Near infrared (NIR) spectroscopic analysis of sliced and packaged dry fermented sausages was standardised in order to identify the main sources of error during the spectrum acquisition process. The aim of the study was to ascertain how many of the apparent critical factors (ACFs) were really significant key factors for spectrum acquisition. Using a 27-4III, Plackett–Burman experimental design, and enhancing resolution via a fold-over such as 27-3IV, seven ACFs were investigated to determine their effect on root mean square (RMS) values obtained using 15 combinations of spectral range and mathematical pre-treatment. The results showed that for the on-line control of products of this type, the spectrum acquisition process was not affected by the ACFs studied when using the whole spectral range of the instrument (400–2500 nm), with the first derivative and either of the scatter corrections examined here, since in these conditions control of factor variability proved unnecessary.
OsborneB.G.FearnT., Near infrared spectroscopy in food analysis.John Wiley & Sons, Chichester, UK (1986).
2.
BlancoM.ValdésD., “Influence of temperature on the predictive ability of near infrared spectroscopy models”, J. Near Infrared Spectrosc.12, 121–126 (2004).
3.
HarginK.D., “Authenticity issues in meat and meat products”, Meat Sci.43, 277–289 (1996). doi: 10.1016/0309-1740(96)00072-1
4.
DingH.B.XuR.J., “Differentiation of beef and kangaroo meat by visible/near-infrared reflectance spectroscopy”, J. Food Sci.64, 814–817 (1999). doi: 10.1111/j.1365-2621.1999.tb15918.x
5.
MurrayI.AucottL.S.PikeI.H., “Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat bone meal”, J. Near Infrared Spectrosc.9, 297–311 (2001).
6.
CozzolinoD.MartinsV.MurrayI., “Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of discriminating between pasture and corn silage feeding regimes”, J. Near Infrared Spectrosc.10, 187–193 (2002).
7.
AlomarD.GalloC.CastañedaM.FuchslocherR., “Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS), Meat Sci.63, 15–24 (2003). doi: 10.1016/S0309-1740(02)00101-8
8.
CozzolinoD.MurrayI., “Identification of animal meat muscles by visible and near infrared reflectance spectroscopy”, Lebensm.-Wiss. U.-Technol.37, 447–452 (2004).
9.
GeesinkG.H.SchreutelkampF.H.FrankhuizenR.VedderH.W.FaberN.M.KranenR.W.GerritzenM.A., “Prediction of pork quality attributes from near infrared reflectance spectra”, Meat Sci.65, 661–668 (2003). doi: 10.1016/S0309-1740(02)00269-3
10.
LeroyB.LambotteS.DotreppeO.LecocqH.IstasseL.ClinquartA., “Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra”, Meat Sci.66, 45–54 (2003). doi: 10.1016/S0309-1740(03)00002-0
11.
LiuY.LyonB.G.WindhamW.R.RealiniC.E.PringleT.D.D.DuckettS., “Prediction of colour, texture and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study”, Meat Sci.65, 1107–1115 (2003). doi: 10.1016/S0309-1740(02)00328-5
12.
García-ReyR.M.García-OlmoJ.De PedroE.Quiles-ZafraR.de CastroM.D. Luque, “Prediction of texture and colour of dry-cured ham by visible and near infrared spectroscopy using a fiber optic probe”, Meat Sci.70, 357–364 (2005). doi: 10.1016/j.meatsci.2005.02.001
13.
TogersenG.IsakssonT.NilsenB.N.BakkerE.A.HildrumK.I., “On-line NIR analysis of fat, water and protein in industrial scale ground meat batches”, Meat Sci.51, 97–102 (1999). doi: 10.1016/S0309-1740(98)00106-5
14.
González-MartínI.González-PérezC.Hernández-MéndezJ.Alvarez-GarcíaN.HernándezJ.L., “On-line non-destructive determination of proteins and infiltrated fat in Iberian pork loin by near infrared spectrometry with a remote reflectance fibre optic probe”, Anal. Chim. Acta453, 281–288 (2002). doi: 10.1016/S0003-2670(01)01454-4
15.
