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Near-infrared Spectroscopy in the Brewing Industry a

b

a

Valeria Sileoni , Ombretta Marconi & Giuseppe Perretti a

Italian Brewing Research Centre, University of Perugia, Perugia, Italy

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Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy Accepted author version posted online: 19 Oct 2012.

Click for updates To cite this article: Valeria Sileoni, Ombretta Marconi & Giuseppe Perretti (2015) Near-infrared Spectroscopy in the Brewing Industry, Critical Reviews in Food Science and Nutrition, 55:12, 1771-1791, DOI: 10.1080/10408398.2012.726659 To link to this article: http://dx.doi.org/10.1080/10408398.2012.726659

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Critical Reviews in Food Science and Nutrition, 55:1771–1791 (2015) Copyright O c Taylor and Francis Group, LLC ISSN: 1040-8398 / 1549-7852 online DOI: 10.1080/10408398.2012.726659

Near-infrared Spectroscopy in the Brewing Industry VALERIA SILEONI,1 OMBRETTA MARCONI,2 and GIUSEPPE PERRETTI1 1

Italian Brewing Research Centre, University of Perugia, Perugia, Italy Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy

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This article offers an exhaustive description of the use of Near-Infrared (NIR) Spectroscopy in the brewing industry. This technique is widely used for quality control testing of raw materials, intermediates, and finished products, as well as process monitoring during malting and brewing. In particular, most of the reviewed works focus on the assessment of barley properties, aimed at quickly selecting the best barley varieties in order to produce a high-quality malt leading to high-quality beer. Various works concerning the use of NIR in the evaluation of raw materials, such as barley, malt, hop, and yeast, are also summarized here. The implementation of NIR sensors for the control of malting and brewing processes is also highlighted, as well as the use of NIR for quality assessment of the final product. Keywords

NIR, barley, beer, malt, malting

INTRODUCTION Beer is the world’s most widely consumed alcoholic beverage with a world production of 1,846,393 hl in 2010 and is also likely the oldest alcoholic beverage (The Barth Report, 2010/2011). Humans have been brewing beer since the beginning of urbanization and civilization in the Neolithic period (Meussdoerffer, 2009). Four raw materials are required for beer production: barley, hops, water, and yeast. The quality of these raw materials has a significant influence on the quality of the final product. Knowledge of the raw materials properties and their effects on the brewing process and final product provides the basis for their handling and processing (Kunze, 2004a). Therefore, characterization of the quality of raw materials used and information for process control, in particular for the malting and fermentation steps, are fundamental for ensuring a high-quality final beer. Ideally, quality assessment and process control methods routinely used in food manufacturing should be noninvasive, nondestructive, and rapid enough to ensure timely processing. The development of rapid analytical methods for food product analysis relies mainly upon two approaches: using substrate physical properties to obtain analytical information and automating chemical methods to the greatest extent possible. The Address correspondence to Dr. Ombretta Marconi, Department of Agricultural, Economic and Food Science, University of Perugia, Via San Costanzo, Perugia, 06126 Italy. E-mail: [email protected]

most rapid analytical methods currently available are based on the physical properties of food products and include spectroscopic methods, such as near-infrared spectroscopy (NIRS) (McClure, 1993; Williams, 2001). NIRS uses the spectral region between 800–2500 nm (or 12500–4000 cm¡1), which is the first spectral region exhibiting absorption bands related to molecule vibrations. This region is characterized by harmonics and combination bands of the fundamental vibrations that occur in the mid-infrared region (2500 up to 25000 nm) (Siesler, 2008). The intensity of a given absorption band active in the NIR region is associated with the magnitude of the dipole change during the displacement of atoms in a vibration and with its degree of anharmonicity. Both phenomena are strongly associated with bonds involving hydrogen and another heavier element, such as carbon, nitrogen, or sulphur (O H, C H, N H, or S H bonds). As these types of bonds are common in organic matter, NIRS is widely used to analyze the composition of food products. For combination bands permitted by anharmonicity, only one of the combining vibrations need be active (i.e. causing a dipole change). As a result, some vibrations that cannot be observed in middle infrared spectra are visible in NIR spectra (Dahm and Dahm, 2001). Unfortunately, the overlap of combination bands and overtones (transitions between more than one energetic levels) greatly decreases the specificity of NIRS, especially for interpretation purposes. The poor specificity of the technique is one of the main reasons why it has been largely ignored by traditional spectroscopists. However, NIR spectroscopy has

