Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Processing Request
The Rapid Non-Destructive Differentiation of Different Varieties of Rice by Fluorescence Hyperspectral Technology Combined with Machine Learning.
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Processing Request
- معلومة اضافية
- المصدر:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 100964009 Publication Model: Electronic Cited Medium: Internet ISSN: 1420-3049 (Electronic) Linking ISSN: 14203049 NLM ISO Abbreviation: Molecules Subsets: MEDLINE
- بيانات النشر:
Original Publication: Basel, Switzerland : MDPI, c1995-
- الموضوع:
- نبذة مختصرة :
A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475-1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties.
- References:
Molecules. 2023 Jul 28;28(15):. (PMID: 37570696)
Food Chem. 2016 Nov 1;210:415-21. (PMID: 27211665)
Spectrochim Acta A Mol Biomol Spectrosc. 2022 May 15;273:120949. (PMID: 35183857)
Brief Bioinform. 2023 Mar 19;24(2):. (PMID: 36653905)
Foods. 2021 Nov 29;10(12):. (PMID: 34945487)
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Dec 5;223:117357. (PMID: 31306962)
Foods. 2023 Mar 12;12(6):. (PMID: 36981130)
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Jan 5;284:121785. (PMID: 36058172)
Molecules. 2022 Feb 10;27(4):. (PMID: 35208985)
Molecules. 2023 Dec 29;29(1):. (PMID: 38202780)
Front Genet. 2020 Dec 14;11:620143. (PMID: 33381156)
Sci Rep. 2024 Jan 11;14(1):1114. (PMID: 38212322)
Crit Rev Food Sci Nutr. 2023 Nov 27;:1-23. (PMID: 38010116)
J Clin Med. 2022 Sep 29;11(19):. (PMID: 36233640)
Front Plant Sci. 2023 Aug 29;14:1251418. (PMID: 37705705)
Entropy (Basel). 2019 Jun 25;21(6):. (PMID: 33267335)
Bioinformatics. 2008 Sep 15;24(18):2010-4. (PMID: 18650208)
- Grant Information:
Grant NO. 035-1921993093 the subject double support program of Sichuan Agricultural University
- Contributed Indexing:
Keywords: fluorescence hyperspectral technology; non-destructive; rice; variety classification
- الموضوع:
Date Created: 20240210 Date Completed: 20240214 Latest Revision: 20240214
- الموضوع:
20240214
- الرقم المعرف:
PMC10856461
- الرقم المعرف:
10.3390/molecules29030682
- الرقم المعرف:
38338424
No Comments.