuring parameters of interest from initial raw material analysis all the way to final fermentation as well as discussion about the benefits of using these parameters in a real-time setting to optimize beer production. The analysis includes barley, hops, yeast, malting, mashing, and fermentation. One individual study of raw materials analyzed grain and maize for moisture, total nitrogen content, and total lipid content. Results were excellent for moisture and suitable for screening purposes for nitrogen and lipids, with likely improvement to occur if the samples were ground. Three studies analyzed beer fermentation for various sugar, acidity, alcohol, and foam analyses. The first was specific to beer wort and geared toward process analysis with excellent correlation achieved for °Brix, pH, and Biomass. The second study used two different types of algorithms to correlate different types of beer under different fermentation conditions to °Brix, pH, Alcohol, and MaxVol (a foam measurement) with good results obtained after model optimization. The third study was specific for craft beer and used three different types of craft beer to analyze Soluble Solids Content (SSC expressed as °Plato and pH. The spectral analysis was able to distinguish between filtered and non-filtered samples while creating calibrations suitable for screening purposes for each type of beer.
Scientific References and Statistics
Near-Infrared Spectroscopy in the Brewing Industry – Sileoni, Marconi, Perretti, Critical Reviews in Food, Science, and Nutrition, 55:12, 1771-1791, 2015
A comprehensive and exhaustive review of NIR spectroscopy in the brewing industry. Multiple works are reviewed for using NIR spectroscopy for quality control testing of raw materials, intermediates, and finished products, as well as process monitoring during malting and fermentation. All major constituents in beer are discussed (barley, hops, yeast, malt, water) as well as the benefits of measuring them when optimizing the brewing process. Listed below are some of the constituents measured and discussed in the review. Correlation coefficients are given when shown.
Principal quality parameters for barley include moisture, protein, starch, and nitrogen which is indicative of protein content. Protein-rich barley is more difficult to process and often results in a higher malting loss. It has effects on foam retention and negative haze effects can be observed when as little as one-third of the protein passes into the beer. The normal commercial requirement is a maximum of 11.5% protein in dry barley matter. The moisture content of barley can vary from 12% to 20% depending on harvesting conditions and it must be below 15% for long-term storage. If not dried high moisture barley can lose its ability to germinate properly as well as be at risk for mold and fungal contamination. The Analysis Committee of the European Brewery Convention (EBC) recommended the use of NIR for determining moisture and nitrogen in 2006. Studies using NIR spectroscopy for mycotoxin analysis have also been conducted and the potential was demonstrated for using NIR to measure contamination levels of various mycotoxins in barley. While promising it must be noted that these mycotoxins were detected in very low concentrations and more validation work will be necessary to prove the feasibility of accurately measuring these constituents while ensuring that the calibration models fit the mycotoxin concentration of interest and not some other parameter. Many methods have also been developed for other quality parameters such as hardness and β-Glucan. Extract yield, wort viscosity, and malt quality can all be directly correlated to β-Glucan in barley. Some work has also been conducted on genotype classification, which can have a substantial effect on changes during germination and malt production.
|Nitrogen||R² = 0.995||RMSEP = 0.66%|
|Moisture||R² = 0.999||RMSEP = 0.389%|
|Protein||R² = 0.97||RMSEP = 0.31%|
|Glucosamine (Mold)||R² = 0.92||RMSEP = 0.22 g/kg|
|Mycelial Dry Matter||R² = 0.94||RMSEP = 5.25 g/kg|
|Deoxynivalenol||R² = 0.933||RMSEP = 3.007 ppm|
|Aflatoxin B1||R² = 0.94||RMSEP = 0.183 ppb|
|Malt Extract||R² = 0.94||RMSEP = 2.29%|
|β-Glucan||R² = 0.88||RMSEP = 0.315%|
|Hardness (PSI)||R² = 0.83||RMSEP = 0.9 PSI|
The relative concentration of hops constituents depends on the hop variety and maturation stage at the time of harvest. For the grower, maximum dry matter content at harvest results in higher yield, but this does not necessarily result in hops with optimal brewing quality characteristics. Although limited in scope, studies have been conducted to measure α-Acids, β-Acids, and Hop Storage Index (HSI) in hops using NIR spectroscopy. The acid compounds are precursors to bittering agents and HPLC is the traditional method for analyzing these. HSI is the estimated alpha acid potential loss when hops are stored at room temperature for six months. Spectroscopic UV wavelength absorptions typically measure this at 325 nm (hop acids) and around 275 nm (degenerative compounds associated with oxidation). These studies demonstrated the ability to use NIR spectroscopy to measure these parameters in hops.