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become more attractive with (a) the more recent availability of chemometric evaluation procedures for qualitative discrimination and quantitative determination (Martens and Naes, 1989; Martens and Martens, 2001; Naes et al., 2002; Conzen, 2006; Rom
ıa and Bernardez, 2008) and (b) the perception that low band intensities can be advantageously exploited for samples with larger thicknesses that are more easily handled (Duncan, 1991). Absorption bands arising from NIR vibrations are typically very broad, leading to spectra that often lack detailed structure, making it difficult to assign individual features to specific chemical components. Also, the spectra contain significant amounts of information that are hidden by the overlapping nature of the specific absorption bands present. However, multivariate (multiple wavelength) data analysis tools and calibration techniques [e.g. partial least-squares (PLS) regression] are often employed to extract the desired chemical information from spectral data sets (The American Society for Testing and Materials, 2001). Indeed, by applying multivariate data analysis techniques, such as PLS regression analysis, sample spectra can be mathematically separated from the matrix, eliminating the need for physical or chemical sample treatments prior to analysis (Geladi and Kowalski, 1986; Haaland and Thomas, 1988; Brown, 1995). In fact, NIR spectra can be intensively exploited for qualitative and quantitative chemical and physical analytical purposes, by using: (a) regression methods, to link the spectra to quantifiable sample properties and (b) classification methods, to group similar samples together according to their spectra (Armenta et al., 2010). NIRS is an indirect method that requires a large number of samples, covering a broad variability for each analytical parameter, with a uniform distribution between extreme values, to obtain an accurate calibration equation (Cruciani et al., 1989). The technique uses a polychromatic source and as wavelength selection is needed, NIRS instrumentation employ a wide range of monochromators and detectors. In fact, modern NIR instruments can be classified in terms of the wavelength selection technology used as follows: filter instruments, Light Emitting Diodes (LEDs) source self-selecting band instruments, dispersive spectrophotometers, and Fourier Transform (FT) spectrophotometers (Livermore et al., 2003). Early filter instrumentation required the use of Multiple Linear Regression (MLR) for calibration. Subsequently, PLS calibrations were developed with the introduction of monochromator instruments. For the MLR approach, a specific number of wavelengths (or frequencies), k, are chosen such that k 14  P of original gravity) beers, as well as beer6 lemonade mixtures and beers directly from the fermentation tanks. A repeatability of § 0.010% v6 v was determined. The mean deviation between the Alcolyzer and distillation determinations was around zero for all beer types. Therefore, the Alcolyzer showed no systematic error. The overall accuracy for the 95% confidence interval was approximately § 0.04% v6 v. It was concluded that the new Alcolyzer represents a successful new application of NIRS for the quantitative analysis of alcohol content in beer (Zanker and Benes, 2004). Similar results were presented 1 year later by I~n
on et al. in 2005, where NIR was used to determine important indicators

