Skip to main content

Advertisement

SpringerLink
Log in

Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle

  • Review Article
  • Published:
Food Engineering Reviews Aims and scope Submit manuscript

Abstract

Partial least squares regression (PLSR) is a classical and widely used linear method for modeling of spectral data. Measurement of fish chemical properties has been playing an important role in providing superior quality products for human health and international trade. This review focuses on the PLSR applied to near-infrared (NIR) and hyperspectral imaging (HSI) spectral data for rapid and chemical-free modeling and predicting chemical properties of fish muscle, including moisture content, lipid content, protein content, pH, and freshness indicators, such as total volatile basic nitrogen, thiobarbituric acid reactive substances, and K index value. Furthermore, the commonly used spectral preprocessing methods and variable selection algorithms are mentioned and discussed for the enhancement of PLSR analysis. The limitations and future trends of NIR and HSI techniques with PLSR analysis are also presented. In a word, NIR and HSI technique in tandem with PLSR method have been developed to be suitable and trustworthy alternatives to the traditional chemical analytical methods such as Kjeldahl, Soxhlet, and chromatography methods for detecting chemical information of fish muscle in an objective, rapid, noninvasive, and chemical-free manner.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Mathiassen JR, Misimi E, Bondø M, Veliyulin E, Østvik SO (2011) Trends in application of imaging technologies to inspection of fish and fish products. Trends Food Sci Technol 22(6):257–275

    Article  CAS  Google Scholar 

  2. Cozzolino D, Murray I (2012) A review on the application of infrared technologies to determine and monitor composition and other quality characteristics in raw fish, fish products, and seafood. Appl Spectrosc Rev 47(3):207–218

    Article  Google Scholar 

  3. Dowlati M, de la Guardia M, Mohtasebi SS (2012) Application of machine-vision techniques to fish-quality assessment. TrAC Trends Anal Chem 40:168–179

    Article  CAS  Google Scholar 

  4. Cheng J-H, Dai Q, Sun D-W, Zeng X-A, Liu D, Pu H-B (2013) Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 34(1):18–31

    Article  CAS  Google Scholar 

  5. Liu D, Zeng X-A, Sun D-W (2013) NIR spectroscopy and imaging techniques for evaluation of fish quality-a review. Appl Spectrosc Rev 48(8):609–628

    Article  CAS  Google Scholar 

  6. He H-J, Wu D, Sun D-W (2015) Nondestructive spectroscopic and imaging techniques for quality evaluation and assessment of fish and fish products. Crit Rev Food Sci Nutr 55(6):864–886

    Article  CAS  Google Scholar 

  7. Pohlenz C, Gatlin DM (2014) Interrelationships between fish nutrition and health. Aquaculture 431:111–117

    Article  CAS  Google Scholar 

  8. Borras E, Ferre J, Boque R, Mestres M, Acena L, Busto O (2015) Data fusion methodologies for food and beverage authentication and quality assessment—a review. Anal Chim Acta 891:1–14

    Article  CAS  Google Scholar 

  9. Cheng J-H, Sun D-W, Han Z, Zeng X-A (2014) Texture and structure measurements and analyses for evaluation of fish and fillet freshness quality: a review. Compr Rev Food Sci Food Saf 13(1):52–61

    Article  Google Scholar 

  10. Neethirajan S, Jayas DS (2011) Nanotechnology for the food and bioprocessing industries. Food Bioprocess Technol 4(1):39–47

    Article  CAS  Google Scholar 

  11. Cui ZW, Sun LJ, Chen W, Sun D-W (2008) Preparation of dry honey by microwave–vacuum drying. J Food Eng 84(4):582–590

    Article  Google Scholar 

  12. Sun D-W, Woods JL (1994) Low temperature moisture transfer characteristics of wheat in thin layers. Trans ASAE 37(6):1919–1926

    Article  Google Scholar 

  13. Mc Donald K, Sun D-W (2001) Effect of evacuation rate on the vacuum cooling process of a cooked beef product. J Food Eng 48(3):195–202

    Article  Google Scholar 

  14. Sun D-W (1997) Solar powered combined ejector-vapour compression cycle for air conditioning and refrigeration. Energy Convers Manag 38(5):479–491

