Food Analytical Methods

, Volume 6, Issue 3, pp 826–837 | Cite as

Application of NIRS for Nondestructive Measurement of Quality Parameters in Intact Oranges During On-Tree Ripening and at Harvest

Article

Abstract

External and internal quality parameters were measured in oranges (Citrus sinensis (L.) Osbeck cv. “Powell Summer Navel”) during on-tree ripening and at harvest using near-infrared reflectance (NIR) spectroscopy. The performance of two NIRS instruments was evaluated: a handheld microelectromechanical system spectrophotometer working in the 1,600- to 2,400-nm range, and a diode array visible–NIR spectrophotometer working in the 380- to 1,700-nm range. Spectra and analytical data were used to construct MPLS prediction models for quantifying weight, size (equatorial and axial diameters), color (L*, a*, b*, C*, h*, and color index), texture (firmness and maximum penetration force), yield (pericarp thickness, juice weight, and juice content), and chemical parameters (soluble solids content, pH, titratable acidity, and maturity index). Both instruments yielded promising results for on-tree and at-harvest quality measurements, but models constructed using the diode array instrument provided greater predictive capacity, particularly for fruit size (equatorial and axial diameters) and total soluble solids content. Subsequent evaluation of the LOCAL algorithm revealed that it increased the predictive capacity of models constructed for all the main parameters tested. These results confirm that noninvasive NIRS technology can be used to simultaneously evaluate external and internal quality parameters in intact oranges both during on-tree ripening and at harvest, thus making it easier for farmers to monitor the ripening process and also to optimize harvest timing in order to meet the demands of the citrus-fruit industry.

Keywords

Near-IR spectroscopy Portable sensors MEMS technology Orange Quality parameters On-tree At harvest 

