Near infrared (NIR) spectroscopy, coupled with multivariate data analysis, is proposed as a rapid and effective analytical method for evaluating the quality of pernambuco (Caesalpina echinata Lam.) wood for making bows for stringed instruments. For this purpose, a set of 30 pernambuco sticks were ranked based on their suitability for making high-quality bows and they were assigned to one of the following categories: 0=very poor to poor, 1 = good to very good and 2 = excellent. Considering the low number of samples in the poor category, the classification study focused on the discrimination between samples of the two higher quality groups. Linear discriminant analysis (LDA) was applied to the NIR data as a classification technique and in order to ensure a more appropriate ratio between the number of objects (samples) and number of variables (absorbance at different wavenumbers), LDA was preceded by feature selection. Based on LDA, 100% of the samples were correctly classified and 92.6% of the samples were correctly predicted by the cross-validation procedure.
BrownW., Timbers of the World. 2, South America.TRADA Technology Ltd, High Wycombe, UK (1978).
2.
BucurV., Acoustics of Wood, 2nd edn.Springer, Berlin, Germany (2006).
3.
RymerR., “Saving the music tree”, Smithsonian35, 52 (2004.).
4.
WegstU.G.K., “Wood for sound”, Am. J. Bot.93, 1439 (2006). doi: 10.3732/ajb.93.10.1439
5.
MatsunagaM.SugiyamaM.MinatoK. and NorimotoM., “Physical and mechanical properties required for violin bow materials”, Holzforschung50, 511 (1996). doi: 10.1515/hfsg.1996.50.6.511
6.
AlvesE.S.LonguiE.L. and AmanoE., “Pernambuco wood (Caesalpinia echinata) used in the manufacture of bows for string instruments”, IAWA J.29, 323 (2008).
7.
SchimleckL.R.EspeyC.MoraC.EvansR.TaylorA. and MunizG., “Characterization of the wood quality of pernambuco (Caesalpinia echinata Lam.) by measurements of density, extractives content, microfibril angle, stiffness, color and NIR spectroscopy”, Holzforschung63, 457 (2009). doi: 10.1515/HF.2009.082
8.
BertranE.BlancoM.CoelloJ.IturriagaH.MaspochS. and MontoliuI., “Near infrared spectrometry and pattern recognition as screening methods for the authentication of virgin olive oils of very close geographical origins”, J. Near Infrared Spectrosc.8, 45 (2000). doi: 10.1255/jnirs.263
9.
CarazzoloA.ConteL.PavanO. and TonuttiP., “Quality of extra virgin oil from Veneto olives [Italian]”, L'Informatore Agrario4, 115 (1999).
10.
DowneyG.McIntyreP. and DaviesA.N., “Geographic classification of extra virgin olive oils from the eastern Mediterranean by chemometrics analysis of visible and near infrared spectroscopic data”, Appl. Spectrosc.57, 158 (2003). doi: 10.1366/000370203321535060
11.
IndahlU.G.SahniN.S.KirkhusB. and NæsT., “Multivariate strategies for classification based on NIR spectra with application to mayonnaise”, Chemometr Intell. Lab. Syst.49, 19 (1999). doi: 10.1016/S01697439(99)000234
12.
SinelliN.CerretaniL.Di EgidioV.BendiniA. and CasiraghiE., “Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity”, Food Res. Int.43, 369 (2010). doi: 10.1016/j.foodres.2009.10.008
13.
BrunnerM.EugsterR.TrenkaE. and StrotzBergamin L., “FTNIR spectroscopy and wood identification”, Holzforschung50, 130 (1996). doi: 10.1515/hfsg.1996.50.2.130
14.
TsuchikawaS.InoueK.NomaJ. and HayashiK., “Application of near infrared spectroscopy to wood discrimination”, J. Wood Sci.49, 29 (2003). doi: 10.1007/s100860300005
15.
CasaleM.SinelliN.OliveriP.Di EgidioV. and LanteriS., “Chemometrics strategies for feature selection and data compression applied to NIR and MIR spectra of extra virgin olive oils for cultivar identification”, Talanta80, 1832 (2010). doi: 10.1016/j.talanta.2009.10.030
16.
CasaleM.ZuninP.CosulichM.E.PistarinoE.PeregoP. and LanteriS., “Characterisation of table olive cultivar by NIR spectroscopy”, Food Chem.122, 1261 (2010). doi: 10.1016/j.foodchem.2010.03.080
17.
BarnesR.J.DhanoaM.S. and ListerS.J., “Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra”, Appl. Spectrosc.43, 772 (1989). doi: 10.1366/0003702894202201
18.
SavitzkyA. and GolayM.J.E., “Smoothing and differentiation of data by simplified least squares procedures”, Anal Chem.36, 1627 (1964). doi: 10.1021/ac60214a047
19.
VandeginsteB.G.M.MassartD.L.BuydensL.M.C.De JongS.LewiP.L. and VerbekeSmeyers J., “Principal component analysis” in Handbook of Chemometrics and Qualimetrics: Part B, Ed by VandeginsteB.G.M. and RutanS.C.Elsevier, Amsterdam, The Netherlands, p. 88 (1998).
20.
VandeginsteB.G.M.MassartD.L.BuydensL.M.C.De JongS.LewiP.L. and VerbekeSmeyers J., “Canonical variates and linear discriminate analysis” in Handbook of Chemometrics and Qualimetrics: Part B, Ed by VandeginsteB.G.M. and RutanS.C.Elsevier, Amsterdam, The Netherlands, p. 213 (1998).
21.
FormaM.LanteriS.CasaleM. and OliverosCerrato M.C., “Stepwise orthogonalization of predictors in classification and regression techniques: An “old” technique revisited”, Chemometr Intell. Lab. Syst.87, 252 (2007). doi: 10.1016/j.chemolab.2007.03.003
22.
KowalskiB.R. and BenderC.F., “An orthogonal feature selection method”, J. Patt. Recogn.8, 1 (1976). doi: 10.1016/0031 3203(76)900236
23.
FormaM.LanteriS.ArmaninoC.CasolinoM.C.CasaleM. and OliveriP., “V PARVUS, an extendable package of programs for explorative data analysis, classification and regression analysis”, Dip. Chimica e Tecnologie Farmaceutiche ed Alimentari, University of Genova (2010). Freely available at http://www.parvus.unige.it
24.
OsborneB.G.FearnT. and HindleP.H., Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, 2nd edn. Longman Scientific and Technical, Harlow, UK (1993).
25.
ShenkJ.S.WorkmanJ.J. and WesterhausM.O., “Application of NIR spectroscopy to agricultural products”, in Handbook of Near Infrared Analysis, Ed by BurnsD.A. and CiurczakE.W.Marcel Dekker Inc., New York, USA, p. 383 (1992).
26.
SchwanningerM.StefkeB. and HinterstoisserB., “Qualitative assessment of acetylated wood with infrared spectroscopic methods”, J. Near Infrared Spectrosc, (submitted).
27.
TsuchikawaS. and SieslerH.W., “Near infrared spectroscopic monitoring of the diffusion process of deuterium labeled molecules in wood. Part I: Softwood”, Appl. Spectrosc.57, 667 (2003). doi: 10.1366/000370203322005364
28.
MichellA.J. and SchimleckL.R., “NIR spectroscopy of woods from Eucalyptus globulus”, Appita J.49, 23 (1996).
