Interest of near infrared spectrometry for assessment of cooperage oak wood quality

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Interest of near infrared spectrometry for assessment of

cooperage oak wood quality

Thomas Giordanengo, Jean-Paul Charpentier, Julien Michel, Sylvie Roussel,

Jean-Michel Roger, Gilles Chaix, Michael Jourdes, Pierre Louis Teissedre,

Nicolas Mourey

To cite this version:

Thomas Giordanengo, Jean-Paul Charpentier, Julien Michel, Sylvie Roussel, Jean-Michel Roger, et al..

Interest of near infrared spectrometry for assessment of cooperage oak wood quality. 16. International

Conference on Near Infrared Spectroscopy. NIR 2013, Institut National de Recherche en Sciences

et Technologies pour l’Environnement et l’Agriculture (IRSTEA). Montpellier, FRA., Jun 2013, La

Grande Motte, France. �hal-02747418�

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NIR 2013 is held under the auspices of the International Council for Near Infrared Spectroscopy

Proceedings

NIR 2013 - 16th International Conference on Near Infrared Spectroscopy

2 - 7 June 2013, la Grande-Motte, France

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Editorial

NIR 2013 was held from 2

nd

to 7th June, 2013 in La Grande Motte, France. It provided the

NIR community with tremendous opportunities for scientific and social gatherings, with

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well as the gala dinner, all of them imbued with a very relaxed atmosphere. More than 380

abstracts had been selected by the scientific committee, giving rise to more than 100 oral

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presentation). In addition, an internationally-renowned guest speaker (a total of six) opened the

methodology session on each morning of the conference. Application sessions provided the

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attention was given to the most original applications including soil analysis, biomedical use,

unusual applications in ecology, archeology or industry, etc.

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We hope you will enjoy reading these proceedings as much as we did!

Let’s meet again in Brazil for NIR2015 …

Véronique Bellon Maurel, NIR2013 Chair, Editor

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Proc

eeding

s

A1 – Agriculture, Environment

Biomass, Wood, Plants, Forages

H. Andrianoelisoa. Near Infrared (NIR) spectroscopy for determination of essential oil chemotypes from

Ravensara aromatic. P.264

D. Andueza. Ability of local and global fresh permanent grassland calibrations to predict the chemical composition

and nutritive value of permanent grassland hays using NIRS. P.269

G.Bambara. Characterization of woody roots located in dykes by near-infrared spectroscopy and chemometrics. P.274

T. Giordanengo. Interest of near infrared spectrometry for assessment of cooperage oak wood quality. P.280

H.Itoh. Regression Models to Estimate Nitrate Ion Concentration in Vegetable Leaves. P.285

A. Kapitan-Gnimdu. Resin and rubber determination in Parthenium argentatum biomass using near infrared spectroscopy. P.291

D. R. Sabatier. Can near infrared reflectance (NIR) spectroscopy be used to predict the resistance of sugarcane

to pests and diseases in a generic analysis of the plant surface? P.298

A. Sandak. Utilization of FT-NIR for proper biomass conversion. P.303

A. Sandak. Assessment of thermal behavior of wood with FT-NIR. P.311

J. Sandak. FT-NIR evaluation of chemical changes to wood surfaces exposed to natural and artificial weathering. P.318

J. Sandak. Estimation of mechanical stresses of wood with FT-NIR. P.325

V. Segura. A comparison of various infrared (IR) spectroscopy techniques for calibrating poplar wood chemical

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NIR2013 Proceedings, 2-7 June, La Grande-Motte, France. A1 – Agriculture and Environment

B e l l o n - M a u r e l V . , W i l l i a m s P . , D o w n e y G . , E d s 2 8 0

Interest of near infrared spectrometry for

assessment of cooperage oak wood

quality

.

T. Giordanengo

a

, J P. Charpentier

b

, J. Michel

a,c

, Sylvie Roussel

d

, J M.

