Food and Bioprocess Technology

, Volume 12, Issue 11, pp 1928–1937 | Cite as

Laser-Based imaging for Cocoa Pods Maturity Detection

  • Nuradila Athirah Lockman
  • Norhashila HashimEmail author
  • Daniel I. Onwude
Original Paper


Non-destructive and laser-based technologies have been explored widely in recent years as a way to monitor fresh produce and crops quality in the agriculture sector. In this study, the effectiveness of laser-induced backscattering imaging (LLBI) was investigated to determine the firmness and colour of cocoa pods at different maturity stages. The LLBI system with 1 mm laser diode beam diameter emitting at 658 nm and 705 nm wavelengths were used to capture backscattered images of Theobroma cacao at three different maturity stages, which were unripe, ripe and over-ripe. The samples were also measured using reference measurement such as colorimeter and handheld penetrometer for measuring colour and firmness, respectively, in order to compare with the LLBI. Results indicated that chroma (C) regressed linearly well with the backscattering parameters with a coefficient of determination (R2) of 0.755 for 658 nm and 0.800 for 705 nm. Classification of samples according to their maturity stages resulted in 90% correctly classified samples into an unripe group using a laser diode at 658 nm and 95% at 705 nm. These findings also revealed that LLBI with laser diode emitted light at 705 nm wavelength gave better evaluation and classification results compared with 658 nm. This study has demonstrated the ability of non-destructive LLBI technique to evaluate the maturity stages of cocoa pods.


Laser-based imaging Backscattering imaging Fruit maturity Cocoa Non-destructive technique 



The authors wish to acknowledge the Universiti Putra Malaysia for their support in facilities assistance received for this study.

Funding Information

This work was supported by Universiti Putra Malaysia.


