L'objectif de ce chapitre de notre mémoire été de faire une suivi pour finaliser et améliorer une démarche scientifique précédente consistant à maîtriser les coûts (énergétiques et environnementaux) du procédé de maltage en travaillant à faible humidité tout en conservant la qualité finale de malt.
Variété Taux de humidité % Porosité %
Concerto-001 13.23 5.91 15.96 7.01 Henley-020 11.05 3.36 16.20 4.48 Victoriana-005 12.90 4.40 15.75 6.89 Henley-007 16.22 5.19 15.46 6.88 Signora-010 12.33 4.39 15.62 7.31 Cellar-013 14.77 7.58 15.65 7.13 Calcule-017 13.08 5.10 15.10 5.98 Prestige-018 12.58 4.04 16.56 4.60 Sebastian-019 11.06 4.35 15.60 6.26
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Dans un premier temps, on a focalisé sur la phase de trempe, pour essayer d’étudier l’impact de diagramme de trempe sur la pénétration d’eau dans les grains, et donc sur la qualité du malt. Cela a été fait par une réalisation d’un suivi de taux d’humidité des grains d’orge (venant de quatre variétés d’orge différents avec des comportements très différents) pendant la trempe avec une mesure de qualité de malt final. Les résultats ont montrés que :
• Les quatre variétés montrent des comportements différents dans la première phase sous eau, les orges ayant le taux de protéines le plus élevé ont une prise d’eau plus importante que les autres orges. mais elle ne peut pas à elle seule expliquer les différences de comportement pendant le maltage
Il est indispensable d'intégrer dans ces modèles des indicateurs qui permettraient de prendre en compte cette variabilité dans le temps et d'expliquer les cinétiques de prise d'eau.
Ensuite, des facteurs structurels de qualité d’orge ont été ajoutés dans nos modèles pour tester les effets des autres qualités d’orge sur la qualité du malt final. La porosité, et la dureté deux qualités structurels de l’orge. En ajoutant les résultats de ces deux qualités obtenus dans nos modèles on peut conclure en deux points :
• On ne peut pas accepter les deux qualités d’orges comme facteurs indépendantes dans nos modèles car ils sont tous les deux fortement liée au taux d’humidité de grain.
• Les deux qualités n’apportent pas une amélioration significative sur nos modèles La réponse à la première perspective de la modélisation a été faite.
128 Références bibliographiques du chapitre 4
Ajib, B., 2008. Maltage à faible humidité. Master BAAN, ENSAIA.
Bicking F., Algorithmes génétiques Commande de processus biotechnologiques 1994
Brookes, P.A., Lovett D.A, Mac William I.C., 1976. The steeping of barley, a review of the metabolic consequences of water uptake and their practical implications, Journal of institute of brewery 82, 14-26.
Chandra, G.S., Proudlove, M.O., Baxter, E.D., 1999. The structure of barley endosperm – an important determinant of malt modification. Journal of the Science of Food and Agriculture 79, 37–46.
Debourge A ., cours de techniques brassicoles pour la formation approfondie .1998
De Francisco, A., Varriano-Marston, E., Hoscney, R.C., 1982. Hardness of pearl millet and grain sorghum. Cereal Chemistry; 59, 5-8.
Fliedel, G., Grenet, C., Gontard, N., Pons, B., Céréales en régions chaudes 3 : Dureté
caractéristiques physicochimiques et aptitude au décorticage des grains de sorgho. AUPELEF, EDs John Libbey Eurotext, Paris©1989, pp.187-201.
Gamlath, J., Aldred, G.P., Panozzo, I.F., 2008. Barley (1- 3; 1-4)-b-glucan and arabinoxylan content are related to kernel hardness and water uptake. Journal of Cereal Science 47, 365-371.
Gibbons G.C., the effects of oxygen, nitrogen and carbon dioxide steeping regimes on endosperm modification and embryo growth of germinating barley seeds, 1983.