Roger

e

, G. Chaix

f

, M. Jourdes

c

, P L. Teissedre

c

and N. Mourey

a

R&D Tonnellerie Radoux – Pronektar, 10 avenue Faidherbe, 17500 Jonzac, France

b

INRA GénoBois, Centre de recherche d’Orléans, 2163 avenue de la Pomme de Pin, CS 40001 ARDON, 45075 Orléans Cedex 2, France

c

Unité de recherche Œnologie EA 4577, USC 1366 INRA, Institut des Sciences de la Vigne et du Vin, Université Bordeaux Segalen, 210, chemin de Leysotte, 33882 Villenave d’Ornon Cedex, France

d

Ondalys, Z.A. Les Baronnes - 385 Avenue des Baronnes, 34 730 Prades le Lez, France

e

Research Group ITAP Irstea – SupAgro, BP5095, 34196 Montpellier cedex 1, France

f

CIRAD – Département BIOS, UMR AGAP, ESALQ USP, Piracicaba-SP, Brasil

Corresponding author: t-giordanengo@radoux.fr

Introduction

Oak wood selection is a key criterion of barrel quality. Coopers choose the French oak trees according to their geographical origin and to an anatomical property: the “grain”. However, the results of wine aging in barrel still show important variability even if these parameters are fixed. Oak chemical composition can explain this lack of reproducibility. Heartwood compounds are extracted when oak is in contact with a hydroalcoholic solution. Oak extractives represent several percent of the wood mass. Different volatile molecules have a direct impact on wine aroma. Polyphenols are non-volatile compounds and account for the major quantity of these extractives. The main components of oak polyphenols are hydrolysable tannins, the ellagitannins (Mayer et al., 1967). Oak polyphenols have an important influence on wine aging in the barrel. They contribute to the taste properties of wine (Glabasnia and Hofmann, 2006), are involved in the oxidation reactions (Vivas and Glories, 1996), participate in wine compound polymerisation (Quideau et al., 2005) and influence the wine colour (Chassaing et al., 2010).

However, oak woods exhibit a wide variability in polyphenol content. Factors such as botanical species, geographical origin, ecological growth conditions, forest management, tree genetics (Snakkers et al., 2000; Doussot et al., 2002; Prida et al., 2006) or position of wood inside the log (Mosedale et al., 1996) all impact on oak polyphenol composition . Thus, the management of wood selection is very important for the control of barrel quality.

Tonnellerie Radoux developed new tools based on near infrared spectrometry in order to determine the chemical composition of oak wood. Since 2009, an OakScan® sensor monitors the production on-line and is used to sort barrel staves according to their polyphenol content (Giordanengo et al., 2009). Near infrared calibrations of oak polyphenol contents were first studied in the laboratory. Then, a sensor was developed for industrial conditions. A large series of wine trials was also undertaken to control sensor efficiency and to qualify newly-available oak selections.

Material and methods

NIR-sensor development

In 2005, Tonnellerie Radoux commenced partnerships with INRA Orléans and Ondalys to work on the project. CIRAD and CEMAGREF also participate in the study.

Four hundred staves from thirty-three different French geographical origins and with different grain were sampled to compose a calibration set (Figure 1). The objective was to maximiser variability of material. Both sessile and pedunculate oak species were selected. The sample collection was split for reference analyses and the NIR measurements.

Figure 1. Distribution of the geographical origins of the wood samples. The Laboratory of Biochemical Analyses from INRA Orléans worked on the reference analyses. Each wood sample was ground to 40μm powder and homogenised. An acetone-water (80 : 20) extraction was carried out using an ultrasonic bath under cold conditions (Boizot and Charpentier, 2006). The raw extract was dried with vacuum

Meuse 11% Moselle 9% Vosges 11% Eure 4% Aisne 4% Vienne 5% Dordogne 6% Oise 3% Meurthe-et-Moselle 3% Marne 3% Eure-et-Loire 10% Loir-et-Cher 6% Bas-Rhin 4% Allier 5% Loire - Atlantique 4% Haute - Saone 4% Sarthe 10%

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evaporation, diluted with a methanol-water (20 : 80) solution and used for the chemical measurements. Two extractions were prepared for each sample. The mean value of the two measurements was used as the chemical reference. A third extraction was made for forty-four samples because of the difference between the first measurement results. The mean value of the three measurements was used as chemical reference for these samples.

Polyphenol content of the extracts was analysed by four methods: total extractive content (weight method, unit: percentage of dried wood), optical density at 280 nm (unit: UV absorbance), Folin-Ciocalteu colorimetric analysis (unit: 10-3 gram equivalent gallic acid / gram of dry wood) (Boizot and Charpentier, 2006) and ellagitannins content measured by HPLC (unit: 10-3 gram equivalent pyrogallol / gram of dry wood) (Zahri et al, 2007). Samples were also scanned by near infrared spectrometry. The work was carried out under laboratory conditions with different NIR spectrometers. Several chemometric methods were assessed in order to study oak polyphenol calibrations.

