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A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and

Haipeng Lan1, Zhentao Wang1, Hao Niu1

  • 1College of Mechanical Electrification Engineering Tarim University Alaer China.

Food Science & Nutrition
|September 30, 2020
PubMed
Summary

A new nondestructive method uses electrical properties and artificial neural networks to quickly assess soluble solid content (SSC) in Korla fragrant pears. The general regression neural network (GRNN) model demonstrated superior prediction accuracy for SSC.

Keywords:
Korla fragrant pearelectrical propertiesneural networknondestructive test

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Area of Science:

  • Agricultural Engineering
  • Food Science
  • Electrical Engineering

Background:

  • Determining soluble solid content (SSC) in Korla fragrant pears is typically destructive and time-consuming.
  • Developing a rapid, nondestructive method for SSC assessment is crucial for quality control and maturity evaluation.

Purpose of the Study:

  • To establish a nondestructive testing method for predicting SSC in Korla fragrant pears.
  • To investigate the relationship between electrical properties and accumulated temperature for SSC prediction.
  • To compare the performance of different artificial neural network models for SSC estimation.

Main Methods:

  • Variations in electrical properties (capacitance, quality factor, loss factor, resistance, impedance, inductance) of pears were measured using a custom-built workbench.
  • Principal Component Analysis (PCA) was employed to extract characteristic variables from electrical property data.
  • Three artificial intelligence models—General Regression Neural Network (GRNN), Back-Propagation Neural Network (BPNN), and Adaptive Network Fuzzy Inference System (ANFIS)—were developed to predict SSC.

Main Results:

  • The GRNN model achieved the highest prediction accuracy for SSC, with an R-squared value of 0.9743 and RMSE of 0.2584.
  • The GRNN model outperformed both the BPNN and ANFIS models in predicting SSC.
  • Electrical properties, analyzed via PCA, proved effective in characterizing pears for SSC estimation.

Conclusions:

  • A nondestructive method utilizing electrical properties and GRNN offers a viable alternative for rapid SSC assessment in Korla fragrant pears.
  • This approach facilitates efficient evaluation of pear maturity and quality.
  • The study highlights the potential of artificial neural networks in agricultural product quality assessment.