Fat content is one of the most important quality indicators for minced beef products. In this study, a multipoint near infrared (NIR) spectrophotometer system, based on a Fabry–Perot interferometer, combined with a four-point photodiode array detector and flexible collimator–probe arrangement, was used for real-time analysis of beef fat content. The system was employed to predict fat content of mixed minced beef samples concurrently under two different conditions: (a) static and slow motion and (b) static and fast motion. Additionally, a separate measurement was conducted to further test the independency of a collimator–probe arrangement by scanning two samples with different fat percentages concurrently under static and motion conditions. Partial least squares regression was employed, obtaining coefficients of determination in calibration (R2c) of 0.95, confirming a good fit for the three models. The fat contents of samples in the independent set were predicted with reasonable accuracy: r2 in the range 0.82–0.92 and standard error of prediction in the range 3.05–3.98%. Moreover, the spectral features observed for the probe independency test clearly illustrated the flexibility and independency of the collimator–probe arrangement. This study showed that the multipoint NIR spectroscopy system can predict beef fat content concurrently under static and motion conditions and illustrates its potential use as an in-line monitoring tool at various junctions in a meat processing plant.
FAO, FAO's Animal Production and Health Division: Meat & Meat Products (2015).
2.
KrishnanRadha K.BabuskinS.BabuAzhagu Saravana P.SivarajanM. and SukumarM., “Evaluation and predictive modeling the effects of spice extracts on raw chicken meat stored at different temperatures”, J. Food Eng.166, 29 (2015). doi: http://dx.doi.org/10.1016/j.jfoodeng.2015.05.021
3.
de VriesM.van MiddelaarC. and de BoerI., “Comparing environmental impacts of beef production systems: A review of life cycle assessments”, Livestock Sci.178, 279 (2015). doi: http://dx.doi.org/10.1016/j.livsci.2015.06.020
4.
MorsyN. and SunD.W., “Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen–thawed minced beef”, Meat Sci.93, 292 (2013). doi: http://dx.doi.org/10.1016/j.meatsci.2012.09.005
5.
KerthC.R.HarbisonA.L.SmithS.B. and MillerR.K., “Consumer sensory evaluation, fatty acid composition, and shelf-life of ground beef with subcutaneous fat trimmings from different carcass locations”, Meat Sci.104, 30 (2015). doi: http://dx.doi.org/10.1016/j.meatsci.2015.01.014
6.
ToldráF. (Ed), Handbook of Meat Processing.Wiley-Blackwell, Oxford (2010).
7.
O'BrienR.D., Fats and Oils: Formulating and Processing for Applications.CRC Press, Boca Raton, FL (2008).
8.
Food Standards Scotland, “Labelling and composition of meat products”, in Annex C – Table of typical values for meat ingredients.Food Standards Scotland, Aberdeen, p. 57 (2004).
9.
ElMasryG.SunD.-W. and AllenP., “Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef”, J. Food Eng.110, 127 (2012). doi: http://dx.doi.org/10.1016/j.jfoodeng.2011.11.028
10.
RankenM.D., Handbook of Meat Product Technology.Blackwell Science, Oxford (2000).
11.
WuD. and SunD.-W., “Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review – part II: Applications”, Innovat. Food Sci. Emerg. Technol.19, 15 (2013). doi: http://dx.doi.org/10.1016/j.ifset.2013.04.016
12.
AlampreseC.CasaleM.SinelliN.LanteriS. and CasiraghiE., “Detection of minced beef adulteration with turkey meat by UV–vis, NIR and MIR spectroscopy”, LWT Food Sci. Technol.53, 225 (2013). doi: http://dx.doi.org/10.1016/j.lwt.2013.01.027
CozzolinoD.BarloccoN.VadellA.BallesterosF. and GallietaG., “The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle”, LWT Food Sci. Technol.36, 195 (2003). doi: http://dx.doi.org/10.1016/s0023-6438(02)00199-8
15.
GowenA.O'DonnellC.CullenP.J. and BellS., “Recent applications of chemical imaging to pharmaceutical process monitoring and quality control”, Eur. J. Pharm. Biopharm.69, 10 (2008). doi: http://dx.doi.org/10.1016/j.ejpb.2007.10.013
16.
MagwazaL.S.OparaU.L.NieuwoudtH.CronjeP.J.SaeysW. and NicolaïB., “NIR spectroscopy applications for internal and external quality analysis of citrus fruit: A review”, Food Bioprocess Technol.5, 425 (2012). doi: http://dx.doi.org/10.1007/s11947-011-0697-1
17.
ElMasryG.SunD.-W. and AllenP., “Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging”, J. Food Eng.117, 235 (2013). doi: http://dx.doi.org/10.1016/j.jfoodeng.2013.02.016
18.
GhoshP.K. and JayasD.S., “Use of spectroscopic data for automation in food processing industry”, Sens. Instrum. Food Quality Safety3, 3 (2009). doi: http://dx.doi.org/10.1007/s11694-008-9068-7
19.
