Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading.

blueberry bruise damage finite element hyperspectral reflectance imaging response surface uniaxial compression experiment

Journal

Foods (Basel, Switzerland)
ISSN: 2304-8158
Titre abrégé: Foods
Pays: Switzerland
ID NLM: 101670569

Informations de publication

Date de publication:
27 Jun 2022
Historique:
received: 21 05 2022
revised: 10 06 2022
accepted: 13 06 2022
entrez: 9 7 2022
pubmed: 10 7 2022
medline: 10 7 2022
Statut: epublish

Résumé

Bruising of the subcutaneous tissues of blueberries is an important form of mechanical damage. Different levels of bruising have a significant effect on the post-harvest marketing of blueberries. To distinguish different grades of blueberry bruises and explore the effects of different factors, explicit dynamic simulation and near-infrared hyperspectral reflectance imaging were employed without harming the blueberries in this study. Based on the results of the compression experiment, an explicit dynamic simulation of blueberries was performed to measure the potential locations of bruises and preliminarily divide the bruise stages. A near-infrared hyperspectral reflectance imaging system was used to detect the actual blueberry bruises. According to the blueberry photos taken by the near-infrared hyperspectral reflectance imaging system, the actual bruise rates of blueberries were obtained by using the Environment for Visualizing Images software for training and classification. Bruise grades of blueberries were divided accordingly. Response surface methodology was used to determine the effects of ripeness, loading speed and loading location on the blueberry bruising rate. Under the optimized parameters, the actual damage rate of blueberries was 1.1%. The results provide an important theoretical basis for the accurate and rapid identification and classification of blueberry bruise damage.

Identifiants

pubmed: 35804715
pii: foods11131899
doi: 10.3390/foods11131899
pmc: PMC9265279
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Key-Area Research and Development Program of Guangdong Province
ID : 2020B0202010004
Organisme : National Natural Science Foundation of China
ID : 31901824
Organisme : Natural Science Foundation of Tianjin City
ID : 19JCQNJC13600
Organisme : Scientific Research Project of Tianjin Municipal Education Commission
ID : 2018KJ120

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Auteurs

Zhaoqi Zheng (Z)

Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China.
Tianjin International Joint Research and Development Center of Low-Carbon Green Process Equipment, Tianjin 300222, China.

Zimin An (Z)

Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China.

Xinyu Liu (X)

Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China.

Jinghui Chen (J)

Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China.

Yonghong Wang (Y)

Tianjin Key Laboratory of Integrated Design and On-Line Monitoring for Light Industry & Food Machinery and Equipment, College of Mechanical Engineering, Tianjin University of Science & Technology, Tianjin 300222, China.

Classifications MeSH