    Article  CAS  Google Scholar 

  15. Wang L, Sun D-W (2004) Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. J Food Eng 61(2):231–240

    Article  Google Scholar 

  16. Zheng L, Sun D-W (2004) Vacuum cooling for the food industry—a review of recent research advances. Trends Food Sci Technol 15(12):555–568

    Article  CAS  Google Scholar 

  17. Kiani H, Zhang Z, Delgado A, Sun D-W (2011) Ultrasound assisted nucleation of some liquid and solid model foods during freezing. Food Res Int 44(9):2915–2921

    Article  CAS  Google Scholar 

  18. Alasalvar C, Taylor K, Öksüz A, Garthwaite T, Alexis M, Grigorakis K (2001) Freshness assessment of cultured sea bream (Sparus aurata) by chemical, physical and sensory methods. Food Chem 72(1):33–40

    Article  CAS  Google Scholar 

  19. Arvanitoyannis IS, Kotsanopoulos KV (2012) Smoking of fish and seafood: history, methods and effects on physical, nutritional and microbiological properties. Food Bioprocess Technol 5(3):831–853

    Article  CAS  Google Scholar 

  20. Önal A (2007) A review: current analytical methods for the determination of biogenic amines in foods. Food Chem 103(4):1475–1486

    Article  CAS  Google Scholar 

  21. Özogul F, Taylor K, Quantick P, Özogul Y (2000) Chemical, microbiological and sensory evaluation of Atlantic herring (Clupea harengus) stored in ice, modified atmosphere and vacuum pack. Food Chem 71(2):267–273

    Article  Google Scholar 

  22. Alander JT, Bochko V, Martinkauppi B, Saranwong S, Mantere T (2013) A review of optical nondestructive visual and near-infrared methods for food quality and safety. Int J Spectrosc 2013:1–36

    Article  CAS  Google Scholar 

  23. Zhu F, Zhang D, He Y, Liu F, Sun D-W (2013) Application of visible and near infrared hyperspectral imaging to differentiate between fresh and frozen–thawed fish fillets. Food Bioprocess Technol 6(10):2931–2937

    Article  CAS  Google Scholar 

  24. Wu D, Sun D-W (2013) Colour measurements by computer vision for food quality control–A review. Trends Food Sci Technol 29(1):5–20

    Article  CAS  Google Scholar 

  25. Wang HH, Sun D-W (2002) Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques. J Food Eng 52(3):279–284

    Article  Google Scholar 

  26. Jackman P, Sun D-W, Allen P (2009) Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. Meat Sci 83(2):187–194

    Article  Google Scholar 

  27. Elmasry G, Kamruzzaman M, Sun D-W, Allen P (2012) Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit Rev Food Sci Nutr 52(11):999–1023

    Article  Google Scholar 

  28. Barbin D, Elmasry G, Sun D-W, Allen P (2012) Near-infrared hyperspectral imaging for grading and classification of pork. Meat Sci 90(1):259–268

    Article  Google Scholar 

  29. Wu D, Sun D-W (2013) Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. Talanta 111:39–46

    Article  CAS  Google Scholar 

  30. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2012) Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innov Food Sci Emerg Technol 16:218–226

    Article  CAS  Google Scholar 

  31. ElMasry G, Barbin DF, Sun D-W, Allen P (2012) Meat quality evaluation by hyperspectral imaging technique: an overview. Crit Rev Food Sci Nutr 52(8):689–711

    Article  Google Scholar 

  32. Feng YZ, Sun D-W (2012) Application of hyperspectral imaging in food safety inspection and control: a review. Crit Rev Food Sci Nutr 52(11):1039–1058

    Article  Google Scholar 

  33. Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review—Part I: Fundamentals. Innov Food Sci Emerg Technol 19:1–14

    Article  CAS  Google Scholar 

  34. Barbin DF, ElMasry G, Sun D-W, Allen P (2013) Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem 138(2):1162–1171

    Article  CAS  Google Scholar 

  35. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2013) Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. Food Chem 141(1):389–396

    Article  CAS  Google Scholar 

  36. Lohumi S, Lee S, Lee H, Cho B-K (2015) A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends Food Sci Technol 46(1):85–98

