American Society of Agricultural and Biological Engineers



Click on “Download PDF” for the PDF version or on the title for the HTML version.


If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Assessing the sensitivity and robustness of prediction models for apple firmness using spectral scattering technique

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  131648152,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: http://dx.doi.org/10.13031/aim.20131648152) @2013
Authors:   Fernando A. Mendoza, Renfu Lu, Qibing Zhu
Keywords:   Apples firmness spectral scattering calibration sensitivity of prediction models.

Abstract. Spectral scattering is useful for nondestructive sensing of fruit firmness. Prediction models, however, are typically built using multivariate statistical methods such as partial least squares regression (PLSR), whose performance generally depends on the characteristics of the data. The aim of this research was to evaluate the influence of range of variability for Magness-Taylor firmness data (i.e., number of samples at 100%, 80% and 60% of the total variability), preprocessing method [mean reflectance and continuous wavelet transform (CWT) decomposition], and harvest season (2009 and 2010) on the performance and robustness of the calibration models for predicting the firmness of 'Jonagold', ‘Golden Delicious', and 'Delicious' apples. A 3×22 mixed factorial experimental design with six replicates per run was used for assessing PLSR models for the spectral scattering data. The same prediction set of apple samples for each replication was tested for the models. The main effects and interactions for the three variables, and their polynomial models were calculated based on the number of latent variables needed for the model building and the standard error of prediction (SEP). Overall results showed that models using large datasets needed a larger number of latent variables and produced smaller SEP values than the small sample dataset models for all apple cultivars. Models preprocessed by mean reflectance method resulted in a smaller number of variables than the models preprocessed by CWT. The results also demonstrated that increasing the number of samples used in the calibration set resulted in decreases in the SEP, although the largest decrease was observed between 350 and 400 samples. It is, therefore, recommended that the number of samples be chosen according to the accuracy required, although 400 apple samples was considered appropriate in this study to establish calibration models for firmness with lower prediction errors.

(Download PDF)    (Export to EndNotes)