AndersonN.M.WalkerP.N., “Measuring fat content of ground beef stream using on-line visible/NIR spectroscopy”, Trans. ASAE46, 117–124 (2003).
16.
TogersenG.ArnesenJ.F.NilsenB.N.HildrumK.I., “On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy”, Meat Sci.63, 515–523 (2003). doi: 10.1016/S0309-1740(02)00113-4
17.
WilliamsP.C., “Sampling, sample preparation, and sample selection”, in Handbook of near infrared analysis, 2nd Edition, Ed by BurnsD.A.CiurczakE.W., revised and expanded. Marcel Dekker, New York, USA, pp. 307–350 (2001).
18.
KangJ.O.ParkJ.Y.ChoyY.H., “Color determination of beef rib eye using near infrared spectroscopy”, Asian Australas. J. Anim. Sci.14, 263–267 (2001).
19.
LiuY.ChenY.R., “Two-dimensional visible/near-infrared correlation spectroscopy study of thawing behaviour of frozen chicken meats without exposure to air”, Meat Sci.57, 299–310 (2001). doi: 10.1016/S0309-1740(00)00106-6
20.
CozzolinoD.MurrayI., “Effect of sample presentation and animal muscle species on the analysis of meat by near infrared reflectance spectroscopy”, J. Near Infrared Spectrosc.10, 37–44 (2002).
21.
XiaJ.J.BergE.P.LeeJ.W.YaoG., “Characterizing beef muscles with optical scattering and absorption coefficients in VIS-NIR region”, Meat Sci.75, 78–83 (2007). doi: 10.1016/j.meatsci.2006.07.002
22.
JoglekarA.M.MayA.T., “Product excellence through design of experiments”, Cereal World32, 857–868 (1987).
23.
BoxG., “Good quality costs less? How come?”, Improving almost anything. Ideas and essays.Wiley-Interscience, John Wiley & Sons Inc., Hoboken, NJ, USA, pp. 4–19 (2006).
24.
GatiusF.FerranJ.PuyJ., “Experimental design procedures in the calibration of quality parameters of alfalfa pellets from near infrared spectra”, J. Near Infrared Spectrosc.12, 167–176 (2004).
25.
Saiz-AbajoM.J.González-SaizJ.M.PizarroC., “Temperature and path length optimisation for near infrared measurements of liquid samples: An alternative approach”, J. Near Infrared Spectrosc.15, 71–80 (2007). doi: 10.1255/jnirs.715
26.
BoxG., “What can you find out from sixteen experimental runs?”, in Improving almost anything. Ideas and essays.John Wiley & Sons, Inc.Hoboken, NJ, USA, pp. 57–72 (2006).
27.
MyersR.H.MontgomeryD.C., Response surface methodology. Process and product optimization using designed experiments.John Wiley & Sons Inc., New York, USA (1995).
28.
WilliamsP.C.NorrisK.H., “Variables affecting near-infrared spectroscopy analysis”, in Near-infrared technology in the agricultural and food industries, 2nd Edn, Ed by WilliamsP.C.NorrisK.H., American Association of Cereal Chemists, St Paul, Minnesota, USA, pp. 171–185 (2001).
29.
PlackettR.L.BurmanJ.P., “The design of optimum multifactorial experiments”, Biometrika33, 305–325 (1946). doi: 10.1093/biomet/33.4.305
BeebeK.R.PellR.J.SeasholtzM.B., Chemometrics. A practical guide.John Wiley & Sons Inc., New York, USA, pp. 9–25 (1998).
32.
RobisonG.K., “Plan a single experiment”, in Practical strategies for experimenting.John Wiley & Sons, Chichester, UK, pp. 113–140 (2000).
33.
ASTM D 882-02, Standard Test Method for tensile properties of thin plastic sheeting.ASTM International, West Conshohocken, Pennsylvania, USA.
34.
ASTM D 1709-04, Standard Test Method for impact resistance of plastic film by the free-falling dart method.ASTM International, West Conshohocken, Pennsylvania, USA.