Parameters in Hops
|Hop Storage Index (HSI)||R2= 0.89||RMSEP=0.010%|
Glycogen and trehalose are both major storage carbohydrates in yeast. Yeast protein content is also an important physiological parameter and is used to determine the price of spent brewery yeast by-product. Studies have been conducted measuring these parameters using NIR spectroscopy with acceptable results. In the case of trehalose, results were much better using slurry for the constituent and this would be the preferred measurement in an online setting.
Parameters of Yeast Protein
|Trehalose (Dried)||R2=0.77||Not Given|
|Trehalose (Slurry)||R2=0.997||Not Given|
Steeping is the first step in the malting process. Sorted and cleaned barley are transferred into tanks and covered with water. During germination, barley undergoes a complex series of biochemical reactions to produce malt. Initial levels of 14% to 15% moisture in barley increase to around 42% to 44% at the end of germination. When the moisture reaches around 30%, the germination process begins by break down of the protein and carbohydrates matrices and the opening of the seed’s starch reserves. Steeping is complete when a sufficient moisture level is reached to allow uniform breakdown of the starches and protein. Monitoring the water content during germination is important for ensuring good malt modification. Studies have measured moisture on germinating barley using NIR spectroscopy with good success and high correlation. Other parameters have been studied as well for malt quality during germination with mixed results. If NIR spectroscopy could be used to monitor the germination process for malt quality, it would allow real-time adjustment of temperature and humidity parameters to accelerate or decelerate the process.
The main objective of the mashing process is to form maltose and other fermentable sugars from solubilized starch. Acceptable results have been achieved measuring these parameters using NIR spectroscopy. However, all these measurements were conducted on wort after sampling and most of the time, the samples were filtered and thermostated before scanning. Direct transmission measurement through mashing matter is very difficult and the filtering and temperature regulation is required. While the constituents of interest are proven to be measurable by NIR, more work will be required to validate a true industrial sensor to monitor mashing during the brewing process.
|Total Carbohydrates (TC)||Not Given||RMSEP=0.5 g/L|
|Fermentable Sugars (FS)||Not Given||RMSEP=1.8 g/L|
|Maltose||Not Given||RMSEP=0.5 g/L|
|Glucose||Not Given||RMSEP=0.6 g/L|
|Maltotriose||Not Given||RMSEP=1.4 g/L|
|Total Soluble Nitrogen (TSN)||Not Given||RMSEP=48 mg/L|
|Free-Amino Nitrogen (FAN)||Not Given||RMSEP=11 mg/L|
|Hot Water Extract (HWE)||R2=0.938||RMSEP=0.9%|
Numerous studies have been conducted using NIR spectroscopy to monitor alcohol content during beer fermentation and most have shown success. Alcohol monitoring using NIR as well as related constituents like original extract and real extract have worked so well that the Analysis Committee of the European Brewery Convention (EBC) approved using NIR for determination of alcohol content in beer. The method is called Analytica-EBC 9.2.6 – Alcohol in Beer by NIRS. Beer samples are degassed so that all carbon dioxide is removed and samples are analyzed using either a scanning or filter NIR spectrometer.
|Original Extract||R2=0.998||RMSEP=0.14% v/v|
|Real Extract||Not Given||RMSEP=0.076% v/v|
While conducted using different instruments and mostly on a laboratory scale, the studies documented in this review demonstrate the ability to use NIR spectroscopy for analysis of raw materials, intermediates, finished products, and as a process control tool in brewing, particularly during the malting and fermentation phases. Increased demand for product control of beer as well as many other liquid foods will require advanced analytical tools and NIR spectroscopy is a proven method for both online and at-line monitoring of brewing.