Goupy J., La Méthode des plans d'expériences : optimisation du choix des essais et de l'interprétation des résultats, 1989.
Hariri A., Etude et modélisation de la trempe en malterie, Thèse de l'Institut National Polytechnique de Lorraine, 2003.
Hjmanger, Technology brewing and maltiry, 2004.
Igathinathane C., Chattopadhyay P.K., Moisture diffusion modelling of drying in parboiled paddy components. Part I: stratchy endosperm, J. Food Engineering, 41, 89-101. 1999
Kim, Kish -Meng, influence de la teneur en eau sur les activités enzymatiques participant a l’élaboration des dérives carbonylés au maltage ,1980.
Kettunen A .J.J ;Hamalanen K., Stnholm et K .Pietila , an modèle for the prediction of β-glucanes activity and β-glucanes concentration during mashing ,J.Food Ingineering ,29,185-200, 1996
Kreisz, S., ,Hartmann, K., , Zarknow, M., Thiele, F., Burberg, F., Back, W., A new statistical method to evaluate the malting performance of new barley varieties,2007.
Massebeuf, S., Optimisation multicritère de procédés discontinus d'homopolymèrisation et de copolymérisation en émulsion, 2000.
Miller B.S., Afework S., Hugues, J.W., Pomeranz, Y., 1981. Wheat hardness: time required to grind wheat with the Brabender automatic microhardness tester. Journal of food science 46, 1863-1865.
129 MODULAD, http://www-rocq.inria.fr/axis/modulad/numero-35/Tutoriel-confais-35/confais-35.pdf, 2006.
Molina-Cano, J.L., Sopena, A., Polo, J.P., Bergareche, C., Moralejo, M.A., Swanston, J.S., Glidewell, S.M., 2002. Relationships between barley hordeins and malting quality in a mutant of cv. triumph. II. Genetic and environmental effects on water uptake. Journal of Cereal Science 36, 39–50.
Morgan A G. Gilland A. A ., D.B Smith D.B., some barley grain and green malt properties and their influence on malt hot water extract ,1983.
Schimmerling P. , Sisson J.C., Zaidi A., Pratique des plans d'expériences 1998
Swanston, J.S., Newton, A.C., Hoad, S.P., Spoor, W., 2006. Variation across environments in patterns of water uptake and endosperm modification in barley varieties and variety mixtures. Journal of the Science of Food and Agriculture 86, 826–833.
Urion, E., Rouleau, H., Bière et malt. 1948.
Viennet R., Nouvelle technologie de planification expérimentale pour l’optimisation multicritères de procédés. Thèse de l'Institut National Polytechnique de Lorraine, Nancy, France, 1997.
Walter E. et Pronzato L., Identification des modèles paramétriques à partir des données expérimentales, Collection M. A. S. C, Ed Masson, Paris ,1994.
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Chapitre 5
Classification of barley according to
harvest year and species by using
mid-infrared spectroscopy and multivariate
analysis
Ce chapitre est présenté sous la forme d'un article. Les méthodes d'analyse de l'orge et du malt ont été décrites dans le chapitre 3. Les résultats de cet article ont été établir à partir des échantillons d'orge de 72 variétés d’orges, cultivées dans 16 lieux en France, récoltées en trois années.
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Classification of barley according to harvest
year and species by using mid-infrared
spectroscopy and multivariate analysis
AJIB B. 1, FOURNIER F. 1, BOIVIN P. 2, SCHMITT M. 2, FICK M. 1,
(1)
Université de Lorraine, LRGP, 2 Avenue de la Forêt de Haye, TSA 40602, 54518 Vandœuvre-lès-Nancy
(1)
CNRS UMR 7274, LRGP, 2 Avenue de la Forêt de Haye, TSA 40602, 54518 Vandœuvre-lès-Nancy
(2)
IFBM, 7, rue du Bois de la Champelle B.P. 267, 54512 Vandœuvre-lès-Nancy Cedex
Abstract:
In order to monitor malt quality in the malting industry, despite yearly variations in the barley quality, a large number of barley samples were analysed using conventional (Moisture, Protein and ß-glucan content) and FTIR Mid-InfraRed spectroscopy. The experimental dataset included barley from 3 harvest years, two barley species, 77 barley varieties, and two-row and six-row barley, from 16 cultivation sites. For each sample, the malt quality indices were also assessed according to EBC standards.
