Open AccessA nondestructive testing method for soluble solid content in Korla fragrant pears based on electrical properties and artificial neural network
Author(s) -
Lan Haipeng,
Wang Zhentao,
Niu Hao,
Zhang Hong,
Zhang Yongcheng,
Tang Yurong,
Liu Yang
Publication year - 2020
Publication title -
food science and nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.614
H-Index - 27
ISSN - 2048-7177
DOI - 10.1002/fsn3.1822
Subject(s) - artificial neural network , adaptive neuro fuzzy inference system , pear , biological system , nondestructive testing , computer science , artificial intelligence , principal component analysis , materials science , fuzzy logic , pattern recognition (psychology) , fuzzy control system , biology , physics , quantum mechanics , world wide web
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 ( R 2 = 0.9743, RMSE = 0.2584), superior to that of the BPNN and ANFIS models. Results facilitate a successful, alternative application for rapid assessment of SSC of the maturation stage Korla fragrant pear.