PMID- 36140077
OWN - NLM
STAT- MEDLINE
DCOM- 20220926
LR  - 20220928
IS  - 2079-6374 (Electronic)
IS  - 2079-6374 (Linking)
VI  - 12
IP  - 9
DP  - 2022 Aug 28
TI  - An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow 
      Search Algorithm.
LID - 10.3390/bios12090692 [doi]
LID - 692
AB  - To rapidly detect whether apples are infected by fungi, a portable electronic 
      nose was used in this study to collect the gas information from apples, and the 
      collected information was processed by smoothing filtering, data dimensionality 
      reduction, and outlier removal. Following this, we utilized K-nearest neighbors 
      (KNN), random forest (RF), support vector machine (SVM), a convolutional neural 
      network (CNN), a back-propagation neural network (BPNN), a particle swarm 
      optimization-back-propagation neural network (PSO-BPNN), a gray wolf 
      optimization-backward propagation neural network (GWO-BPNN), and a sparrow search 
      algorithm-backward propagation neural network (SSA-BPNN) model to discriminate 
      apple samples, and adopted the 10-fold cross-validation method to evaluate the 
      performance of each model. The results show that SSA can effectively optimize the 
      performance of the BPNN, such that the recognition accuracy of the optimized 
      SSA-BPNN model reaches 98.40%. This study provides an important reference value 
      for the application of an electronic nose in the non-destructive and rapid 
      detection of fungal infection in apples.
FAU - Zhao, Changtong
AU  - Zhao C
AD  - Mechanical Electrical Engineering School, Beijing Information Science and 
      Technology University, Beijing 100192, China.
FAU - Ma, Jie
AU  - Ma J
AD  - Mechanical Electrical Engineering School, Beijing Information Science and 
      Technology University, Beijing 100192, China.
FAU - Jia, Wenshen
AU  - Jia W
AD  - Mechanical Electrical Engineering School, Beijing Information Science and 
      Technology University, Beijing 100192, China.
AD  - Institute of Quality Standard and Testing Technology, Beijing Academy of 
      Agriculture and Forestry Sciences, Beijing 100097, China.
FAU - Wang, Huihua
AU  - Wang H
AD  - Department of Food and Bioengineering, Beijing Vocational College of Agriculture, 
      Beijing 102206, China.
FAU - Tian, Hui
AU  - Tian H
AD  - Mechanical Electrical Engineering School, Beijing Information Science and 
      Technology University, Beijing 100192, China.
FAU - Wang, Jihua
AU  - Wang J
AD  - Institute of Quality Standard and Testing Technology, Beijing Academy of 
      Agriculture and Forestry Sciences, Beijing 100097, China.
FAU - Zhou, Wei
AU  - Zhou W
AUID- ORCID: 0000-0003-1763-714X
AD  - Hebei Food Safety Key Laboratory, Hebei Food Inspection and Research Institute, 
      Shijiazhuang 050091, China.
LA  - eng
GR  - 21375501D/Hebei Province Key Research and Development Project, China/
GR  - 2019YFC1605603/National Key Research and Development Program of China/
GR  - 31801634/National Natural Science Foundation of China/
GR  - CZZJ202102/Financial Supplementary Special Project of Beijing Academy of 
      Agriculture and Forestry Sciences/
PT  - Journal Article
DEP - 20220828
PL  - Switzerland
TA  - Biosensors (Basel)
JT  - Biosensors
JID - 101609191
SB  - IM
MH  - Algorithms
MH  - *Malus
MH  - *Mycoses
MH  - Neural Networks, Computer
MH  - Support Vector Machine
PMC - PMC9496132
OTO - NOTNLM
OT  - apples
OT  - electronic nose
OT  - fungal infection
OT  - sparrow search algorithm
COIS- The authors declare no conflict of interest.
EDAT- 2022/09/24 06:00
MHDA- 2022/09/28 06:00
PMCR- 2022/08/28
CRDT- 2022/09/23 01:10
PHST- 2022/06/20 00:00 [received]
PHST- 2022/08/16 00:00 [revised]
PHST- 2022/08/17 00:00 [accepted]
PHST- 2022/09/23 01:10 [entrez]
PHST- 2022/09/24 06:00 [pubmed]
PHST- 2022/09/28 06:00 [medline]
PHST- 2022/08/28 00:00 [pmc-release]
AID - bios12090692 [pii]
AID - biosensors-12-00692 [pii]
AID - 10.3390/bios12090692 [doi]
PST - epublish
SO  - Biosensors (Basel). 2022 Aug 28;12(9):692. doi: 10.3390/bios12090692.