The development of new sensors has facilitated the implementation of NIR spectroscopy as a tool for monitoring the brewing process with successful results.
Near-Infrared Spectroscopy for Proficient Quality Evaluation of the Malt and Maize Used for Beer Production – Sileoni, Marconi, Marte, Fantozzi, Journal of the Institute of Brewing, 116 (2), 134-140, 2010
NIR Spectroscopy was used to analyze whole malt grains for moisture and total nitrogen content and maize grits for moisture and total lipid content. Total samples were two hundred ninety-five malt whole grains for moisture, two hundred eighty-one malt whole grains for total nitrogen content, one hundred twenty-eight maize grits for moisture, and one hundred two maize grits for total lipids. Different varieties were used for each sample type. An FT-NIR spectrometer collected spectra from 11500 cm-1 to 4000 cm-1 at 8 cm-1 resolution and sixty-four averaged scans per spectrum. Reference data for the parameters of interest were collected based on standard methods from the Analytica European Brewery Convention (Analytica-EBC). Various pre-processing methods and selective wavelength ranges were tested in the calibration models to optimize results.
Moisture R2= 0.9591 RMSEP= 0.165%
Wavenumber Region = 7501.9 cm-1 to 4246.6 cm-1
Total Nitrogen Content R2= 0.7796 RMSEP= 0.048%
Wavenumber Region = 9970.4 cm-1 to 7498.1 cm-1, 6101.8 cm-1 to 4246.6 cm-1
Moisture R2= 0.9488 RMSEP = 0.152%
Wavenumber Region = 9970.4 to 4246.6 cm-1
Total Lipid Content R2= 0.8427 RMSEP = 0.066%
Wavenumber Region = 8736.2 to 7498.1 cm-1, 6101.8 to 4246.6 cm-1
Correlation coefficients showed excellent results for moisture in both types of samples and results considered good enough for screening purposes in the case of total nitrogen content in malt and total lipid content in maize. Separate validation predictions for each model proved the feasibility of using these models for measuring the parameters of interest. It is likely that better results could be obtained for nitrogen and lipids if the samples were ground, but the results here show the potential of real-time monitoring of malt and maize used for brewing.
Beer Fermentation: Monitoring of Process Parameters by FT-NIR and Multivariate Data Analysis – Grassi, Amigo, Lyndgaard, et al., Food Chemistry 155 (2014) 279-286
The fermentation of beer wort was monitored for nine days using FT-NIR spectroscopy for the purpose of monitoring °Brix, pH, and biomass. Two different yeast strains were used at three fermentation temperatures for the data collection and all were replicated twice using two different sampling methods (directly from the supernatant and after centrifugation for fifteen minutes at 3000 g) for a total of six different experiments. Samples were collected in triplicate right after yeast pitching and then every twenty-two hours for nine days. Standard methods were used to determine reference values for the parameters of interest. FT-NIR spectra were collected in transmission mode using a 1mm pathlength cuvette from 12000 cm-1 to 4000 cm-1 at 16 cm-1 spectral resolution. One hundred twenty-eight scans were collected and averaged for each spectrum. Principle Component Analysis (PCA), Partial Least Squares (PLS), and Locally Weighted Regression (LWR) were used to determine wavelength ranges of interest for following fermentation evolution and to correlate the NIR spectral data to reference values for °Brix, pH, and biomass.
|°Brix||R2= 0.988||RMSEP= 0.259|
|pH||R2= 0.987||RMSEP= 0.112|
|Biomass (OD @ 620nm)||R2= 0.951||RMSEP= 0.211|
Results obtained from the different multivariate techniques confirmed the feasibility of measuring these parameters using FT-NIR spectroscopy. PCA results confirmed that the sampling method did not matter and that it was possible to follow fermentation evolution from a chemical point of view from the spectral data. PLS results showed acceptable models for °Brix, pH, and Biomass but did suggest a possible non-linear relationship between the spectra and parameters of interest. LWR and PLS in combination confirmed the non-linear relationship but also created robust and precise models with good correlation that worked well regardless of the sampling method. The results of this study prove the feasibility of measuring °Brix, pH, and Biomass using NIR spectroscopy and show the potential to use this method for process control in online industrial brewing systems.
