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Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying

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Abstract

Colour change is a common physical phenomenon observed during drying, which need to be controlled since it directly affects consumer acceptance of dried products. This study aimed to investigate the feasibility of using computer vision, combined with laser light backscattering analysis at 650 nm in order to predict colour changes of papaya during drying. The results revealed that each image-processing factor obtained can potentially be used to describe every colour attribute change, except for chroma value. The multivariate correlations of measured backscattering parameters as well as the digital image properties were found to yield the best fitting for colour validations. Interestingly, the use of computer vision technique coupled with laser backscattering methods provides a reliable tool for quality control based on a rapid, consistent, and non-intrusive method for in-line quality measurement in established fruit drying processes.

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Abbreviations

0:

Initial value

ΔE :

Total colour difference

a*:

Intensity in green-red

A I :

Illuminated area (pixel)

b*:

Intensity in blue-yellow

C*:

Chroma

CA :

Colour attribute

CA cvs :

Colour image of papaya

CA exp :

Experimentally observed colour attribute

CA pre :

Predicted colour attribute

CR-100:

Chroma meter

CVS:

Computer vision systems

f(x) :

Converted value sR, sG, or sB

h*:

Hue (°)

I L :

Light intensity

L*:

Lightness

MAPE:

Mean absolute percentage error (%)

N :

Number of observations

PPO:

Polyphenoloxidase

RGB :

Red, green, and blue

RH:

Relative humidity (%)

R 2 :

Coefficients of determination

sR, sG, sB :

Components of sRGB

t :

Time

x :

Value of R′, G′, or B′

y image :

Image attribute

X n , Y n , Z n :

XYZ values of a reference white colour

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Acknowledgements

This research was the result of a scholarship from the Food Security Center of Universität Hohenheim, which is part of the DAAD (German Academic Exchange Service) Program “Exceed”. It was also supported by DAAD and the German Federal Ministry for Economic Cooperation and Development (BMZ). This research was also undertaken as part of the CGIAR Research Program on Agriculture for Nutrition and Health, and supported by International Institute of Tropical Agriculture (IITA). The authors gratefully acknowledge the International Fund for Agricultural Development (IFAD) and the European Commission (EC) (PJ-002057) for giving the opportunity to prepare this article. The authors also acknowledge the contributions of the “Feed the future Mozambique improved seeds for better agriculture (SEMEAR)” which supported co-authorships of the paper. We gratefully acknowledge the financial support. We are also grateful to Mrs. Ingrid Amberg and Mrs. Dorothea Hirschbach-Müller for their technical support.

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Authors and Affiliations

  1. International Institute of Tropical Agriculture (IITA), Bukavu, Democratic Republic of the Congo

    Patchimaporn Udomkun

  2. Institute of Agricultural Engineering, Tropics and Subtropics Group, Universität Hohenheim, Stuttgart, Germany

    Marcus Nagle, Dimitrios Argyropoulos & Joachim Müller

  3. International Institute of Tropical Agriculture (IITA), Nampula, Mozambique

    Alexander Nimo Wiredu

  4. Department of Food Technology, Silpakorn University, Nakhon Pathom, Thailand

    Busarakorn Mahayothee

Authors
  1. Patchimaporn Udomkun
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  2. Marcus Nagle
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  3. Dimitrios Argyropoulos
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  4. Alexander Nimo Wiredu
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  5. Busarakorn Mahayothee
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  6. Joachim Müller
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Correspondence to Patchimaporn Udomkun.

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Udomkun, P., Nagle, M., Argyropoulos, D. et al. Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying. Food Measure 11, 2142–2150 (2017). https://doi.org/10.1007/s11694-017-9598-y

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  • DOI: https://doi.org/10.1007/s11694-017-9598-y

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