PMID- 37308777
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1097-0010 (Electronic)
IS  - 0022-5142 (Linking)
VI  - 103
IP  - 14
DP  - 2023 Nov
TI  - Application of colorimetric sensor array coupled with machine-learning approaches 
      for the discrimination of grains based on freshness.
PG  - 6790-6799
LID - 10.1002/jsfa.12777 [doi]
AB  - BACKGROUND: Volatile organic compounds (VOCs) in grain fluctuate depending on the 
      degree of grain freshness. A new colorimetric sensor array (CSA) was developed as 
      capture probes for the quantification of VOCs in grains in this work, and it was 
      designed to monitor the variation of grain VOCs. CSA spectral data acquisition 
      using visible-near-infrared spectroscopy and image processing of CSA's image 
      imformation by computer were used comparatively. Then, machine-learning-based 
      models - for example, synergistic interval partial least squares, genetic 
      algorithm, competitive adaptive reweighted sampling (CARS) algorithm, and ant 
      colony optimization (ACO) algorithm - were introduced to optimize variables. 
      Moreover, principal component analysis, and linear discriminant analysis (LDA), 
      and K-nearest neighbors (KNN) were used for the classification. Ultimately, 
      quantitative models for detecting grain freshness are developed using various 
      variable selection strategies. RESULTS: Compared with the pattern recognition 
      results of image processing, visible-near-infrared spectroscopy could better 
      separate the grains with different freshness from principal component analysis, 
      and the prediction set of LDA models could correctly identify 100% of rice, 
      96.88% of paddy, and 97.9% of soybeans. In addition, compared with CARS and ACO, 
      the LDA model and KNN model based on genetic algorithms show the best prediction 
      performance. The prediction set could correctly identify 100% of rice and paddy 
      samples and 95.83% of soybean samples. CONCLUSION: The method developed could be 
      used for non-destructive detection of grain freshness. (c) 2023 Society of Chemical 
      Industry.
CI  - (c) 2023 Society of Chemical Industry.
FAU - Liang, Yue
AU  - Liang Y
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Lin, Hao
AU  - Lin H
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Kang, Wencui
AU  - Kang W
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Shao, Xiaokang
AU  - Shao X
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Cai, Jianrong
AU  - Cai J
AUID- ORCID: 0000-0002-7509-3067
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Li, Huanhuan
AU  - Li H
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
FAU - Chen, Quansheng
AU  - Chen Q
AUID- ORCID: 0000-0003-2498-3278
AD  - School of Food and Biological Engineering, Jiangsu University, Jiangsu, China.
AD  - College of Food and Biological Engineering, Jimei University, Xiamen, China.
LA  - eng
GR  - SCX 203321/Jiangsu Agricultural independent innovation fund/
GR  - BE2021343/Key R&D program of Jiangsu Province/
GR  - 51975259/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20230628
PL  - England
TA  - J Sci Food Agric
JT  - Journal of the science of food and agriculture
JID - 0376334
RN  - 0 (Volatile Organic Compounds)
SB  - IM
MH  - Colorimetry
MH  - Least-Squares Analysis
MH  - Algorithms
MH  - Spectroscopy, Near-Infrared/methods
MH  - Discriminant Analysis
MH  - *Volatile Organic Compounds/analysis
MH  - *Oryza
OTO - NOTNLM
OT  - colorimetric sensor array
OT  - freshness degree
OT  - grain
OT  - image processing
OT  - visible-near-infrared spectroscopy
EDAT- 2023/06/13 01:14
MHDA- 2023/10/23 00:43
CRDT- 2023/06/12 23:29
PHST- 2023/05/28 00:00 [revised]
PHST- 2022/08/05 00:00 [received]
PHST- 2023/06/13 00:00 [accepted]
PHST- 2023/10/23 00:43 [medline]
PHST- 2023/06/13 01:14 [pubmed]
PHST- 2023/06/12 23:29 [entrez]
AID - 10.1002/jsfa.12777 [doi]
PST - ppublish
SO  - J Sci Food Agric. 2023 Nov;103(14):6790-6799. doi: 10.1002/jsfa.12777. Epub 2023 
      Jun 28.