  1. Adebayo, S. E., Hashim, N., Abdan, K., Hanafi, M., & Mollazade, K. (2016). Prediction of quality attributes and ripeness classification of bananas using optical properties. Scientia Horticulturae, 212, 171–182. Scholar
  2. Baranyai, L., & Zude, M. (2009). Analysis of laser light propagation in kiwifruit using backscattering imaging and Monte Carlo simulation. Computers and Electronics in Agriculture, 69(1), 33–39.CrossRefGoogle Scholar
  3. Barrett, D. M., Beaulieu, J. C., & Shewfelt, R. (2010). Color, flavor, texture, and nutritional quality of fresh-cut fruits and vegetables: desirable levels, instrumental and sensory measurement, and the effects of processing. Critical Reviews in Food Science and Nutrition, 50(5), 369–389. Scholar
  4. Bean, R. (2014). Lighting: interior and exterior. Taylor & Francis Retrieved from
  5. Cubillos, A. F., García, M., & M. C., Calvo S, A. M., Carvajal R, G. H., & Tarazona-Díaz, M. P. (2019). Study of the physical and chemical changes during the maturation of three cocoa clones, EET8, CCNN51 and ICS60. Journal of the Science of Food and Agriculture. Scholar
  6. Daymond, A. J., & Hadley, P. (2008). Differential effects of temperature on fruit development and bean quality of contrasting genotypes of cacao (Theobroma cacao). Annals of Applied Biology, 153, 175–185.Google Scholar
  7. Deheuvels, O., Avelino, J., Somarriba, E., & Malezieux, E. (2012). Vegetation structure and productivity in cocoa-based agroforestry systems in Talamanca, Costa Rica. Agriculture, Ecosystems & Environment, 149, 181–188.CrossRefGoogle Scholar
  8. Han, D., Liu, X., & Lu, C. (2006). Optical-nondestructive detection of breakdown apples. Transactions of The Chinese Society of Agricultural Machinery, 86–89.Google Scholar
  9. Hashim, N., Janius, R. B., Baranyai, L., Abdul Rahman, R., Osman, A., & Zude, M. (2012). Kinetic model for colour changes in bananas during the appearance of chilling injury symptoms. Food and Bioprocess Technology, 5, 2952–2963.CrossRefGoogle Scholar
  10. Hashim, N., Pflanz, M., Regen, C., Janius, R. B., Abdul Rahman, R., Osman, A., Shitan, M., & Zude, M. (2013). An approach for monitoring the chilling injury appearance in bananas by means of backscattering imaging. Journal of Food Engineering, 116, 28–36.CrossRefGoogle Scholar
  11. Hashim, N., Janius, R. B., Abdul Rahman, R., Osman, A., Shitan, M., & Zude, M. (2014). Changes of backscattering parameters during chilling injury in Bananas. Journal of Engineering Science and Technology, 9(3), 314–325.Google Scholar
  12. Hashim, N., Adebayo, S. E., Abdan, K., & Hanafi, M. (2018). Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biology and Technology, 135, 38–50.CrossRefGoogle Scholar
  13. Hii, C. L., Law, C. L., Suzannah, S., & Misnawi, & Cloke, M. (2009). Polyphenols in cocoa (Theobroma cacao L.). Asian Journal of Food and Agro-Industry, 2(04), 702–722.Google Scholar
  14. International Cocoa Organization (ICCO). (2019). Quarterly Bulletin of Cocoa Statistics, Vol. XLV - No. 1. Retrieved from
  15. Jha, S. N., Chopra, S., & Kingsly, A. R. P. (2007). Modeling of color values for nondestructive evaluation of maturity of mango. Journal of Food Engineering, 78(1), 22–26. Scholar
  16. Lopez, A., & Dimick, P. (1995). Cocoa fermentation. Biotechnology, 9, 561–577.Google Scholar
  17. Lu, R., & Peng, Y. (2006). Hyperspectral scattering for assessing peach fruit firmness. Biosystems Engineering, 93(2), 161–171.CrossRefGoogle Scholar
  18. Marty-terrade, S., & Marangoni, A. G. (2012). Impact of cocoa butter origin on crystal behavior. Cocoa Butter and Related Compounds, 245–274. Scholar
  19. Mohammadi, V., Kheiralipour, K., & Ghasemi-Varnamkhasti, M. (2015). Detecting maturity of persimmon fruit based on image processing technique. Scientia Horticulturae, 184, 123–128. Scholar
  20. Mohd Ali, M., Hashim, N., Bejo, S. K., & Shamsudin, R. (2017). Quality evaluation of watermelon using laser-induced backscattering imaging during storage. Postharvest Biology and Technology, 123, 51–59.CrossRefGoogle Scholar
  21. Mollazade, K., Omid, M., Akhlaghian Tab, F., Kalaj, Y. R., Mohtasebi, S. S., & Zude, M. (2013). Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Computers and Electronics in Agriculture, 98, 34–45.CrossRefGoogle Scholar
  22. Motamayor, J. C., Mockaitis, K., Schmutz, J., Haiminen, N., Iii, D. L., Podicheti, R., et al. (2013). The genome sequence of the most widely cultivated cacao type and its use to identify candidate genes regulating pod color the genome sequence of the most widely cultivated cacao type and its use to identify candidate genes regulating pod color. Genome Biology, 14(6), r53. Scholar
  23. Nguyen, V. T. (2015). Mass proportion, proximate composition and effects of solvents and extraction parameters on pigment yield from cacao pod shell (Theobroma Cacao L.). Journal of Food Processing and Preservation, 39, 1414–1420.CrossRefGoogle Scholar
  24. Onwude, D. I., Hashim, N., Abdan, K., & Chen, G. (2018). Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying. Computers and Electronics in Agriculture, 150, 178–187. Scholar
  25. Ozturk, G., & Young, G. M. (2017). Food evolution: the impact of society and science on the fermentation of cocoa beans. Comprehensive Reviews in Food Science and Food Safety, 16, 431–455.CrossRefGoogle Scholar
  26. Pathare, P. B., Opara, U. L., & Al-Said, F. A.-J. (2012). Colour measurement and analysis in fresh and processed foods: a review. Food and Bioprocess Technology, 6(1), 36–60. Scholar
  27. Peng, Y., & Lu, R. (2008). Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology, 48, 52–62.CrossRefGoogle Scholar
  28. Prameela, K. P. (1997). Effect of weather on cocoa and improvement of bean size through seasonal crop orientation (Doctoral dissertation). Retrieved from
  29. Prasanna, V., Prabha, T. N., & Tharanathan, R. N. (2007). Fruit ripening phenomena--an overview. Critical Reviews in Food Science and Nutrition, 47(1), 1–19.CrossRefGoogle Scholar
  30. Qin, J., & Lu, R. (2008). Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology, 49(3), 355–365.CrossRefGoogle Scholar
  31. Romano, G., Baranyai, L., Gottschalk, K., & Zude, M. (2008). An approach for monitoring the moisture content changes of drying banana slices with laser light backscattering imaging. Food and Bioprocess Technology, 1, 410–414.CrossRefGoogle Scholar
  32. Romano, G., Nagle, M., Argyropoulos, D., & Müller, J. (2011). Laser light backscattering to monitor moisture content, soluble solid content and hardness of apple tissue during drying. Journal of Food Engineering, 104, 657–662.CrossRefGoogle Scholar
  33. Ruzaidi, A., Maleyki, A., Amin, I., Nawalyah, A. G., & Muhajir, H. (2008). Hypoglycaemic properties of Malaysian cocoa (Theobroma cacao). International Food Research Journal, 15(3), 305–312.Google Scholar
  34. Saputro, A. H., Juansyah, S. D., & Handayani, W. (2018). Banana (Musa sp.) maturity prediction system based on chlorophyll content using visible-NIR imaging. In 2018 International Conference on Signals and Systems (ICSigSys) (pp. 64–68).
  35. Udomkun, P., Nagle, M., Argyropoulos, D., Mahayothee, B., & Müller, J. (2016). Multi-sensor approach to improve optical monitoring of papaya shrinkage during drying. Journal of Food Engineering, 189, 82–89.CrossRefGoogle Scholar
  36. Wang, L. Y., Zhang, D. X., Zhang, H. J., & Wang, X. P. (2008). Measurement of fruit maturity based on laser-induced photoluminescence spectrum. Spectroscopy and Spectral Analysis, 28(12), 2772–2776.PubMedGoogle Scholar
  37. Wills, R. B. H., McGlasson, W. B., Graham, D., & Joyce, D. C. (1998). Postharvest: an introduction to the physiology and handling of fruits, vegetables, and ornamentals. Oxfordshire: CABI.Google Scholar
  38. Wood, G., & Lass, R. (1985). Cocoa (4th ed.). New York: Longman.Google Scholar
  39. Zulkifli, A., Hashim, N., Abdan, K., & Hanafi, M. (2016). Evaluation of physicochemical properties of Musa Acuminata cv. Berangan at different ripening stages. International Food Research Journal, 23(Suppl), S97–S100.Google Scholar
  40. Zulkifli, A., Hashim, N., Abdan, K., & Hanafi, M. (2019). Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas. Computers and Electronics in Agriculture, 160, 100–107.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biological and Agricultural Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia
  2. 2.SMART Farming Technology Research Centre (SFTRC), Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia
  3. 3.Department of Agricultural and Food Engineering, Faculty of EngineeringUniversity of UyoUyoNigeria

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