ArticleFood science & nutrition2020
A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network.
Article in Food science & nutrition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction of Storage Quality and Multi-Objective Optimization of Storage Conditions for FreshFoods (Basel, Switzerland) · 2025Article
- Vis/NIR Spectroscopy and Vis/NIR Hyperspectral Imaging for Non-Destructive Monitoring of Apricot Fruit Internal Quality with Machine Learning.Foods (Basel, Switzerland) · 2025Article
- Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops.PloS one · 2025Article
- A Performance Evaluation of Two Hyperspectral Imaging Systems for the Prediction of Strawberries' Pomological Traits.Sensors (Basel, Switzerland) · 2023Article
- Discrimination of Inner Injury of Korla Fragrant Pear Based on Multi-Electrical Parameters.Foods (Basel, Switzerland) · 2023Article
- Plant impedance spectroscopy: a review of modeling approaches and applications.Frontiers in plant science · 2023Review
- Using artificial neural network in predicting the key fruit quality of loquat.Food science & nutrition · 2021Article
- A nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network.Food science & nutrition · 2020Article
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Authors and funding
7 authors.
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Abstract
The detection of soluble solid content in Korla fragrant pear is a destructive and time-consuming endeavor. In effort to remedy this, a nondestructive testing method based on electrical properties and artificial neural network was established in this study. Specifically, variations of electrical properties (e.g., equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance, and equivalent parallel inductance) of Korla fragrant pears with accumulated temperature were tested using a workbench developed by ourselves. After that the characteristic variables of electrical properties were constructed by principal component analysis (PCA). In addition, three models were constructed to predict SSC in Korla fragrant pears based on the characteristic variables: general regression neural network (GRNN), back-propagation neural network (BPNN), and adaptive network fuzzy inference system (ANFIS). The results indicated that the GRNN model has the best prediction effects of SSC (
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