Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

S.E Adebayo,N Hashim

doi:10.26656/fr.2017.5(s1).022

Abstract

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

Highlights

Quantification of fruit qualities relied on various destructive techniques that require the removal of a little quantity of fruit tissue and juicing for the measurement of solids content (SSC), total acidity, and nutritional content (Hoehn et al, 2003; Liu et al, 2010; Wold et al, 2004)
Since optical properties relate to fruit component and structure and these undergo significant changes during fruit development
Fruit components such as anthocyanins, chlorophyll and water content absorbed light photon at a different wavelength and as the fruit develops the amount of these components either reduces or increases thereby alters the optical properties of the fruit

Summary

Introduction

Quantification of fruit qualities relied on various destructive techniques that require the removal of a little quantity of fruit tissue and juicing for the measurement of SSC, total acidity, and nutritional content (Hoehn et al, 2003; Liu et al, 2010; Wold et al, 2004). These techniques resulted in a large amount of postharvest losses and inability to measure the whole batch as few samples from the batches are used for the measurement, which it involves more man-hours to carry them out. Human evaluation remains the most widely used method of fruit quality assessment It has been established, that as a result of disparity in colour prejudice between individuals, eye fatigue, personal bias, lack of colour memory, and different lighting conditions often resulted in varying assessments. Visible, near-infrared, infrared, and x-ray radiations of the electromagnetic spectrum

Methods

Results

Conclusion