Electrochemical impedance spectroscopy for pear ripeness detection and integration with robotic manipulators DOI

Xunan Sui,

Jiawen Zou,

Zhide Geng

и другие.

Food Control, Год журнала: 2025, Номер unknown, С. 111425 - 111425

Опубликована: Май 1, 2025

Язык: Английский

Multiscale bioimpedance detection methods and modeling for dynamic non-destructive monitoring of agricultural product quality DOI
Yun Li,

Laizhao Guo,

Haonan Yang

и другие.

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 104888 - 104888

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Non-destructive prediction of apple SSC/TAC and firmness based on multilayer autoencoder and multilayer perceptron DOI Open Access
Xu Tian,

Lyuwen Huang,

Mengqun Zhai

и другие.

Intelligence & Robotics, Год журнала: 2025, Номер 5(1), С. 181 - 201

Опубликована: Фев. 25, 2025

The physical and biochemical indices of apple fruit serve as crucial phenotypic parameters in genomic cultivation. Among them, the soluble solids content (SSC), titratable acid (TAC), firmness are three most paramount that directly reflect inner quality apples. To achieve a more accurate prediction internal physicochemical indicators, novel non-destructive detection approach fused with nonlinear multi-features using multilayer autoencoder (MAE) was proposed. For dielectric spectrum device employed to gather electrical 300 Fuji sample These measurements were taken at nine distinct frequencies, spanning from 0.158 3,980 kHz. normal control group for validation, precisely detect its parameters, special analysis apparatuses utilized collect data on firmness, SSCs, TACs. predict key such SSC/TAC, classical regression models implemented subject comprehensive analysis. experimental results reveal feature variable selection based MAE perceptron (MLP) achieved best performance. Specifically, correlation coefficients (R 2) predicting SSC/TAC reached up 0.88 0.82, respectively, root mean square errors (RMSEs) 0.66 2.08. Regarding state-of-the-art dimensionality reduction, can be validated extraction methodology complex parameters. It demonstrates robust applicability diverse array other

Язык: Английский

Процитировано

0

Nondestructive Internal Quality Detection Method for Yellow Pitaya Based on EIS and Tactile Multimodal Perception Data-Driven Approach DOI
Jiahao Yu, Yu Sun,

Nedeljko Latinovic

и другие.

Journal of Food Composition and Analysis, Год журнала: 2025, Номер unknown, С. 107744 - 107744

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Electrochemical impedance spectroscopy for pear ripeness detection and integration with robotic manipulators DOI

Xunan Sui,

Jiawen Zou,

Zhide Geng

и другие.

Food Control, Год журнала: 2025, Номер unknown, С. 111425 - 111425

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0