    Article  CAS  Google Scholar 

  37. Rinnan Å, van den Berg F, Engelsen SB (2009) Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem 28(10):1201–1222

    Article  CAS  Google Scholar 

  38. Blanco M, Villarroya I (2002) NIR spectroscopy: a rapid-response analytical tool. TrAC Trends Anal Chem 21(4):240–250

    Article  CAS  Google Scholar 

  39. Tavallaie R, Talebpour Z, Azad J, Soudi MR (2011) Simultaneous determination of pyruvate and acetate levels in xanthan biopolymer by infrared spectroscopy: effect of spectral pre-processing for solid-state analysis. Food Chem 124(3):1124–1130

    Article  CAS  Google Scholar 

  40. Amigo JM, Babamoradi H, Elcoroaristizabal S (2015) Hyperspectral image analysis. A tutorial. Anal Chim Acta 896(10):34–51

    Article  CAS  Google Scholar 

  41. Andersen CM, Bro R (2010) Variable selection in regression—a tutorial. J Chemom 24(11–12):728–737

    Article  CAS  Google Scholar 

  42. Mehmood T, Liland KH, Snipen L, Sæbø S (2012) A review of variable selection methods in partial least squares regression. Chemometr Intell Lab Syst 118:62–69

    Article  CAS  Google Scholar 

  43. Liu D, Sun D-W, Zeng X-A (2014) Recent advances in wavelength selection techniques for hyperspectral image processing in the food industry. Food Bioprocess Technol 7(2):307–323

    Article  Google Scholar 

  44. Dai Q, Cheng J-H, Sun D-W, Zeng X-A (2015) Advances in feature selection methods for hyperspectral image processing in food industry applications: a review. Crit Rev Food Sci Nutr 55(10):1368–1382

    Article  CAS  Google Scholar 

  45. Elmasry G, Barbin DF, Sun D-W, Allen P (2012) Meat quality evaluation by hyperspectral imaging technique: an overview. Crit Rev Food Sci Nutr 52(8):689–711

    Article  Google Scholar 

  46. Dai Q, Sun D-W, Xiong Z, Cheng J-H, Zeng X-A (2014) Recent advances in data mining techniques and their applications in hyperspectral image processing for the food industry. Compr Rev Food Sci Food Saf 13(5):891–905

    Article  Google Scholar 

  47. Riedl J, Esslinger S, Fauhl-Hassek C (2015) Review of validation and reporting of non-targeted fingerprinting approaches for food authentication. Anal Chim Acta 885:17–32

    Article  CAS  Google Scholar 

  48. Cawley GC, Talbot NL (2003) Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers. Pattern Recogn 36(11):2585–2592

    Article  Google Scholar 

  49. Dong M, Wang N (2011) Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Appl Math Model 35(3):1024–1035

    Article  Google Scholar 

  50. Wong T-T (2015) Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recogn 48(9):2839–2846

    Article  Google Scholar 

  51. Cheng J-H, Sun D-W (2015) Recent applications of spectroscopic and hyperspectral imaging techniques with chemometric analysis for rapid inspection of microbial spoilage in muscle foods. Compr Rev Food Sci Food Saf 14(4):478–490

    Article  CAS  Google Scholar 

  52. Naes T, Isaksson T, Fearn T, Davies T (2002) A user friendly guide to multivariate calibration and classification. NIR publications, Chichester

    Google Scholar 

  53. Williams PC (2001) Implementation of near-infrared technology. Near Infrared Technol Agric Food Ind 2:145–169

    Google Scholar 

  54. Cozzolino D, Cynkar W, Shah N, Smith P (2011) Multivariate data analysis applied to spectroscopy: potential application to juice and fruit quality. Food Res Int 44(7):1888–1896

    Article  CAS  Google Scholar 

  55. Nicolai BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118

    Article  Google Scholar 

  56. Huang H, Yu H, Xu H, Ying Y (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J Food Eng 87(3):303–313

    Article  CAS  Google Scholar 

  57. Magwaza LS, Opara UL, Nieuwoudt H, Cronje PJ, Saeys W, Nicolaï B (2012) NIR spectroscopy applications for internal and external quality analysis of citrus fruit-a review. Food Bioprocess Technol 5(2):425–444