Principal Component Analysis (PCA) was carried out on mean-centred, normalized, and derivative spectra using 200 cm-1 width spectral bands. The most informative spectral bands were observed in the [800-1000] cm-1 and [1000-1200] cm-1 ranges. PCA revealed that barley harvested in 2010 and in 2011 had bands that were very close together, while 2009 harvest clearly displayed a difference in its quality. PCA analysis was performed on subsets defined according to harvest year or barley’s specie and row. PCA made it possible to distinguish two species and confirmed that 2-row winter barley quality was closer to 2-row spring barley quality than to 6-row winter barley. Results indicate that MIR spectrometry could be a very useful and rapid analytical tool to assess barley qualitative quality.
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1.
Introduction
Malt is the basic ingredient used in the production of beer. Malt provides most of the starch, enzymes and proteins which are necessary to give beer its distinctive flavour and alcohol. Malt quality, therefore, is essential.
Malt quality is the result of many factors, ranging from barley quality, to plant sanitation, to process controls. The world production of barley for breweries, which is in the neighbourhood of 25 MT, is intended to be transformed into malt and then into beer. However, the world beer consumption is in full expansion (2 billion hl) (IFBM) and maltsters are constantly searching for a better understanding and controlling of the malting process in order to get the best quality of malt possible.
The purpose of malting is to produce endogenous enzymes, breaking down the matrix surrounding the starch granules to prepare it for conversion before stopping this action for storage [1].
The malting process includes three main steps: steeping, germination and kilning.
(i) Steeping: to increase moisture from 15% to 42-44% by alternating periods of soaking in water and aeration.
(ii) Germination: growing rootlets and acrospires with cool and humid airflow.
(iii) Kilning to reduce the moisture to 4-5% by dry airflow. The combination of low moisture grain and high temperatures denatures many enzymes while keeping those necessary for later starch conversion in brewing .
During steeping, alternating periods of soaking and aeration will allow the grain to take in water with the purpose of hydrating the endosperm to a sufficient level to allow for radicle protrusion, i.e. germination from a physiological viewpoint. At the end of the steeping phase, all kernels should be chitted, which means germinated, and the entire endosperm should be hydrated.
During the germination phase, by blowing cold and humid air to maintain moisture at around 42%, the embryo will produce gibberellins that will engage in producing enzymes in the aleurone layers. Then, the enzymes, particularly the ß-glucanases, will need to move inside the endosperm to break down the cell wall. In order for the cell wall to break down properly, the endosperm should be sufficiently hydrated.
During kilning, which is a step that involves a huge amount of energy consumption, moisture is eliminated to get malt with 4 to 5 % moisture, a desired coloration and a specific aroma according to the type of beer.
To sum up, the malting process produces enzymes, some of which will be used by the brewer in the mashing step, such as amylases to convert starch into fermentable sugars, arabinoxylanase to hydrolyse malt arabinoxylans. Others, such as proteolytic enzymes and glucanases, will be involved in the malting process. So, malt will contain starch, proteins, modified proteins, and only small amounts of β-glucans because, during malting process, 90%
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of β-glucan is hydrolysed to obtain friable malt. Modifying structures, particularly the
endosperm cell wall, is crucial to get good malt.
Malt quality is essentially related to two kinds of factors: (i) the operatory conditions of the malting process: steeping (temperature, steeping diagram), germination (temperature, duration of germination), kilning (temperatures, duration of kilning), (ii) raw material qualities (moisture content, protein content, β-glucan content, and arabinoxylan content, germination
index). To control malt quality one needs to investigate and understand the impact of these factors on the malt.
