PMID- 35454743
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2304-8158 (Print)
IS  - 2304-8158 (Electronic)
IS  - 2304-8158 (Linking)
VI  - 11
IP  - 8
DP  - 2022 Apr 16
TI  - Identification of Moldy Peanuts under Different Varieties and Moisture Content 
      Using Hyperspectral Imaging and Data Augmentation Technologies.
LID - 10.3390/foods11081156 [doi]
LID - 1156
AB  - Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to 
      be screened in the production process. Hyperspectral imaging techniques can be 
      used to identify moldy peanuts. However, the changes in spectral information and 
      texture information caused by the difference in moisture content in peanuts will 
      affect the identification accuracy. To reduce and eliminate the influence of this 
      factor, a data augmentation method based on interpolation was proposed to improve 
      the generalization ability and robustness of the model. Firstly, the 
      near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture 
      content gradients with 39,119 kernels were collected. Then, the data augmentation 
      method called the difference of spectral mean (DSM) was constructed. K-nearest 
      neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used 
      to verify the effectiveness of the data augmentation method on data with two 
      gradients and three gradients. The experimental results show that the data 
      augmentation can effectively reduce the influence of the difference in moisture 
      content on the model identification accuracy. The DSM method has the highest 
      accuracy improvement in 5 varieties of peanut datasets. In particular, the 
      accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was 
      improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study 
      provides a new method for improving the identification accuracy of moldy peanuts 
      and also provides a reference basis for the screening of related foods such as 
      corn, orange, and mango.
FAU - Liu, Ziwei
AU  - Liu Z
AUID- ORCID: 0000-0002-0168-9896
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Jiang, Jinbao
AU  - Jiang J
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Li, Mengquan
AU  - Li M
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Yuan, Deshuai
AU  - Yuan D
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Nie, Cheng
AU  - Nie C
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Sun, Yilin
AU  - Sun Y
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
FAU - Zheng, Peng
AU  - Zheng P
AD  - College of Geosciences and Surveying Engineering, China University of Mining and 
      Technology (Beijing), Beijing 100083, China.
LA  - eng
GR  - 41871341/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20220416
PL  - Switzerland
TA  - Foods
JT  - Foods (Basel, Switzerland)
JID - 101670569
PMC - PMC9030905
OTO - NOTNLM
OT  - classification
OT  - data augmentation
OT  - hyperspectral image
OT  - moisture content
OT  - moldy peanut identification
COIS- The authors declare no conflict of interest.
EDAT- 2022/04/24 06:00
MHDA- 2022/04/24 06:01
PMCR- 2022/04/16
CRDT- 2022/04/23 01:04
PHST- 2022/02/05 00:00 [received]
PHST- 2022/04/12 00:00 [revised]
PHST- 2022/04/14 00:00 [accepted]
PHST- 2022/04/23 01:04 [entrez]
PHST- 2022/04/24 06:00 [pubmed]
PHST- 2022/04/24 06:01 [medline]
PHST- 2022/04/16 00:00 [pmc-release]
AID - foods11081156 [pii]
AID - foods-11-01156 [pii]
AID - 10.3390/foods11081156 [doi]
PST - epublish
SO  - Foods. 2022 Apr 16;11(8):1156. doi: 10.3390/foods11081156.