Industrial sensors

The first industrial application was developed on the barrel stave production and named OakScan®.

A near infrared sensor was integrated on the production line. Calibrations were developed on this sensor with the sample set. A new quantification unit was created on the basis of these calibrations: the

Index of Polyphenols (IP). The near infrared measurement is

automated. Software was developed to record the spectra (Figure 2), to calculate the near infrared estimations and to programme a printer which labels the barrel staves

(Figure 3).

Figure 2. Front page of OakScan® software

Figure 3. Pictures of Tonnellerie Radoux production site (left : printed barrel stave after NIR sorting ; right : production site)

The robustness of the NIR measurement was studied. Some conditions of production influence the near infrared absorbance because of variation in external parameters such as temperature or

wood moisture content. Orthogonalisation methods were used to limit the influence of these interfering parameters (Roger et al., 2003, Giordanengo et al., 2008). An observation period was held to observe measurements and to learn about the natural distribution of oak polyphenols on a large scale. Further calibration samples were added at this time. The information stored during this observation stage, together with the sensory results of wine trials, were combined to define three classes of oak wood according to the Index of Polyphenols. Since August 2009, each stave is scanned and printed with a label which corresponds to the classes of Index of Polyphenols. Barrel staves are sorted and assembled according to the chemical composition of oak. Other industrial sensors were created to control green wood at the sawmill in 2011, and to sort tank staves1 in 2012 (Giordanengo et

al., 2012).

Wine aging studies

Trials to study the impact of oak polyphenols on wine content started with the 2006 vintage. For the first trial, laboratory methods were employed to sort the staves according to their polyphenols content. The use of the sensor for the wine trials started with the 2008 vintage.

During the 2009 vintage, a large wine trial study was carried out with barrels produced using the near infrared selection criterion. These trials were performed in different French geographical regions (Bordeaux, Burgundy, Vallée du Rhône, Languedoc Roussillon) and different countries (Spain, Chile, USA - California). Various grape varieties and blends were studied (Merlot, Cabernet Sauvignon, Pinot Noir, Syrah, Sauvignon Blanc), and different barrel heat treatments were tested. Specific wine trials were also studied involving oak products (chips and tank staves). In order to study further the interactions of oak polyphenols with wine, Tonnellerie Radoux began a partnership with the Institut des Sciences de la Vigne et du Vin from Bordeaux II University. A CIFRE thesis on the Classification and

influences of oak wood polyphenols on wine sensory quality was

conducted during the period 2009-2012 (Michel, 2012). The efficiency of the NIR sensor and the specificity of the new oak selections were studied.

Results and discussion

NIR-sensor development

The laboratory study showed efficient near infrared calibrations based on PLS regression to predict optical density at 280nm, Folin-Ciocalteu colorimetric dosage and for ellagitannins measured by high performance liquid chromatography. Figure 4 represents near infrared assessment versus the reference analyses, obtained using leave-one-out cross-validation on the full calibration set. The specifications of these calibrations are given in Table 1.

1_{Specific products : toasted oak staves used directly inside the tanks}

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Figure 4. Near infrared predictions versus reference values estimated

with a PLS calibration, by leave-one-out cross-validation, for the four reference analyses. NIR data registered on Bruker Vector 22/NI spectrometer.

Table 1. PLS calibration results

Near infrared calibrations for optical density at 280nm, for total polyphenols content assessed by Folin-Ciocalteu method and for ellagitannins content are sufficiently accurate to sort wood into a few classes based on polyphenol content (Table 1).

The results of this study confirmed the interest of the technology for the rapid and non-destructive measurement of oak polyphenols and led to the development of industrial sensors.

Industrial sensors

The OakScan® measurement introduces a new criterion, Index of

Polyphenols, for each barrel stave. Three classes of staves are sorted

and labelled as low tannin, median tannin and high tannin staves. Each production day, the process measures and sorts from 5000 to 10000 barrel staves.

Figure 5 presents a control chart of one morning of measurement. The chart illustrates the variability of polyphenol contents within the same range of grain and geographical origins.

The staves are assembled according to their polyphenol contents to produce a barrel. The tannin content of barrels is more homogeneous and more reproducible.

