Skip to main content
SpringerLink
Log in

Near infrared spectroscopy to predict bitter pit development in different varieties of apples

  • Original Paper
  • Published:
Journal of Food Measurement and Characterization Aims and scope Submit manuscript

Abstract

Bitter pit is a physiological disorder in apples. Several major apple varieties are susceptible to this disorder that poses a great challenge to growers and the associated industry as it significantly reduces the produce utilization value and marketability. The current method of bitter pit detection is through visual assessment of symptoms. Near infrared (NIR) spectroscopy is a non-invasive technique that can be utilized towards detecting bitter pit development in fruits in pre-/non-symptomatic stages. Therefore, NIR spectra (935–2500 nm) of apples were collected from healthy and bitter pit Honeycrisp, Golden Delicious, and Granny Smith apples from a commercial orchard. The apples were stored in a controlled environment and spectral reflectance data were acquired at days 0, 7, 14, 35, and 63 after harvest. Chemical analysis was performed at the end of the storage period to estimate calcium, magnesium, and potassium content in the fruit peel. Partial least square regression (PLSR) was used to identify the apples as healthy or bitter pit using NIR-based spectral features. In addition, specific spectral features were selected by implementing two feature extraction methods: PLSR and stepwise discriminant analysis (SDA) on day 63 spectral dataset. The PLSR and SDA-based selected features from day 63 in Honeycrisp apples classified the same dataset with classification accuracies of about 100% with both methods. Regression analysis indicated a strong relationship between the PLSR-based spectral features and magnesium-to-calcium ratio in fruit peel in all three (Honeycrisp, Golden Delicious, and Granny Smith) apple varieties.

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. A. Miqueloto, C.V.T. do Amarante, C.A. Steffens, A. dos Santos, E. Mitcham, Relationship between xylem functionality, calcium content and the incidence of bitter pit in apple fruit. Sci. Hortic. 165, 319–323 (2014)

    Article  CAS  Google Scholar 

  2. B.M. Nicolaï, E. Lötze, A. Peirs, N. Scheerlinck, K.I. Theron, Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol. Technol. 40(1), 1–6 (2006)

    Article  Google Scholar 

  3. E. Lötze, C. Huybrechts, A. Sadie, K.I. Theron, R.M. Valcke, Fluorescence imaging as a non-destructive method for pre-harvest detection of bitter pit in apple fruit (Malus domestica Borkh.). Postharvest Biol. Technol. 40(3), 287–294 (2006)

    Article  Google Scholar 

  4. R. Sharma, R. Pal, D. Singh, J. Singh, M. Dhiman, M. Rana, Relationships between storage disorders and fruit calcium contents, lipoxygenase activity, and rates of ethylene evolution and respiration in ‘Royal Delicious’ apple (Malus × domestica Borkh.). J. Hortic. Sci. Biotechnol. 87(4), 367–373 (2012)

    Article  CAS  Google Scholar 

  5. S.T. de Freitas, C.V. do Amarante, J.M. Labavitch, E.J. Mitcham, Cellular approach to understand bitter pit development in apple fruit. Postharvest Biol. Technol. 57(1), 6–13 (2010)

    Article  Google Scholar 

  6. R. Gutiérrez, Efecto de la ubicación en altitud y latitud de huertos de manzanos cv. Braeburn y Red Chief sobre la madurez de los frutos, la incidencia de bitter pit y en la capacidad de predicción mediante la infiltración de frutos con sales de magnesio, (2002).

  7. U.-N. U.S. Department of Agriculture, -National Agricultural Statistics Service, USDA-NASS, (2013).

  8. B. Zarcinas, B. Cartwright, L. Spouncer, Nitric acid digestion and multi-element analysis of plant material by inductively coupled plasma spectrometry, Commun. Soil Sci. Plant. Anal. 18(1), 131–146 (1987)

    Article  CAS  Google Scholar 

  9. G. ElMasry, N. Wang, C. Vigneault, J. Qiao, A. ElSayed, Early detection of apple bruises on different background colors using hyperspectral imaging, LWT-Food Sci. Technol. 41(2), 337–345 (2008).