AndersonN.M. and WalkerP.N., “Measuring fat content of ground beef stream using on-line visible/NIR spectroscopy”, Trans. ASAE46, 117 (2003). doi: http://dx.doi.org/10.13031/2013.12537
20.
WoldJ.O'FarrellM.HøyM. and TschudiJ., “On-line determination and control of fat content in batches of beef trimmings by NIR imaging spectroscopy”, Meat Sci.89, 317 (2011). doi: http://dx.doi.org/10.1016/j.meatsci.2011.05.001
21.
AndresS.MurrayI.NavajasE.A.FisherA.V.LambeN.R. and BungerL., “Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy”, Meat Sci.76, 509 (2007). doi: http://dx.doi.org/10.1016/j.meatsci.2007.01.011
22.
SrivichienS.TerdwongworakulA. and TeerachaichayutS., “Quantitative prediction of nitrate level in intact pineapple using Vis–NIRS”, J. Food Eng.150, 29 (2015). doi: http://dx.doi.org/10.1016/j.jfoodeng.2014.11.004
23.
PullanagariR.R.YuleI.J. and AgnewM., “On-line prediction of lamb fatty acid composition by visible near infrared spectroscopy”, Meat Sci.100, 156 (2015). doi: http://dx.doi.org/10.1016/j.meatsci.2014.10.008
24.
PrietoN.JuarezM.LarsenI.L.Lopez-CamposO.ZijlstraR.T. and AalhusJ.L., “Rapid discrimination of enhanced quality pork by visible and near infrared spectroscopy”, Meat Sci.110, 76 (2015). doi: http://dx.doi.org/10.1016/j.meatsci.2015.07.006
25.
ShiZ.CogdillR.P.ShortS.M. and AndersonC.A., “Process characterization of powder blending by near-infrared spectroscopy: Blend end-points and beyond”, J. Pharm. Biomed. Anal.47, 738 (2008). doi: http://dx.doi.org/10.1016/j.jpba.2008.03.013
26.
ScheibelhoferO.BalakN.KollerD. and KhinastJ., “Spatially resolved monitoring of powder mixing processes via multiple NIR-probes”, Powder Technol.243, 161 (2013). doi: http://dx.doi.org/10.1016/j.powtec.2013.03.035
27.
Meza-MarquezO.G.Gallardo-VelazquezT. and Osorio-RevillaG., “Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef”, Meat Sci.86, 511 (2010). doi: http://dx.doi.org/10.1016/j.meatsci.2010.05.044
28.
TeamR Core, R: A Language and Environment for Statistical Computing.R Foundation for Statistical Computing, Vienna (2014).
29.
MevikB.-H.WehrensR. and LilandK.H., “PLS: Partial least squares and principal component regression”, R package version 2.4–3 (2013).
30.
MoreiraS.A.SarraguçaJ.SaraivaD.F.CarvalhoR. and LopesJ.A., “Optimization of NIR spectroscopy based PLSR models for critical properties of vegetable oils used in biodiesel production”, Fuel150, 697 (2015). doi: http://dx.doi.org/10.1016/j.fuel.2015.02.082
31.
MakkyM. and SoniP., “In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach”, J. Food Eng.120, 248 (2014). doi: http://dx.doi.org/10.1016/j.jfoodeng.2013.08.011
32.
Hoving-BolinkA.VedderH.MerksJ.De KleinW.ReimertH.FrankhuizenR. and Van den BroekW., “Perspective of NIRS measurements early post mortem for prediction of pork quality”, Meat Sci.69, 417 (2005). doi: http://dx.doi.org/10.1016/j.meatsci.2004.08.012
33.
IsakssonT.MillerC.E. and NæsT., “Nondestructive NIR and NIT determination of protein, fat, and water in plastic-wrapped, homogenized meat”, Appl. Spectrosc.46, 1685 (1992). doi: http://dx.doi.org/10.1366/0003702924926745
34.
BarloccoN.VadellA.BallesterosF.GaliettaG. and CozzolinoD., “Predicting intramuscular fat, moisture and Warner–Bratzler shear force in pork muscle using near infrared reflectance spectroscopy”, Anim. Sci.82, 111 (2006). doi: http://dx.doi.org/10.1079/ASC20055
35.
CozzolinoD.De MattosD. and MartinsD.V., “Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle”, Anim. Sci.74, 477 (2002).
36.
PrietoN.RoeheR.LavinP.BattenG. and AndresS., “Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review”, Meat Sci.83, 175 (2009). doi: http://dx.doi.org/10.1016/j.meatsci.2009.04.016
BarbinD.F.ElMasryG.SunD.-W.AllenP. and MorsyN., “Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging”, Innovat. Food Sci. Emerg. Technol.17, 180 (2013). doi: http://dx.doi.org/10.1016/j.ifset.2012.11.001
39.
ChengJ.-H. and SunD.-W., “Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis”, LWT Food Sci. Technol.62, 1060 (2015). doi: http://dx.doi.org/10.1016/j.lwt.2015.01.021
40.
AlexandrakisD., “NIRS in an industrial environment,”Eur Pharm Rev. Near InfraredSuppl.17, 3 (2012).
