Browse > Article
http://dx.doi.org/10.5851/kosfa.2019.e88

Differentiation between Normal and White Striped Turkey Breasts by Visible/Near Infrared Spectroscopy and Multivariate Data Analysis  

Zaid, Amal (College of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK))
Abu-Khalaf, Nawaf (College of Agricultural Sciences and Technology, Palestine Technical University-Kadoorie (PTUK))
Mudalal, Samer (Department of Nutrition and Food Technology, Faculty of Agriculture and Veterinary Medicine, An-Najah National University)
Petracci, Massimiliano (Department of Agricultural and Food Sciences, Alma Mater Studiorum, University of Bologna)
Publication Information
Food Science of Animal Resources / v.40, no.1, 2020 , pp. 96-105 More about this Journal
Abstract
The appearance of white striations over breast meat is an emerging and growing problem. The main purpose of this study was to employ the reflectance of visible-near infrared (VIS/NIR) spectroscopy to differentiate between normal and white striped turkey breasts. Accordingly, 34 turkey breast fillets were selected representing a different level of white striping (WS) defects (normal, moderate and severe). The findings of VIS/NIR were analyzed by principal component (PC1) analysis (PCA). It was found that the first PC1 for VIS, NIR and VIS/NIR region explained 98%, 97%, and 96% of the total variation, respectively. PCA showed high performance to differentiate normal meat from abnormal meat (moderate and severe WS). In conclusion, the results of this research showed that VIS/NIR spectroscopy was satisfactory to differentiate normal from severe WS turkey fillets by using several quality traits.
Keywords
VIS/NIR spectroscopy; white striping; quality; PCA;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Monteyne T, Coopman R, Kishabongo AS, Himpe J, Lapauw B, Shadid S, Van Aken EH, Berenson D, Speeckaert MM, De Beer T, Delanghe JR. 2018. Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus. Clin Chem Lab Med 56:1551-1558.   DOI
2 Moran L, Andres S, Allen P, Moloney AP. 2018. Visible and near infrared spectroscopy as an authentication tool: Preliminary investigation of the prediction of the ageing time of beef steaks. Meat Sci 142:52-58.   DOI
3 Mudalal S, Lorenzi M, Soglia F, Cavani C, Petracci M. 2015. Implications of white striping and wooden breast abnormalities on quality traits of raw and marinated chicken meat. Animal 9:728-734.   DOI
4 Mudalal S. 2019. Incidence of white striping and its effect on the quality traits of raw and processed turkey breast meat. Food Sci Anim Resour 39:410-417.   DOI
5 Petracci M, Cavani C. 2012. Muscle growth and poultry meat quality issues. Nutrients 4:1-12.   DOI
6 Petracci M, Mudalal S, Soglia F, Cavani C. 2015. Meat quality in fast-growing broiler chickens. Worlds Poult Sci J 71:363-374.   DOI
7 Petracci M, Soglia F, Berri C. 2017. Muscle metabolism and meat quality abnormalities. In Poultry quality evaluation. Petracci M, Berri C (ed). Woodhead, Cambridge, UK. pp 51-75.
8 Soglia F, Mazzoni M, Petracci M. 2018a. Spotlight on avian pathology: Current growth-related breast meat abnormalities in broilers. Avian Pathol 48:1-3.   DOI
9 Soglia F, Baldi G, Laghi L, Mudalal S, Cavani C, Petracci M. 2018b. Effect of white striping on turkey breast meat quality. Animal 12:2198-2204.   DOI
10 Van Kempen T. 2001. Infrared technology in animal production. Worlds Poult Sci J 57:29-48.   DOI
11 Wold JP, Veiseth-Kent E, Host V, Lovland A. 2017. Rapid on-line detection and grading of wooden breast myopathy in chicken fillets by near-infrared spectroscopy. PLOS ONE 12:e0173384.   DOI
12 Yancey JWS, Apple JK, Meullenet JF, Sawyer JT. 2010. Consumer responses for tenderness and overall impression can be predicted by visible and near-infrared spectroscopy, Meullenet-Owens razor shear, and Warner-Bratzler shear force. Meat Sci 85:487-492.   DOI
13 Yang Y, Zhuang H, Yoon SC, Wang W, Jiang H, Jia B. 2018. Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-infrared spectroscopy. Food Chem 244:184-189.   DOI
14 Zuidhof MJ, Schneider BL, Carney VL, Korver DR, Robinson FE. 2014. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005. Poult Sci 93:2970-2982.   DOI
15 Baldi G, Soglia F, Mazzoni M, Sirri F, Canonico L, Babini E, Laghi L, Cavani C, Petracci M. 2018. Implications of white striping and spaghetti meat abnormalities on meat quality and histological features in broilers. Animal 12:164-173.   DOI
16 Barbut S. 1996. Estimates and detection of the PSE problem in young turkey breast meat. Can J Anim Sci 76:455-457.   DOI
17 Barbut S. 2009. Pale, soft, and exudative poultry meat: Reviewing ways to manage at the processing plant. Poult Sci 88:1506-1512.   DOI
18 Barlocco N, Vadell A, Ballesteros F, Galietta G, Cozzolino D. 2006. Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy. Anim Sci 82:111-116.   DOI
19 Beghi R, Giovenzana V, Tugnolo A, Guidetti R. 2018. Application of visible/near infrared spectroscopy to quality control of fresh fruits and vegetables in large-scale mass distribution channels: A preliminary test on carrots and tomatoes. J Sci Food Agric 98:2729-2734.   DOI
20 Maiorano G. 2017. Meat defects and emergent muscle myopathies in broiler chickens: Implications for the modern poultry industry. Sci Ann Pol Soc Anim Prod 13:43-51.
