Food Analytical Methods

, Volume 11, Issue 5, pp 1356–1366 | Cite as

Quality Assessment of Intact Chicken Breast Fillets Using Factor Analysis with Vis/NIR Spectroscopy

  • Yi Yang
  • Hong Zhuang
  • Seung-Chul Yoon
  • Wei Wang
  • Hongzhe Jiang
  • Beibei Jia
  • Chunyang Li


Factor analysis (FA) method was tested to assess quality of chicken breast fillets with the visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm. According to inherent correlation, three factors were extracted from the measured eight quality traits (L*, a*, b*, pH, moisture, drip loss, expressible fluid, and salt-induced water gain). The extracted “grade factor” (F 1), “color factor” (F 2), and “moisture factor” (F 3) could respectively represent the characteristics and the variation tendency of the corresponding quality traits and were defined as three new quality assessment indexes. Furthermore, partial least squares regression (PLSR) models were established to quantitatively relate spectral information to eight individual quality traits and three factors. The results indicated that the models for predicting each factor performed better than those for individual quality traits. Key wavelengths of each quality trait were then selected, and the corresponding spectra were taken to build new PLSR prediction models. The selected key wavelengths showed obvious practical significance, and the new models had comparable predictive performance to those models developed based on the full spectra, among which the new models of F 1 and F 2 had acceptable and robust predictive abilities (R2p = 0.73, RPD = 1.91; R2p = 0.74, RPD = 1.97). Our results in the present study demonstrate the potential for FA and Vis/NIR spectroscopy as a useful method to assess the quality of chicken breast fillets.


Poultry Pectoralis major Water hold capacity pH Color Partial least squares regression (PLSR) 


Funding Information

The authors acknowledge financial support by the China National Science and Technology Support Program (Grant no. 2012BAK08B04).

Compliance with Ethical Standards

Conflict of Interest

Yi Yang declares that he has no conflict of interest. Hong Zhuang declares that he has no conflict of interest. Seung-Chul Yoon declares that he has no conflict of interest. Wei Wang declares that he has no conflict of interest. Hongzhe Jiang declares that he has no conflict of interest. Beibei Jia declares that he has no conflict of interest. Chunyang Li declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.