As far as operating factors are concerned, malting control is a real challenge because of the various operating parameters during malting. These factors have already been widely investigated. Correlations between the steeping program (temperature, soaking and aeration periods) to the germination quality were found [3]. Studies have shown that higher water content (longer soaking) and longer germination influences the malt extract and decreases filterability [4]. The influence of the whole malting process on the lipid content in the malt was studied [5]. The steeping and germination were shown to provide optimal conditions for lipid degradation, whereas kilning proved to have no influence. But, for further investigation into control malt quality, it is not sufficient to study only the malting process. The biological structure of barley and the complex transformations it undergoes during the malting process must also be taken into account.
As far as the raw material qualities are concerned, barley is a complex system which is characterized by a wide range of physical and chemical indices. Maltsters are looking for barley that will germinate evenly and easily. Various studies and analyses were carried out on the factors affecting barley quality and, therefore, malt quality. The environmental and geographical effect on barley protein and arabinoxylan content respectively were investigated ??? . Several protein genes influencing malt quality could be identified [8]. Other studies focused on the effect of barley quality factors on malt quality [10] and confirmed that high-protein contents decrease available carbohydrates, thus having a negative influence on the brewing process. The influence of barley genotype on the apparent attenuation limit (AAL) was investigated [11]. Barley variety was found to have more important influence on protein degradation and cytolytic degradation than the technological factors [4]. Other studies have found that biological composition of the barley endosperm affected malt quality [12][13]. Steelier barley produced less root mass, more free amino nitrogen and soluble protein, and malt of a less friable structure.
In fact, there is no perfect barley for good malt quality. Indeed, the composition of the raw material is related to various factors such as the year of harvest, the barley species, the number of rows in the barley, the site of cultivation and the barley variety. In this study, we aim to show a correlation between the variation in the barley’s raw material and the differences in the malt it produces. A large dataset is required for such an analysis. Traditionally, a complete quality analysis includes measuring 4 to 7 physical and chemical parameters for raw barley. This analysis is usually long and expensive. Alternative methods
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were suggested to easily and rapidly collect information on barley and thus to facilitate the selection of new cultivars by maltsters. Due to the sensitivity of proton nuclear magnetic resonance, water mobility in hydrated barley was studied by ??? and it was found that the uptake of water and its distribution depends on the composition of the grain. NMR-imagery was coupled with morphological analysis (image acquisition, segmentation, external and internal image feature extraction, classification and interpretation) [15]. They found that morphological features may be successfully employed for preliminary varietal identification of barley kernels. Moreover, it was proved that the Rapid Visco Analyser coupled with multivariate analysis was a very useful analytical tool to study the starch pasting process and were able to predict some important quality factors of barley [16].
Conventional methods involve time consuming, laborious and costly procedures, including physical and chemical analysis or microscopy analysis. A rapid and inexpensive method based on spectroscopy for the certification process would be ideal for characterising barley quality before the malting process.
Infrared spectroscopy is an easy and rapid method which requires little or no sample preparation. Both Near InfraRed (NIR), Mid-InfraRed (MIR) IR methods are based on vibrational spectroscopy which offers the possibility for fast and flexible analysis of a large number of samples. These methods have already provided analytical approaches for the agricultural and food industry as alternatives to traditional analytical methods. In addition, they can be used on samples of almost any morphology.
NIR spectroscopy is defined by a range of electromagnetic radiation in the region of about 780-2500 nm (12821-4000 cm-1). The absorption of radiation by the molecules in this region is primarily caused by overtones and combinations of fundamental vibration bands that appear in the MIR region. NIR was already well established in evaluating grain composition. It was concluded that seed phenotyping of barley by NIR and chemometric was a new, reliable tool for characterising the effects of mutant genes in selecting barley for quality in plant breeding ???. Models for quickly and reliably predicting moisture and total lipid content in maize by means of near-infrared (NIR) spectroscopy were developed [18]. Furthermore, NIR was proved to have potential for predicting the quality of the whole malt grain (prediction of moisture and nitrogen content, of malt extract and protein content, of the progress of the malting process.