References

  1. Agustí M (2003) Citriculture, 2nd edn. Mundi-Prensa, MadridGoogle Scholar
  2. Barnes RJ, Dhanoa MS, Lister SJ (1989) Standard normal variate transformation and de-trending of near infrared diffuse reflectance spectra. Appl Spectrosc 43:772–777CrossRefGoogle Scholar
  3. Barton FE II, Shenk JS, Westerhaus MO, Funk DB (2000) The development of near infrared wheat quality models by locally weighted regressions. J Near Infrared Spectrosc 8:201–208CrossRefGoogle Scholar
  4. Cayuela JA (2008) Vis/NIR soluble solids prediction in intact oranges (Citrus sinensis L.) cv. Valencia Late by reflectance. Postharvest Biol Technol 47:75–80CrossRefGoogle Scholar
  5. Cayuela JA, Weiland C (2010) Intact orange quality prediction with two portable NIR spectrometers. Postharvest Biol Technol 58:113–120CrossRefGoogle Scholar
  6. González-Caballero V, Sánchez MT, López MI, Pérez-Marín D (2010) First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery. J Food Eng 101:158–165CrossRefGoogle Scholar
  7. ISI (2000) The complete software solution using a single screen for routine analysis, robust calibrations, and networking. Manual. FOSS NIRSystems/TECATOR, Infrasoft International, LLC, Silver SpringGoogle Scholar
  8. Kahn TL, Bier OJ, Beaver RJ (2007) New late-season navel orange varieties evaluated for quality characteristics. Calif Agr 61:138–143CrossRefGoogle Scholar
  9. Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible–NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 43:602–607CrossRefGoogle Scholar
  10. Lu HS, Xu HR, Ying YB, Fu XP, Yu HY, Tian HQ (2006) Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits. J Zhejiang Univ-Sc B 7:794–799CrossRefGoogle Scholar
  11. Miller WM, Zude-Sasse M (2004) NIR-based sensing to identify soluble solids content of Florida citrus. Appl Eng Agr 20:321–327Google Scholar
  12. Nicolaï 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 46:99–118CrossRefGoogle Scholar
  13. Obenland D, Collin S, Sievert J, Fjeld K, Doctor J, Arpaia ML (2008) Commercial packing and storage of navel oranges alters aroma volatiles and reduces flavor quality. Postharvest Biol Technol 47:159–167CrossRefGoogle Scholar
  14. Paz P, Sanchez MT, Pérez-Marín D, Guerrero JE, Garrido-Varo A (2008) Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy. J Agr Food Chem 56:2565–2570CrossRefGoogle Scholar
  15. Paz P, Sanchez MT, Pérez-Marín D, Guerrero JE, Garrido A (2009) Evaluating NIR instruments for quantitative assessment of intact apple quality. J Sci Food Agr 89:781–790CrossRefGoogle Scholar
  16. Peirs A, Scheerlinck N, Touchant K, Nicolaï BM (2002) Comparison of Fourier transform and dispersive near-infrared reflectance spectroscopy for apple quality measurements. Biosyst Eng 81:305–311CrossRefGoogle Scholar
  17. Pérez-Marín D, Garrido-Varo A, Guerrero JE (2005) Implementation of LOCAL algorithm with near-infrared spectroscopy for compliance assurance in compound feeding stuffs. Appl Spectrosc 59:69–77CrossRefGoogle Scholar
  18. Pérez-Marín D, Sánchez MT, Paz P, Soriano MA, Guerrero JE, Garrido-Varo A (2009) Non-destructive determination of quality parameters in nectarines during on-tree ripening and postharvest storage. Postharvest Biol Technol 52:180–188CrossRefGoogle Scholar
  19. Pérez-Marín D, Paz P, Guerrero JE, Garrido-Varo A, Sánchez MT (2010) Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums. J Food Eng 99:294–302CrossRefGoogle Scholar
  20. Pérez-Marín D, Sánchez MT, Paz P, González-Dugo V, Soriano MA (2011) Postharvest shelf-life discrimination of nectarines produced under different irrigation strategies using NIR-spectroscopy. LWT-Food Sci Technol 44:1405–1414CrossRefGoogle Scholar
  21. Pérez-Pérez J, Romero P, Navarro J, Botiá P (2008) Response of sweet orange cv. ‘Lane Late’ to deficit irrigation strategy in two rootstocks. II: flowering, fruit growth, yield and fruit quality. Irrigation Sci 26:519–529CrossRefGoogle Scholar
  22. Sánchez MT, Pérez-Marín D (2011) Nondestructive measurement of fruit quality by NIR spectroscopy. In: Vázquez M, Ramírez JA (eds) Advances in post-harvest treatments and fruit quality and safety. Nova, Hauppauge, pp 101–163Google Scholar
  23. Sánchez MT, De la Haba MJ, Guerrero JE, Garrido-Varo A, Pérez-Marín D (2011) Testing of a LOCAL approach for the prediction of quality parameters in intact nectarines using a portable NIRS instrument. Postharvest Biol Technol 60:130–135CrossRefGoogle Scholar
  24. Sánchez MT, De la Haba MJ, Guerrero JE, Benítez-López M, Fernández-Novales J, Garrido-Varo A, Pérez-Marín D (2012) Non-destructive characterization and quality control of intact strawberries based on NIR spectral data. J Food Eng 110:102–108CrossRefGoogle Scholar
  25. Saranwong S, Kawano S (2007) Applications to agricultural and marine products: Fruits and vegetables. In: Ozaki Y, McClure WF, Christy AA (eds) Near-infrared spectroscopy in food science and technology. Wiley, New Jersey, pp 219–242Google Scholar
  26. Shenk JS, Westerhaus MO (1991a) Population definition sample selection and calibration procedures for near infrared spectra and modified partial least squares regression. Crop Sci 31:469–474CrossRefGoogle Scholar
  27. Shenk JS, Westerhaus MO (1991b) Population structuring of near infrared spectra and modified partial least squares regression. Crop Sci 31:1548–1555CrossRefGoogle Scholar
  28. Shenk JS, Westerhaus MO (1995a) Analysis of agriculture and food products by near infrared reflectance spectroscopy. Monograph, NIRSystems, Inc, Silver SpringGoogle Scholar
  29. Shenk JS, Westerhaus MO (1995b) Routine operation, calibration, development and network system management manual. Monograph, NIRSystems, Inc, Silver SpringGoogle Scholar
  30. Shenk JS, Westerhaus MO (1996) Calibration the ISI way. In: Davies AMC, Williams PC (eds) Near infrared spectroscopy: the future waves. NIR Publications, Chichester, pp 198–202Google Scholar
  31. Shenk JS, Westerhaus MO, Berzaghi P (1997) Investigation of a LOCAL calibration procedure for near infrared instruments. J Near Infrared Spectrosc 5:223–232CrossRefGoogle Scholar
  32. Shenk JS, Workman JJ Jr, Westerhaus MO (2001) Application of NIR spectroscopy to agricultural products. In: Burns DA, Ciurczak EW (eds) Handbook of near infrared analysis, practical spectroscopy series, vol. 27, 2nd edn. Marcel Dekker, New York, pp 419–473Google Scholar
  33. Williams PC (2001) Implementation of near-infrared technology. In: Williams PC, Norris KH (eds) Near-infrared technology in the agricultural and food industries. AACC Inc, St. Paul, pp 145–169Google Scholar
  34. Zhang K, Chai Y, Yang SX, Weng D (2011a) Pre-warning analysis and application in traceability systems for food production supply chains. Expert Syst Appl 38:2500–2507CrossRefGoogle Scholar
  35. Zhang X, Feng J, Xu M, Hu J (2011b) Modeling traceability information and functionality requirement in export-oriented tilapia chain. J Sci Food Agr 91:1316–1325CrossRefGoogle Scholar
  36. Zude M, Pflanz M, Kaprielian C, Aivazian BL (2008) NIRS as a tool for precision horticulture in the citrus industry. Biosyst Eng 99:455–459CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Bromatology and Food TechnologyUniversity of CordobaCordobaSpain
  2. 2.Department of Animal ProductionUniversity of CordobaCordobaSpain

Personalised recommendations