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.
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.
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