  1. Abeni F, Bergoglio G (2001) Characterization of different strains of broiler chicken by carcass measurements, chemical and physical parameters and NIRS on breast muscle. Meat Sci 57:133–137CrossRefGoogle Scholar
  2. Andres S, Silva A, Soare-Pereira AL, Martins C, Bruno-Soare 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 78:217–224CrossRefGoogle Scholar
  3. AOAC (1990) Official methods of analysis. 15th ed. Washington, DCGoogle Scholar
  4. Balage JM, Silva SL, Gomide CA, Bonin MN, Figueria AC (2015) Predicting pork quality using Vis/NIR spectroscopy. Meat Sci 108:37–43CrossRefGoogle Scholar
  5. Barbin DF, Kaminishikawahara CM, Soares AL, Mizubuti IY, Grespan M, Shimokomaki M, Hirooka EY (2015) Prediction of chicken quality attributes by near infrared spectroscopy. Food Chem 168:554–560CrossRefGoogle Scholar
  6. Barbut S, Zhang L, Marcone M (2005) Effects of pale, normal, and dark chicken breast meat on microstructure, extractable proteins, and cooking of marinated fillets. Poult Sci 84:797–802CrossRefGoogle Scholar
  7. 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 82:111–116CrossRefGoogle Scholar
  8. Bowker B, Hawkins S, Zhuang H (2014a) Measurement of water-holding capacity in raw and freeze-dried broiler breast meat with visible and near-infrared spectroscopy. Poult Sci 93:1834–1841CrossRefGoogle Scholar
  9. Bowker B, Zhuang H, Buhr R (2014b) Impact of carcass scalding and chilling on muscle proteins and meat quality of broiler breast fillets. LWT--Food Sci. Technol 59:156–162CrossRefGoogle Scholar
  10. Chan DE, Walker PN, Mills EW (2002) Prediction of pork quality characteristics using visible and near-infrared spectroscopy. Am Soc Agric Eng 45:1519–1527Google Scholar
  11. Chen KP, Jiao DH, Huang JM, Huang RQ (2007) Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen. Environ Pollut 147:771–780CrossRefGoogle Scholar
  12. Cozzolino D, De M, Vaz MD (2002) Visible/near infrared reflectance spectroscopy for predicting composition and tracing system of production of beef muscle. Anim Sci 74:477–484CrossRefGoogle Scholar
  13. Cozzolino D, Barlocco N, Vadell A, Ballesteros A, Gallieta G (2003) The use of visible and near infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle. LWT—Food Sci. Technol 36:195–202Google Scholar
  14. EIMasry GF, Sun DW, Allen P (2011) Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Res Int 44:2624–2633CrossRefGoogle Scholar
  15. EIMasry GF, Sun DW, Allen P (2012) Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J Food Eng 110:127–140CrossRefGoogle Scholar
  16. Hawkins SA, Bowker B, Zhuang H, Gamble G, Holser R (2014) Post-mortem chemical changes in poultry breast meat monitored with visible-near infrared spectroscopyJ. J Food Res 3:57–65CrossRefGoogle Scholar
  17. He HJ, Wu D, Sun DW (2013) Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innovative Food Sci Emerg Technol 18:237–245CrossRefGoogle Scholar
  18. He HJ, Wu D, Sun DW (2014) Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis/NIR) hyperspectral imaging. Food Chem 156:394–401CrossRefGoogle Scholar
  19. Honikel KO, Hamm R (1994) Quality attributes and their measurement in meat, poultry and fish products. Blackie Academic and Professional, New YorkGoogle Scholar
  20. Kapper C, Klont RE, Verdonk JMAJ, Williams PC, Urlings HAP (2012a) Prediction of pork quality with near infrared spectroscopy (NIRS): 1. Feasibility and robustness of NIRS measurements at laboratory scale. Meat Sci 91:294–299CrossRefGoogle Scholar
  21. Kapper C, Klont RE, Verdonk JMAJ, Williams PC, Urlings HAP (2012b) Prediction of pork quality with near infrared spectroscopy (NIRS): 2. Feasibility and robustness of NIRS measurements under production plant conditions. Meat Sci 91:300–305CrossRefGoogle Scholar
  22. Keskin M, Dodd RB, Han YJ, Khalilian A (2004) Assessing nitrogen content of golf course turfgrass clippings using spectral reflectance. Appl Eng Agric 20:245–253CrossRefGoogle Scholar
  23. Kuppusamy MR, Giridhar VV (2006) Factor analysis of water quality characteristics including trace metal speciation in the coastal environmental system of Chennai Ennore. Environ Int 32:174–179CrossRefGoogle Scholar
  24. Lesiów T, Kijowski T (2003) Impact of PSE and DFD meat on poultry processing—a review. Pol J Food Nutr Sci 12:3–8Google Scholar
  25. Liu HJ (2015) Detection method and device for determination of Huanghua pear harvest date using near infrared spectroscopy. Dissertation, Zhejiang UniversityGoogle Scholar