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of the quality of beers, such as original and real extract and alcohol content, using a PLS calibration approach. The sample population used consisted of 43 samples, obtained commercially in Spain and including different types of beer. Cluster hierarchical analysis was used to select calibration and validation data sets. Two methods of sample introduction were compared critically, on the basis of spectral reproducibility for triplicate measurements and after careful selection of the best spectral preprocessing and spectral range for building the PLS model, to obtain the best predictive capability. For each mode of sample introduction, two calibration sets were assayed, one calibration was based on the use of 15 samples and a second extended calibration was based on the use of 30 samples, thus leaving 28 and 13 samples, respectively, for validation. The best results were obtained for 1 mm flow cell measurements. For this method, original zero-order spectra data in the ranges 2220–2221 and 2250–2350 nm were chosen. For the real extract, original extract, and alcohol, the absolute mean difference values (dx¡y) for the two different validation sets and standard deviation of mean differences (sx¡y) values ranged from ¡0.04 and 0.07% w6 w, ¡0.01 and 0.13% w6 w, and ¡0.01 and 0.1% v6 v, respectively. The RMSEP values were 0.156 0.14% w6 w for real extract, 0.286 0.22% w6 w for original extract, and 0.086 0.09% v6 v for alcohol. The maximum errors in the prediction of any of these three indicators for a new sample were 2.2% w6 w, 1.2% w6 w, and 1.9% v6 v for real extract, original extract, and alcohol, respectively. This method compared favorably with the automatic reference method in terms of speed, reagent consumed, and waste generated (I~ n
on et al., 2005). Comparable results were obtained 1 year later by the same authors (Llario et al., 2006). They used Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATRFTIR) combined with a PLS calibration approach, again for measurement of the same important quality parameters of beers (original and real extracts and alcohol content). Using a calibration set comprising 12 samples, selected via hierarchical cluster analysis, and a validation data set of 11 samples, the absolute mean difference values (dx¡y) and standard deviation of mean differences values (sx¡y) of the real extract, original extract, and alcohol content were 0.009 and 0.069% (w6 w), ¡0.021 and 0.20% (w6 w), and ¡0.003 and 0.130% (v6 v), respectively. The Root Mean Square Error of Prediction values ranged from 0.075 to 0.107% w6 w for real extract, were 0.20% w6 w for original extract, and varied from 0.12 to 0.14% v6 v for alcohol content (Llario et al., 2006). The same authors also applied a combination of mid-infrared (MIR) and NIRS for the determination of original and real extract and alcohol content in beer (I~ n
on et al., 2006). For each technique, spectra were obtained in triplicate. In the case of NIR, a 1 mm path length quartz flow cell was used, whereas attenuated total reflectance measurements were used for the MIR range. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A

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critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. PLS and ANN were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR–MIR combined spectra, and in all the cases ANN performed better than PLS, which may be due to nonlinearity in some of the data. The RMSEP values obtained for the combined NIR–MIR spectra for the determination of real extract, original extract, and ethanol were 0.076% w6 w, 0.14% w6 w, and 0.091% v6 v, respectively (I~ n
on et al., 2006). In 2010, another study was published that combined NIRS with another technique in order to improve the predictive performance (Castritius et al., 2010). NIRS was combined with refractometry to determine the concentration of alcohol and real extract in various beer samples. A PLS regression was used to evaluate the correlation between the spectroscopy6 refractometry data and alcohol6 extract concentration. This multivariate combination of spectroscopy and refractometry enhanced the precision of the determination of alcohol as compared to single spectroscopic measurements, due to the effect of high extract concentration on the spectral data, especially for nonalcoholic beer samples. A major advantage of this NIRS-refractometry combined method is the use of one robust measurement device with one flow cell and a very small sample volume. This combination would also enable an additional specification of novel mixtures of beverages compared to commercial analysis methods, especially for soft drink or fruit juice-based matrices. The sample set used for this investigation consisted of 78 samples, including nonalcoholic beer (alcohol concentration between 0.05 and 0.5% v6 v), draft beer, beer mixtures, regular beer, and bock beer. Of these, 28 samples were used for model building (calibration set) and 20 samples were used for testing the obtained model (validation set). The reference value of the alcohol and the extract concentration of the beer samples were measured with an Alcolyzer Plus Beer Analyzing System (Anton Paar GmbH, Graz, Austria). A grating NIR spectrometer was used for these NIR measurements and the spectra were measured in a flow cell with a 10 mm pathlength, thermostatted at 20 C. The spectra were recorded in the spectral range of 1100–1220 nm (according to the second overtone of CH, CH) by accumulating five scans with a resolution of 4 nm using water as a reference. Three methods were developed to determine the concentration of ethanol in the NIR spectra using ethanol standards and beer samples from the calibration set as follows: method A, firstorder derivation, absorbance at different wavelengths as reference for the alcohol concentration; method B, linear baseline correction, absorbance at different wavelengths as reference for the alcohol concentration; method C, linear baseline correction, area beneath NIR spectra as reference for the alcohol concentration. The RMSEP of the validation process for alcohol and extract concentration were 0.23% w6 w (method A), 0.12% w6 w (method B), 0.19% w6 w (method C), and 0.11%