    Article  CAS  Google Scholar 

  10. S. Jarolmasjed, C.Z. Espinoza, S. Sankaran, L.R. Khot, Postharvest bitter pit detection and progression evaluation in ‘Honeycrisp’apples using computed tomography images. Postharvest Biol. Technol. 118, 35–42 (2016)

    Article  Google Scholar 

  11. N. Shetty, R. Gislum, Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR. Field Crops Res. 120(1), 31–37 (2011)

    Article  Google Scholar 

  12. C. Camps, C. Simone, C. Gilli, Assessment of tomato quality using portable NIR spectroscopy and PLSR with wavelengths selection, XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on 936, 437–442 (2010)

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

    Article  Google Scholar 

  14. P. Rajkumar, N. Wang, G. EImasry, G. Raghavan, Y. Gariepy, Studies on banana fruit quality and maturity stages using hyperspectral imaging. J. Food Eng. 108(1), 194–200 (2012)

    Article  Google Scholar 

  15. X. Zhang, F. Liu, Y. He, X. Gong, Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging, Biosyst. Eng. 115(1), 56–65 (2013)

    Article  Google Scholar 

  16. A.G. Frenich, D. Jouan-Rimbaud, D. Massart, S. Kuttatharmmakul, M.M. Galera, J.M. Vidal, Wavelength selection method for multicomponent spectrophotometric determinations using partial least squares. Analyst 120(12), 2787–2792 (1995)

    Article  CAS  Google Scholar 

  17. D.J. Krusienski, E.W. Sellers, D.J. McFarland, T.M. Vaughan, J.R. Wolpaw, Toward enhanced P300 speller performance. J. Neurosci. Methods 167(1), 15–21 (2008)

    Article  CAS  Google Scholar 

  18. M.H. Siddiqi, S.-W. Lee, A.M. Khan, Weed image classification using wavelet transform, stepwise linear discriminant analysis, and support vector machines for an automatic spray control system, J. Inf. Sci. Eng. 30(4), 1227–1244 (2014)

    Google Scholar 

  19. G.K. Kafle, L.R. Khot, S. Jarolmasjed, S. Yongsheng, K. Lewis, Robustness of near infrared spectroscopy based spectral features for non-destructive bitter pit detection in honeycrisp apples. Postharvest Biol. Technol. 120, 188–192 (2016)

    Article  Google Scholar 

  20. A. Polesello, R. Giangiacomo, G.G. Dull, Application of near infrared spectrophotometry to the nondestructive analysis of foods: a review of experimental results, Crit. Rev. Food Sci. Nutr. 18(3), 203–230 (1983)

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

This activity was funded by Washington State Department of Agriculture (WSDA)–Specialty Crop Block Grant Program (SCBGP). In addition, the activity was partly supported by the USDA National Institute for Food and Agriculture Hatch Projects WNP00821. We would also like to thank Dr. Lav R. Khot, Dr. Jianfeng Zhou, and Ming Li as well as fruit growers; Borton Fruit, Dave Hovde, and Dan Bowton for their help during this study.

Author information

Authors and Affiliations

  1. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA

    Sanaz Jarolmasjed, Carlos Zúñiga Espinoza & Sindhuja Sankaran

Authors
  1. Sanaz Jarolmasjed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Zúñiga Espinoza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sindhuja Sankaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sindhuja Sankaran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jarolmasjed, S., Zúñiga Espinoza, C. & Sankaran, S. Near infrared spectroscopy to predict bitter pit development in different varieties of apples. Food Measure 11, 987–993 (2017). https://doi.org/10.1007/s11694-017-9473-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11694-017-9473-x

Keywords

Search

Navigation