Mid-IR spectroscopy is a physico-chemical method based on measuring the vibration of a molecule excited by IR radiation at a specific wavelength range. The mid-IR (400-4000 cm-1) is the most commonly used region for analysis as all molecules possesses characteristic absorbance frequencies and primary molecular vibrations in this range [22]. As IR radiation passes through a sample, specific wavelengths are absorbed, causing the chemical bonds in the material to undergo vibrations that make them stretch, contract, and bend. Functional groups present in a molecule tend to absorb IR radiation in the same wave number range regardless of other structures in the molecule, and spectral peaks are derived from the absorption of bond vibrational energy changes in the IR region [23]. The correlation between
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IR band positions and chemical structures in the molecule can thus be exploited. Mid-IR spectrometry was widely used in the spirit drinks domain. The FTIR is proved to be a useful tool in the quality control of alcoholic beverages [24]. FTIR spectroscopy is routinely used in many oenological and wine cellars [25]. The mid-IR spectroscopy was also shown to be a valuable analytical tool for measuring beer quality [26]. It has also been applied in food biotechnology: the Mid-IR is used in the characterisation of edible oil and fat [27] [28] . The feasibility of the Mid-IR technique as a rapid analyser for dairy products was demonstrated [29]. FTIR was also used to study well-defined components such as plant cell wall polysaccharides [30] [31]. FTIR could usefully complement the NIR in monitoring grain transformation during the malting process.
Predicting compositions in spectra from large numbers of samples is very difficult because of the numerous interactions between the signals of the chemical components in the samples and by random differences in the spectral data.The goal of using IR spectra to predict chemical composition in complex samples has been made possible by applying multivariate approaches to data analysis. The simplest and possibly the most commonly used multivariate approach is PCA. This was first described in theoretical terms by Karl Pearson in 1901 [32] and later put into practical application by Hotelling in 1933 [33]. With PCA, the variance in the spectra data at hundreds of wave numbers is condensed into a much smaller number of new principal component-linear combinations of the original variables 00. These few principal components explain most of the variance in the original data, and facilitate an efficient interpretation of the sample spectral prosperities by qualitative analyses such as discrimination or classification 00. Due to there being fewer variables, the data can be more easily explored graphically or statistically for correlations with experimental, environmental and sample effects. PCA works best with highly correlated data and spectral data is highly correlated. Any single data point in a spectrum is highly influenced by the value of its immediate neighbours. PCA is of great value in helping to understand differences between samples in geographic location, growth year and species. Usually, only the first two or three PCs are considered. PCA thus presents the influence of the first principal components on a 2D or 3D graph [38] [39]. The PCA is widely combined to IR spectroscopy. It was concluded that NIR classification with the algorithmPCA is a strong tool for obtaining a preliminaryoverview of associations between variations in the malt quality and the barley samples 0. A comprehensive overview of an MIR application coupled with PCA analysed to determine the quality of several ingredients and food products was established by [41].
The aim of the present work is to analyse and classify a large set (394) of spring and winter barley, growing in 16 different locations, of 77 varieties, using Mid-IR spectroscopy in combination with multivariate analysis (PCA), according to year of harvest and species. Our goal is to demonstrate the utility of the Mid-IR as a rapid, inexpensive, and efficient
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2.
Material and methods
2.1 Sample collection
A total of 394 Barley samples, provided by the French Institute of Brewing and Malting (IFBM), were studied. The samples were harvested in 2009, 2010 and 2011. They were composed of 256 spring barley and 138 winter barley, with 77 different varieties, grown in 16 different sites in France. Figure 33 depicts the locations of the harvested barley. The