    Article  CAS  Google Scholar 

  58. Prieto N, Roehe 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(2):175–186

    Article  CAS  Google Scholar 

  59. Cen H, He Y (2007) Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 18(2):72–83

    Article  CAS  Google Scholar 

  60. Osborne BG, Fearn T, Hindle PH (1993) Practical NIR spectroscopy with applications in food and beverage analysis. Longman scientific and technical, New York

    Google Scholar 

  61. Khodabux K, L’Omelette MSS, Jhaumeer-Laulloo S, Ramasami P, Rondeau P (2007) Chemical and near-infrared determination of moisture, fat and protein in tuna fishes. Food Chem 102(3):669–675

    Article  CAS  Google Scholar 

  62. Cozzolino D, Murray I, Scaife J (2002) Near infrared reflectance spectroscopy in the prediction of chemical characteristics of minced raw fish. Aquac Nutr 8(1):1–6

    Article  CAS  Google Scholar 

  63. Liu Y, Ma D-H, Wang X-C, Liu L-P, Fan Y-X, Cao J-X (2015) Prediction of chemical composition and geographical origin traceability of Chinese export tilapia fillets products by near infrared reflectance spectroscopy. LWT Food Sci Technol 60(2):1214–1218

    Article  CAS  Google Scholar 

  64. Xiccato G, Trocino A, Tulli F, Tibaldi E (2004) Prediction of chemical composition and origin identification of european sea bass (Dicentrarchus labrax L.) by near infrared reflectance spectroscopy (NIRS). Food Chem 86(2):275–281

    Article  CAS  Google Scholar 

  65. Dalle Zotte A, Ottavian M, Concollato A, Serva L, Martelli R, Parisi G (2014) Authentication of raw and cooked freeze-dried rainbow trout (Oncorhynchus mykiss) by means of near infrared spectroscopy and data fusion. Food Res Int 60:180–188

    Article  CAS  Google Scholar 

  66. Huang Y, Cavinato A, Mayes D, Kangas L, Bledsoe G, Rasco B (2003) Nondestructive determination of moisture and sodium chloride in cured Atlantic salmon (Salmo salar) (Teijin) using short-wavelength near-infrared spectroscopy (SW-NIR). J Food Sci 68(2):482–486

    Article  CAS  Google Scholar 

  67. Nielsen D, Hyldig G, Nielsen J, Nielsen HH (2005) Lipid content in herring (Clupea harengus L.)-influence of biological factors and comparison of different methods of analyses: solvent extraction, Fatmeter, NIR and NMR. LWT Food Sci Technol 38(5):537–548

    Article  CAS  Google Scholar 

  68. Karlsdottir MG, Arason S, Kristinsson HG, Sveinsdottir K (2014) The application of near infrared spectroscopy to study lipid characteristics and deterioration of frozen lean fish muscles. Food Chem 159:420–427

    Article  CAS  Google Scholar 

  69. Folkestad A, Wold JP, Rørvik K-A, Tschudi J, Haugholt KH, Kolstad K, Mørkøre T (2008) Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.). Aquaculture 280(1):129–135

    Article  CAS  Google Scholar 

  70. Olafsdottir G, Martinsdóttir E, Oehlenschläger J, Dalgaard P, Jensen B, Undeland I, Nilsen H (1997) Methods to evaluate fish freshness in research and industry. Trends Food Sci Technol 8(8):258–265

    Article  CAS  Google Scholar 

  71. Cheng J-H, Sun D-W, Zeng X-A, Liu D (2015) Recent advances in methods and techniques for freshness quality determination and evaluation of fish and fish fillets: a review. Crit Rev Food Sci Nutr 55(7):1012–1225

    Article  Google Scholar 

  72. Castro P, Millán R, Penedo JC, Sanjuán E, Santana A, Caballero MJ (2012) Effect of storage conditions on total volatile base nitrogen determinations in fish muscle extracts. J Aquat Food Prod Technol 21(5):519–523

