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Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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Inner Properties Estimation of Gala Apple Using Spectral Data and Two Statistical and Artificial Intelligence Based

Vali Rasooli Sharabiani¹, Sajad Sabzi¹, Razieh Pourdarbani¹

¹Department of Biosystems Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.

Foods (Basel, Switzerland)

|December 24, 2021

Summary

This study introduces a non-destructive spectral method for estimating Gala apple quality. The hybrid artificial neural network (ANN-ICA) accurately predicts total soluble solids (TSS) and BrimA, enabling efficient fruit analysis.

Keywords:

apple artificial neural network non-destructive prediction optimization algorithm ripening spectroscopy

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Area of Science:

Agricultural Science
Spectroscopy
Data Science

Background:

Conventional methods for fruit quality assessment are destructive and time-consuming.
Non-destructive methods are crucial for real-time quality control in the fruit industry.
Spectral analysis offers a promising avenue for non-destructive fruit property determination.

Purpose of the Study:

To develop and validate a non-destructive method for estimating total soluble solids (TSS) and BrimA in Gala apples.
To utilize spectral data in the 200-1100 nm range for fruit quality assessment.
To compare the efficacy of different algorithms for predicting fruit chemical properties.

Main Methods:

Collected Gala apple samples at various maturity stages.
Extracted and pre-processed spectral data (200-1100 nm).
Employed artificial neural network-simulated annealing (ANN-SA) for optimal wavelength selection and partial least squares regression (PLSR) and artificial neural network-imperialist competitive algorithm (ANN-ICA) for property estimation.

Main Results:

The ANN-ICA model demonstrated high accuracy in predicting TSS (correlation coefficient: 0.963) and BrimA (correlation coefficient: 0.965).
Low root mean squared errors (0.167% for TSS, 0.596% for BrimA) indicate reliable estimations.
The ANN-ICA algorithm, repeated 500 times, confirmed its validity and robustness.

Conclusions:

Non-destructive spectral analysis, particularly using the ANN-ICA model, is effective for accurately estimating TSS and BrimA in Gala apples.
This method offers a viable alternative to destructive testing for fruit quality assessment.
The findings support the integration of spectral technology for efficient and precise fruit quality management.