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w6 w (method A, B, and C), respectively (Castritius et al., 2010). Finally, it is important to consider that the Analysis Committee of the European Brewery Convention (EBC) included determination of alcohol content of beer by NIRS in 2008. The method is called Analytica-EBC 9.2.6—Alcohol in Beer by NIRS. Beer samples are degassed avoiding the loss of alcohol content but ensuring that all carbon dioxide is removed so that it cannot interfere in the analysis. Subsequently, beer samples are analyzed by use of either a scanning or filter NIR instrument. Absorbance measurements between 800 and 2500 nm are determined by either transmission or by transflectance. The method can be applied to all beers. Precision values% (V6 V) were determined in two collaborative trials, one for Low and Non-Alcohol Beers, with 15 laboratories, and the other for Normal Alcohol Beers with 23 laboratories, analyzing in both cases five beer samples. A repeatability (r95) value of 0.022% (V6 V) and a reproducibility (R95) value of 0.103% (V6 V) were obtained for alcohol values over the range 0.97– 0.81% V6 V, while a r95 value of 0.032 and a R95 value of 0.099 were obtained for alcohol values between 2.18 and 8.77% V6 V.

hand, new NIRS sensors are being developed and also implemented to monitor malting and brewing processes in production facilities with successful results. These applications reflect the potential of NIRS as a powerful tool for general process monitoring in real time. In fact, PAT is playing an ever increasing role in product–process optimization strategies and NIRS has significant potential as a PAT method. Increased demands for effective product control require advanced analytical tools that allow real-time or short-term monitoring and precise control of product quality along the production lines in factories and in the field. In comparison with other analytical techniques, optical spectroscopy, such as NIRS, has a wealth of inherent advantages, such as good temporal/spatial resolution and the potential for remote, noninvasive sensing. The contribution of on-line analysis to process control is growing, but only a very limited number of applications can be considered to be fully in-line. Therefore, the implementation of NIRS in the brewing industry has the potential to enhance process control, with a consequent reduction of time and production costs that will lead to a higher quality final product.

REFERENCES CONCLUSION The implementation of NIRS in brewing applications has been summarized here. This review has shown that NIRS is widely used for the analysis of raw materials, intermediates, finished products, and in process control. In particular, many studies have shown the application of NIRS to the quality assessment of barley, in particular, evaluation of its malting quality in early generations, genotypes classification, micotoxin detection, and quantitative analyses of moisture, nitrogen, and b-glucan. These parameters all change with endosperm modification, and therefore constitute an index of the quality of the malt. For this reason, the assessment of malt suitability to produce beer and monitoring of the malting process by NIRS are extensively described in this review. The implementation of NIR in the evaluation of raw materials, such as hop and yeast, has also been reviewed. Analysis of intermediate products by NIRS has also been described, including quantitative analyses of wort, and in particular, extract, total carbohydrates, total nitrogen, fermentability, and a-free-amine nitrogen (FAN). As shown here, NIRS has also been applied to analysis of the finished product, beer, to determine real extract, ethanol, nitrogen, and bitterness. Finally, a number of studies discussed here have shown that it is possible to constantly monitor the production process through NIRS, including the mashing phase and evolution of fermentation. However, it is important to note that the majority of studies of NIRS for quality control of raw materials, intermediates, and final product has been carried out on a laboratory scale, an environment removed from the production facilities used for commercial malting and brewing processes. On the other

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