Non-Destructive Prediction of Titratable Acidity and Taste Index Properties of Gala Apple Using Combination of Different Hybrids ANN and PLSR-Model Based Spectral Data

Abstract

Non-destructive estimation of the internal properties of fruits and vegetables is very important, because better management can be provided for subsequent operations. Researchers and scientists around the world are focusing on non-destructive methods because if they are developed and commercialized, there will be an impressive change in the food industry. In this regard, this paper aims to present a non-destructive method based on Vis-NIR spectral data. The different stages of the proposed algorithm are: (1) Collection of samples of Gala apples, (2) Spectral data extraction by spectroscopy, (3) Pre-processing of spectral data, (4) Measurement of chemical properties of titratable acidity (TA) and taste index, (5) Selection of key wavelengths using hybrid artificial neural network-firefly algorithm (ANN-FA), (6) Non-destructive estimation of the properties using two methods of hybrid ANN- Particle swarm optimization algorithm and partial least squares regression. For considering the reliability of methods for estimating the chemical properties, the prediction operation was executed in 300 iterations. The results represented that the mean and standard deviation of the correlation coefficient and the root mean square error of hybrid ANN-PSO and PLSR for TA were 0.9095 ± 0.0175, 0.0598 ± 0.0064, 0.834 ± 0.0313 and 0.0761 ± 0.0061 respectively. These values for taste index were 0.918 ± 0.02, 3.2 ± 0.39, 0.836 ± 0.033 and 4.09 ± 0.403, respectively. Therefore, it can be concluded that the hybrid ANN-PSO has a better performance for non-destructive prediction of the two mentioned chemical properties than the PLSR method. In general, the proposed method can predict the chemical properties of TA and taste index non-destructively, which is very useful for mechanized harvesting and management of post-harvest operation.