    Article  CAS  Google Scholar 

  73. Canizales-Rodríguez DF, Ocaño-Higuera VM, Marquez-Rios E, Graciano-Verdugo AZ, Cárdenas-López JL, Yepiz-Gómez MS, Castillo-Yáñez FJ (2013) Biochemical, physical, chemical, and microbiological assessment of blue shrimp (Litopenaeus stylirostris) stored in ice. J Aquat Food Prod Technol 24(3):259–269

    Article  CAS  Google Scholar 

  74. Ding R, Huang X, Han F, Dai H, Teye E, Xu F (2014) Rapid and nondestructive evaluation of fish freshness by near infrared reflectance spectroscopy combined with chemometrics analysis. Anal Methods 6(24):9675–9683

    Article  Google Scholar 

  75. Feng Y-Z, Sun D-W (2012) Application of hyperspectral imaging in food safety inspection and control: a review. Crit Rev Food Sci Nutr 52(11):1039–1058

    Article  Google Scholar 

  76. Wu D, Sun D-W (2013) Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: a review-Part I: Fundamentals. Innov Food Sci Emerg Technol 19:1–14

    Article  CAS  Google Scholar 

  77. Elmasry G, Kamruzzaman M, Sun D-W, Allen P (2012) Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Crit Rev Food Sci Nutr 52(11):999–1023

    Article  Google Scholar 

  78. Dale LM, Thewis A, Boudry C, Rotar I, Dardenne P, Baeten V, Pierna JAF (2013) Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Appl Spectrosc Rev 48(2):142–159

    Article  CAS  Google Scholar 

  79. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5(4):1121–1142

    Article  Google Scholar 

  80. Xiong Z, Sun D-W, Zeng X-A, Xie A (2014) Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: a review. J Food Eng 132:1–13

    Article  CAS  Google Scholar 

  81. He H-J, Wu D, Sun D-W (2013) Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging. Innov Food Sci Emerg Technol 18:237–245

    Article  Google Scholar 

  82. Zhu F, Zhang H, Shao Y, He Y, Ngadi M (2013) Mapping of fat and moisture distribution in Atlantic salmon using near-infrared hyperspectral imaging. Food Bioprocess Technol 7(4):1208–1214

    Article  CAS  Google Scholar 

  83. He H-J, Wu D, Sun D-W (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 156:394–401

    Article  CAS  Google Scholar 

  84. Xu J-L, Riccioli C, Sun D-W (2016) Efficient integration of particle analysis in hyperspectral imaging for rapid assessment of oxidative degradation in salmon fillet. J Food Eng 169:259–271

    Article  CAS  Google Scholar 

  85. Cheng J-H, Sun D-W, Zeng X-A, Pu H-B (2014) Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging. Innov Food Sci Emerg Technol 21:179–187

    Article  CAS  Google Scholar 

  86. Cheng J-H, Sun D-W, Pu HB, Wang Q-J, Chen Y-N (2015) Suitability of hyperspectral imaging for rapid evaluation of thiobarbituric acid (TBA) value in grass carp (Ctenopharyngodon idella) fillet. Food Chem 171:258–265

    Article  CAS  Google Scholar 

  87. Cheng J-H, Sun D-W, Pu H, Zhu Z (2015) Development of hyperspectral imaging coupled with chemometric analysis to monitor K value for evaluation of chemical spoilage in fish fillets. Food Chem 185:245–253

    Article  CAS  Google Scholar 

  88. Dissing BS, Nielsen ME, Ersbøll BK, Frosch S (2011) Multispectral imaging for determination of astaxanthin concentration in salmonids. PLoS ONE 6(5):e19032

    Article  CAS  Google Scholar 

  89. Erkan N (2012) The effect of thyme and garlic oil on the preservation of vacuum-packaged hot smoked rainbow trout (Oncorhynchus mykiss). Food Bioprocess Technol 5(4):1246–1254

    Article  CAS  Google Scholar 

  90. Prieto N, Roehe R, Lavín P, Batten G, Andrés S (2009) Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Sci 83(2):175–186

    Article  CAS  Google Scholar 

  91. ElMasry G, Sun D-W, Allen P (2012) Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef. J Food Eng 110(1):127–140

    Article  Google Scholar 

  92. Kobayashi K-I, Matsui Y, Maebuchi Y, Toyota T, Nakauchi S (2010) Near infrared spectroscopy and hyperspectral imaging for prediction and visualisation of fat and fatty acid content in intact raw beef cuts. J Near Infrared Spectrosc 18(5):301