Assessment of Beer Quality Based on Foamability and Chemical Composition Using Computer Vision Algorithms, Near Infrared Spectroscopy, and Machine Learning Algorithms – Viejo, Fuentes, Torrico, et al., Journal of Food Science and Agriculture 2018: 98: 618-627
NIR spectroscopy was examined as a method for measuring beer quality parameters. Six replicates of twenty-one types of beer from three different types of fermentation were used for the study. Fermentation types were top, bottom, and spontaneous, which all differ in their specific process, such as yeast type, production temperature, and fermentation time. Fifteen foam and color parameters were evaluated in the samples using the RoboBEER robotic pourer, one of which (MaxVol – Maximum Volume of Foam) was used as a reference method for NIR chemometric modeling. Standard reference methods were used to determine °Brix, pH, and alcohol. All samples were scanned using a NIR handheld spectrometer from 1600 nm to 2396 nm at 7 nm to 9 nm intervals. Principle Component Analysis (PCA) was used to identify relationships between the parameters and selective wavelength ranges of interest. Both Partial Least Squares (PLS) and Artificial Neural Networks (ANN) methods were used to create chemometric models correlating the NIR spectra to the parameters of interest.
|All Four Targets/Combined Output (ANN)||R2=0.97||RMSEP=0.97|
The ANN method proved to be more capable of fitting the target values to the spectral data than PLS and those results are shown above. ANN works using machine learning algorithms that simulate human brain processing and is typically suited to model complex linear relationships more accurately than PLS. PCA analysis identified relationships between specific NIR wavelengths and the parameters analyzed with Robobeer as well as resulting in an 85% accuracy when classifying beers according to fermentation type. The results here show promise for using NIR spectroscopy and RoboBEER as quality analysis tools in the production of beer.
Rapid Evaluation of Craft Beer Quality During Fermentation Process by Vis/NIR Spectroscopy – Giovenzana, Beghi, Guidetti, Journal of Food Engineering 142 (2014) 80-86
Three different types of craft beer were procured to use a portable VIS/NIR spectrometer to measure Soluble Solids Content (SSC expressed as °Plato) and pH directly on a craft beer production line. NIR transflectance spectra were collected from 450 nm to 980 nm at different stages of fermentation and were collected on both filtered and non-filtered samples. Reference values were collected for SSC and pH using standard methods. Various spectral pre-treatments were performed before Principle Component Analysis (PCA) and Partial Least Squares (PLS) regression models were created to evaluate the feasibility of measuring the parameters of interest.
R2= 0.87-0.88 RMSEP= 1.1-1.8 °Plato
R2= 0.77-0.96 RMSEP= 0.6-2.3 °Plato
R2= 0.69-0.92 RMSEP= 0.1-0.2
R2= 0.76-0.97 RMSEP= 0.06-0.2
PCA modeling showed clear discrimination in the spectra between the three different types of craft beer samples and proved that spectra of filtered and non-filtered beer were distinguishable. This could prove to be useful information for analyzing the condition of the process line. The PLS regression models showed mixed results, likely for a number of reasons. Color and turbidity conditions are different for each type of beer during fermentation, and this could affect the calibration models. Visual examination of the spectra showed different variations in noise between samples. From the limited scope of work presented here, it can be concluded that even using the worst correlated models in this study can at least provide a basis for craft beer analysis during the fermentation process. It is important to consider that craft beer manufacturers are smaller in scale than large breweries and typically only analyze for SSC and pH, making the use of a reasonably priced portable NIR analyzer a feasible method for improving fermentation conditions.
Process Analytical Technology for the Food Industry -O’Donnell, Fagan, Cullen, et al., Springer, Food Engineering Series (2014)