  26. Liu CW, Lin KH, Gou YM (2003) Application of factor analysis in the assessment of groundwater quality in a Blackfoot disease area in Taiwan. Sci Total Environ 313:77–89CrossRefGoogle Scholar
  27. Liu Y, Lyon BG, Windham WR, Lyon CE, Savage EM (2004a) Principal component analysis of physical, color, and sensory characteristics of chicken breasts deboned at two, four, six, and twenty-four hours postmortem. Poult Sci 83:101–108CrossRefGoogle Scholar
  28. Liu Y, Lyon BG, Windham WR, Lyon CE, Savage EM (2004b) Prediction of physical, color and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. Poult Sci 83:1467–1474CrossRefGoogle Scholar
  29. Ma C, Liang Q, Wen PC, Zhang Y (2016) Effect of modified atmosphere packaging with different oxygen content on color stability of yak meat. Food Ferment Ind 42:130–136Google Scholar
  30. Magwaza LS, Opara UL, Nieuwoudt H, Cronje PJR, Saeys W, Nicolai B (2012) NIR spectroscopy applications for internal and external quality analysis of citrus fruit—a review. Food Bioprocess Technol 5:425–444CrossRefGoogle Scholar
  31. Menesatti P, Zanella A, D’Andrea S, Costa C, Paglia G, Pallottino F (2009) Supervised multivariate analysis of hyper-spectral NIR images to evaluate the starch index of apples. Food Bioprocess Technol 2:308–314CrossRefGoogle Scholar
  32. Monroy M, Prasher S, Ngadi MO, Wang N, Karimi Y (2010) Pork meat quality classification using visible/near-infrared spectroscopic data. Biosyst Eng 107:271–276CrossRefGoogle Scholar
  33. Prieto N, Andres S, Giraldez FJ, Mantecon AR, Lavin P (2008) Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. Meat Sci 79:692–699Google Scholar
  34. Prieto N, Roeche R, Lavin P, Batten G, Andres S (2009) Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Sci 83:175–186Google Scholar
  35. Razmkhah H, Abrishamchi A, Torkian A (2010) Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: a case study on Jajrood River (Tehran, Iran). J Environ Manag 91:852–860CrossRefGoogle Scholar
  36. Riovanto R, De Marchi M, Cassandro M, Penasa M (2012) Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat. Food Chem 134:2459–2464CrossRefGoogle Scholar
  37. Samuel D, Park B, Sohn M, Wicker L (2011) Visible-near-infrared spectroscopy to predict water-holding capacity in normal and pale broiler breast meat. Poult Sci 90:914–921CrossRefGoogle Scholar
  38. Smith DP, Northcutt JK (2009) Pale poultry muscle syndrome. Poult Sci 88:1493–1496CrossRefGoogle Scholar
  39. Workman J, Weyer L (2007) Practical guide to interpretive near-infrared spectroscopy. CRC Press, Boca RatonGoogle Scholar
  40. Wu D, Sun DW, He Y (2012) Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet. Innovative Food Sci Emerg Technol 16:361–372CrossRefGoogle Scholar
  41. Xue JX (2016) Nondestructive detection for assessing comprehensive quality attributes of fresh jujube based on spectroscopy and imaging technology. Dissertation, Shanxi Agricultural UniversityGoogle Scholar
  42. Zarei H, Bilondi MP (2013) Factor analysis of chemical composition in the Karoon River basin, southwest of Iran. Appl Water Sci 3:753–761CrossRefGoogle Scholar
  43. Zhang L, Barbut S (2005) Effects of regular and modified starches on cooked pale, soft, and exudative; normal; and dry, firm, and dark breast meat batters. Poult Sci 84:789–796CrossRefGoogle Scholar
  44. Zhao HW, Han DH, Song SH, Chang D (2012) Screening of maturity characterization factors for mini watermelon fruit. Trans Chin Soc Agric Eng 17:281–286Google Scholar
  45. Zhuang H, Bowker B (2016) Effect of marination on lightness of broiler breast fillets varies with raw meat color attributes. LWT--Food Sci Technol 69:233–235CrossRefGoogle Scholar
  46. Zhuang H, Savage EM (2008) Validation of a combi oven cooking method for preparation of chicken breast meat for quality assessment. J Food Sci 73:424–430CrossRefGoogle Scholar
  47. Zhuang H, Savage EM (2012) Postmortem aging and freezing and thawing storage enhance ability of early deboned chicken pectoralis major muscle to hold added salt water. Poult Sci 91:1203–1209CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Yi Yang
    • 1
  • Hong Zhuang
    • 2
  • Seung-Chul Yoon
    • 2
  • Wei Wang
    • 1
  • Hongzhe Jiang
    • 1
  • Beibei Jia
    • 1
  • Chunyang Li
    • 3
  1. 1.College of EngineeringChina Agricultural UniversityBeijingChina
  2. 2.Quality & Safety Assessment Research Unit, U.S. National Poultry Research CenterUSDA-ARSAthensUSA
  3. 3.Institute of Food Science and TechnologyJiangsu Academy of Agricultural SciencesNanjingChina

Personalised recommendations