This method also provides the opportunity to generate two classes of barrels, low tannin content and high tannin content barrels, which are new technical products available for winemakers.

The same kind of methods were used to develop two other industrial sensors (Giordanengo et al., 2012). The near infrared measurement of polyphenol content in green wood is employed to classify raw staves at an early stage. It is also used to sort the raw oak material which is employed for the production of oak chips for oenology. The most recent development is a tool to sort tank staves according to their polyphenol content.

Wine aging studies

The results of the different wine trials show that the Index of

Polyphenols of wood is correlated with the concentration of oak

ellagitannins found in wine (Michel et al., 2011; Michel, 2012).

Figure 5. An example of an OakScan® control chart (two types of barrel staves with different grain sizes : MF27095 / MG27105)

Figure 6. Pictures of labelled barrel staves after production (left) and in

an assembled barrel (right)

1

mean standard

deviation SEL rank SECV R²CV biasCV SECV / SEL

Ext 15.4 4.9 2.1 6 3.3 0.56 -0.004 1.6

OD280 0.64 0.24 0.05 5 0.09 0.85 -0.0008 1.9

Folin 122 24 6.9 4 10.7 0.86 -0.2 1.5

Ellagit 223 116 21 10 49 0.82 -0.08 2.3

Reference analyses

Reference chemical analyses Near infrared calibrations

Ext : total extractive content ; OD 280 : optic density at 280 nm ; Folin : Folin-Ciocalteu colorimetric analysis ; Ellagit : ellagitannins content measured by HPLC ; SEL : standard error of laboratory ; rank : number of latent variables used in PLS regression model ; SECV : standard error of cross-validation ; R²cv : determination

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Figures 7 and 8 present the ellagitannins content in wine after aging in oak barrels with different IP, for Merlot and Sauvignon Blanc grape varietals. The extraction of oak polyphenols is demonstrated to be linked to the Index of Polyphenols.

The sensory perception of oak in wine is different for three classes of wood sorted according to IP value. Mouth-feel differentiation was expected: an increase in astringency, bitterness and wine structure with oak polyphenol concentration was experienced.

Furthermore, the types of wood aromatics change with the Index

of Polyphenols. Sensory analyses revealed that, for the same toasting

level, barrels with high polyphenol contents give more “toasted” and “smoked” aromas to the wine. An example is shown in Figure 9 which represents the results of sensory analyses on a Merlot-Cabernet wine aged in contact with tank staves with three different IP values. The content of furanic aldehydes, guaiacol and syringol, compounds involved in aromatic perceptions, is correlated with the Index of

Polyphenols of the wood (Giordanengo et al., 2012; Michel, 2012).

These studies on wine developed knowledge on the adaptation of chemical composition of wood to the types of wine aging. The results also lead to the development of specific heat treatments adapted to oak polyphenol content in order to enhance the potential of wood.

Conclusion

Sensors were adapted to measure the polyphenol contents of oak wood.

Near infrared spectrometry is a powerful technology which gives coopers a new tool to improve the control of oak chemical composition. As an R&D toolbox, near infrared analysis helps to manage the variability factors and is effective in the study of wine aging, to work on the production process or to develop new products.

For production, the development of a new selection criterion, based on these sensors, enables production of more homogeneous barrels, tank staves and chips. The reproducibility of the impact of oak on wine aging is improved. Moreover, new selections of oak are available. Winemakers have the opportunity to use the different selections to engender specific wine aging.

Figure 7. Ellagitannin content quantified in a Merlot

wine for three different oak IP measured after 6, 12 and 18 months of aging in oak barrels (Michel, 2012)

Figure 8. Ellagitannin content quantified in a

Sauvignon Blanc wine for three different oak IP measured after 6 months of aging in oak barrels (Michel, 2012) 1 1 1,5 2 2,5 3 3,5 4 4,5 5 intensité fruit intensité boisé (P***/J*) vanille coco épices (P*/J*) grillé (P**/J*) fumé (P***/J**) fruité bouche rondeur amplitude (P*) structuration (P) astringence (P*/J***) amertume (J*) fondu persistance (J***)

IPs_25 IPs_43 IPs_61

Figure 9. Spider graph of sensory analyses performed on a

Merlot-Cabernet wine aged in contact with tank staves of three different Index of Polyphenols. Same heat treatment for the three products (Pronektar Medium +).

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