21 McDevitt RM, Gavin AJ, Andres S, Murray I. 2005. The ability of visible and near infrared reflectance spectroscopy to predict the chemical composition of ground chicken carcasses and to discriminate between carcasses from different genotypes. J Near Infrared Spectrosc 13:109-117.   DOI
22 Meulemans A, Dotreppe O, Leroy B, Istasse L, Clinquart A. 2002. Prediction of organoleptic and technological characteristics of pork meat by near infrared spectroscopy. Viandes & Produits Carnes-Hors Serie 9emes Journees Sciences du Muscle et Technologies des Viandes, Clermond-Ferrand, France. pp 241-242.
23 Mitsumoto M, Maeda S, Mitsuhashi T, Ozawa S. 1991. Near-infrared spectroscopy determination of physical and chemical characteristics in beef cuts. J Food Sci 56:1493-1496.   DOI
24 Ellekjaer MR, Isaksson T. 1992. Assessment of maximum cooking temperatures in previously heat treated beef. Part 1: Near infrared spectroscopy. J Sci Food Agric 59:335-343.   DOI
25 Gardner CM. 2018. Transmission versus reflectance spectroscopy for quantitation. J Biomed Opt 23:018001.
26 Guillemain A, Degardin K, Roggo Y. 2017. Performance of NIR handheld spectrometers for the detection of counterfeit tablets. Talanta 165:632-640.   DOI
27 Hollo J, Kaffka KJ, Gonczy JL. 1987. Near infrared diffuse reflectance/transmittance spectroscopy: Proceedings of the International NIR/NIT Conference. Akademiai Kiado, Budapest, Hungary.
28 Jens PW, Ingrid M, Atle L, Karen WS, Ragni O. 2019. Near-infrared spectroscopy detects woody breast syndrome in chicken fillets by the markers protein content and degree of water binding. Poult Sci 98:480-490.   DOI
29 Jolliffe I. 2011. Principal component analysis. In International encyclopedia of statistical science. Lovric M (ed). Springer, Berlin, German. pp 1094-1096.
30 Kuttappan VA, Owens CM, Coon C, Hargis BM, Vazquez-Anon M. 2017. Incidence of broiler breast myopathies at 2 different ages and its impact on selected raw meat quality parameters. Poult Sci 96:3005-3009.   DOI
31 Li X, He Y. 2006. Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques. Eur Food Res Technol 223:731-736.   DOI
32 Liu Y, Chen YR. 2000. Two-dimensional correlation spectroscopy study of visible and near-infrared spectral variations of chicken meats in cold storage. Appl Spectrosc 54:1458-1470.   DOI
33 Fumiere O, Sinnaeve G, Dardenne P. 2000. Attempted authentication of cut pieces of chicken meat from certified production using near infrared spectroscopy. J Near Infrared Spectrosc 8:27-34.   DOI
34 Brondum J, Munck L, Henckel P, Karlsson A, Tornberg E, Engelsen SB. 2000. Prediction of water-holding capacity and composition of porcine meat by comparative spectroscopy. Meat Sci 55:177-185.   DOI
35 Bowker B, Hawkins S, Zhuang H. 2014. Measurement of water-holding capacity in raw and freeze-dried broiler breast meat with visible and near-infrared spectroscopy. Poult Sci 93:1834-1841.   DOI
36 Brambila GS, Chatterjee D, Bowker B, Zhuang H. 2017. Descriptive texture analyses of cooked patties made of chicken breast with the woody breast condition. Poult Sci 96:3489-3494.   DOI
37 Buning-Pfaue H. 2003. Analysis of water in food by near infrared spectroscopy. Food Chem 82:107-115.   DOI
38 Cozzolino D, Martins V, Murray I. 2002. Visible and near infrared spectroscopy of beef longissimus dorsi muscle as a means of dicriminating between pasture and corn silage feeding regimes. J Near Infrared Spectrosc 10:187-193.   DOI
39 Cozzolino D, Murray I. 2004. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. LWT-Food Sci Technol 37:447-452.   DOI
40 Cozzolino D, Murray I, Paterson R, Scaife JR. 1996. Visible and near infrared reflectance spectroscopy for the determination of moisture, fat and protein in chicken breast and thigh muscle. J. Near Infrared Spectrosc 4:213-223.   DOI
41 Cunha WG, Tinoco MLP, Pancoti HL, Ribeiro RE, Aragao FJL. 2010. High resistance to Sclerotinia sclerotiorum in transgenic soybean plants transformed to express an oxalate decarboxylase gene. Plant Pathol 59:654-660.   DOI
42 Abu-Khalaf N. 2015. Sensing tomato's pathogen using Visible/Near Infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA). Palest Tech Univ Res J 3:12-22.
43 Andres S, Silva A, Soares-Pereira AL, Martins C, Bruno-Soares AM, Murray I. 2008. The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. Meat Sci 78:217-224.   DOI
44 De Marchi M, Riovanto R, Penasa M, Cassandro M. 2012. At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy. Meat Sci 90:653-657.   DOI