    Article  CAS  Google Scholar 

  93. ElMasry G, Sun D-W, Allen P (2013) Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging. J Food Eng 117(2):235–246

    Article  CAS  Google Scholar 

  94. Barbin DF, ElMasry G, Sun D-W, Allen P (2012) Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. Anal Chim Acta 719:30–42

    Article  CAS  Google Scholar 

  95. Liu D, Pu H, Sun D-W, Wang L, Zeng X-A (2014) Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. Food Chem 160:330–337

    Article  CAS  Google Scholar 

  96. Xie A, Sun D-W, Xu Z, Zhu Z (2015) Rapid detection of frozen pork quality without thawing by Vis–NIR hyperspectral imaging technique. Talanta 139:208–215

    Article  CAS  Google Scholar 

  97. Barbin DF, ElMasry G, Sun D-W, Allen P (2013) Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chem 138(2):1162–1171

    Article  CAS  Google Scholar 

  98. Liu L, Ngadi M (2014) Predicting intramuscular fat content of pork using hyperspectral imaging. J Food Eng 134:16–23

    Article  Google Scholar 

  99. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2012) Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. Anal Chim Acta 714:57–67

    Article  CAS  Google Scholar 

  100. Kamruzzaman M, ElMasry G, Sun D-W, Allen P (2012) Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression. Innov Food Sci Emerg Technol 16:218–226

    Article  CAS  Google Scholar 

  101. Kandpal LM, Lee H, Kim MS, Mo C, Cho B-K (2013) Hyperspectral reflectance imaging technique for visualization of moisture distribution in cooked chicken breast. Sensors (Basel) 13(10):13289–13300

    Article  Google Scholar 

  102. Xiong Z, Sun D-W, Xie A, Han Z, Wang L (2015) Potential of hyperspectral imaging for rapid prediction of hydroxyproline content in chicken meat. Food Chem 175:417–422

    Article  CAS  Google Scholar 

  103. Xiong Z, Sun D-W, Pu H, Xie A, Han Z, Luo M (2015) Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. Food Chem 179:175–181

    Article  CAS  Google Scholar 

  104. Xiong Z, Sun D-W, Xie A, Pu H, Han Z, Luo M (2015) Quantitative determination of total pigments in red meats using hyperspectral imaging and multivariate analysis. Food Chem 178:339–345

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This research was supported by the financial support of the Natural Science Foundation of Guangdong Province (2014A030313244), the International S&T Cooperation Program of China (2015DFA71150BC), the Key Projects of Administration of Ocean and Fisheries of Guangdong Province (A201401C04), the Collaborative Innovation Major Special Projects of Guangzhou City (201508020097), and the International S&T Cooperation Program of Guangdong Province (2013B051000010). The authors also gratefully acknowledge the financial support of Guangdong Province Government (China) through the program “Leading Talent of Guangdong Province (Da-Wen Sun)” and the financial support provided by China Scholarship Council (CSC) for supporting Jun-Hu Cheng’s PhD study at KU Leuven in Belgium.

Author information

Authors and Affiliations

  1. School of Food Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Jun-Hu Cheng & Da-Wen Sun

  2. Academy of Contemporary Food Engineering, Guangzhou Higher Education Mega Center, South China University of Technology, Guangzhou, 510006, China

    Jun-Hu Cheng & Da-Wen Sun

  3. MeBioS, Department of Biosystems, KU Leuven, Willem de Croylaan 42, 3001, Heverlee, Belgium

    Jun-Hu Cheng

  4. Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

    Da-Wen Sun

Authors
  1. Jun-Hu Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Da-Wen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Da-Wen Sun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, JH., Sun, DW. Partial Least Squares Regression (PLSR) Applied to NIR and HSI Spectral Data Modeling to Predict Chemical Properties of Fish Muscle. Food Eng Rev 9, 36–49 (2017). https://doi.org/10.1007/s12393-016-9147-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12393-016-9147-1

Keywords

Search

Navigation