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Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis

Author

Listed:
  • Thiago de Paula Oliveira

    (Universidade de São Paulo (USP))

  • John Hinde

    (National University of Ireland, Galway)

  • Silvio Sandoval Zocchi

    (Universidade de São Paulo (USP))

Abstract

The maturity stages of papaya fruit based on peel color are frequently characterized from a sample of four points on the equatorial region measured by a colorimeter. However, this procedure may not be suitable for assessing the papaya’s overall mean color and an alternative proposal is to use image acquisition of the whole fruit’s peel. Questions of interest are whether a sample on the equatorial region can reproduce a sample over the whole peel region and if the colorimeter can compete with a scanner, or digital camera, in measuring the mean hue over time. The reproducibility can be verified by using the concordance correlation for responses measured on a continuous scale. Thus, in this work we propose a longitudinal concordance correlation (LCC), based on a mixed-effects regression model, to estimate agreement over time among pairs of observations obtained from different combinations between measurement method and sampled peel region. The results show that the papaya’s equatorial region is not representative of the whole peel region, suggesting the use of image analysis rather than a colorimeter to measure the mean hue. Moreover, in longitudinal studies the LCC can suggest over which period the two methods are likely to be in agreement and where the simpler colorimeter method could be used. The performance of the LCC is evaluated using a small simulation study. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Thiago de Paula Oliveira & John Hinde & Silvio Sandoval Zocchi, 2018. "Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 233-254, June.
    • Handle: RePEc:spr:jagbes:v:23:y:2018:i:2:d:10.1007_s13253-018-0321-1
    DOI: 10.1007/s13253-018-0321-1
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    References listed on IDEAS

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    1. Sasiprapa Hiriote & Vernon M. Chinchilli, 2011. "Matrix-based Concordance Correlation Coefficient for Repeated Measures," Biometrics, The International Biometric Society, vol. 67(3), pages 1007-1016, September.
    2. Chen, Chia-Cheng & Barnhart, Huiman X., 2013. "Assessing agreement with intraclass correlation coefficient and concordance correlation coefficient for data with repeated measures," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 132-145.
    3. Huiman X. Barnhart & John M. Williamson, 2001. "Modeling Concordance Correlation via GEE to Evaluate Reproducibility," Biometrics, The International Biometric Society, vol. 57(3), pages 931-940, September.
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