35.
KhuriA.I.CornellJ.A., Response surfaces: Designs and analyses, Second edition. Marcel Dekker Inc., New York, USA (1996).
36.
Garrido-VaroA.García-OlmoJ.Pérez-MarínM.D., “Application in the analysis of fat and oils”, in Near-infrared spectroscopy in agriculture, Ed by RobertsC.A., American Society of Agronomy (ASA), Crop Science Society of America (CSSA) and Soil Science Society of America (SSSA), Madison, Wisconsin, USA, pp. 487–558 (2004).
37.
DuckworthJ., “Mathematical data preprocessing”, in “Near-infrared spectroscopy in agriculture”, Ed by RobertsC.A., American Society of Agronomy (ASA), Crop Science Society of America (CSSA) and Soil Science Society of America (SSSA), Madison, Wisconsin, USA, pp. 115–132 (2004).
38.
GeladiP.MacDougallP.D.MartensH., “Linearization and scatter-correction for near-infrared reflectance spectra of meat”, Appl. Spectrosc.39, 491–500 (1985). doi: 10.1366/0003702854248656
39.
PizarroC.Esteban-DíezI.NistalA.J.González-SáizJ.M., “Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy”, Anal. Chim. Acta.509, 217–227 (2004). doi: 10.1016/j.aca.2003.11.008
40.
IsakssonT.NaesT., “The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy”, Appl. Spectrosc.42, 1273–1284 (1988). doi: 10.1366/0003702884429869
41.
NaesT.IsakssonT.KowalskyB., “Locally weighted regression and scatter correction for near-infrared reflectance data”, J. Anal. Chem.62, 664–673 (1990). doi: 10.1021/ac00206a003
42.
BertrandD., “Prétraitament de données spectrales”, in La spectroscopies infrarouge et ses applications analytiques, Ed by BertrandD.DufourE., Technique & Documentation, pp. 351–370 (2000).
43.
ShenkJ.S.WesterhausM.O., “Investigation of a LOCAL calibration procedure for near infrared instruments”, J. Near Infrared Spectrosc.5, 223–232 (1997).
44.
ParkR.S.GordonF.J.AgnewR.E.BarnesR.J.SteenR.W.J., “The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage”, Anim. Feed Sci. Technol.68, 235–246 (1997). doi: 10.1016/S0377-8401(97)00055-2
45.
NaydenovaY.TomovP.DardenneP., “Near infrared spectroscopy estimation of feeding value of forage perennial grasses in breeding programmes by global and specific calibrations. Estimation of chemical composition and digestibility”, J. Near Infrared Spectrosc.6, 153–165 (1998).
46.
Fernández-CabanásV.M., “Métodos de procesamiento de la señal espectroscópica NIR: Aplicación al análisis cuantitativo y cualitativo de productos agroalimentarios”. Thesis. Universidad de Córdoba. Córdoba, Spain (2002).
47.
MeurensM.LepoutreG., “On-line process and quality control by NIR sensor in food industry”, in Proceedings of the 11th international near infrared spectroscopy conference, Ed by DaviesA.M.C.VaroA. Garrido. NIR Publications, Chichester, West Sussex, UK, pp. 143–147 (2004).
48.
JeszenskyÉ.KocsányiL.RichterP., “Eliminating the interference pattern in near-infrared spectra used for identification of thin plastic foils”, Appl. Spectrosc.6, 693–697 (2004). doi: 10.1366/000370204872953
49.
EspañaF.GaitánA.J.OrtizV.De PedroE.J.PérezJ., Actas del III Congreso Español de Ingeniería de Alimentos [CD-ROM], Pamplona, Spain (2004).
50.
CozzolinoD.BarloccoN.VadellA.BallesterosF.GallietaG., “The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle”, Lebensm.-Wiss. U.-Technol.36, 195–202 (2003).
51.
FumièreO.SinnaeveG.DardenneP., “Attempted authentication of cut pieces of chicken meat from certified production using near infrared spectroscopy”, J. Near Infrared Spectrosc.8, 27–34 (2000).
