NIR Spectral Classification of Lettuce Using Principal Component Analysis Sort and Fuzzy Linear Discriminant Analysis

Bin WU; Jia-qi SHEN; Xin WANG; Xiao-hong WU; Xiao-lei HOU

doi:10.3964/j.issn.1000-0593(2022)10-3079-05

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 10 >Page 3079 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 10, 3079 (2022)

NIR Spectral Classification of Lettuce Using Principal Component Analysis Sort and Fuzzy Linear Discriminant Analysis

Bin WU^1、1;, Jia-qi SHEN^2、2;, Xin WANG^2、2;, Xiao-hong WU^3、3;, and Xiao-lei HOU^2、2;

Author Affiliations

¹1. Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China

²2. Institute of Talented Engineering Students, Jiangsu University, Zhenjiang 212013, China

³3. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

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DOI: 10.3964/j.issn.1000-0593(2022)10-3079-05 Cite this Article

Bin WU, Jia-qi SHEN, Xin WANG, Xiao-hong WU, Xiao-lei HOU. NIR Spectral Classification of Lettuce Using Principal Component Analysis Sort and Fuzzy Linear Discriminant Analysis[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3079 Copy Citation Text

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Fig. 1. NIR spectral data of lettuce samples treated by MSC

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Fig. 2. The accuracy changing with the weight coefficient (m)
(a): m in the range of 1~15; (b): m in the range of 1~5

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Fig. 3. The accuracy changing with the number of cluster center (K)

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The number of eigenvectors	PCA	PCA Sort
The number of eigenvectors	accuracy/%	accuracy/%
6	48.33	78.33
7	61.67	88.33
8	83.33	86.67
9	75.00	85.00
10	81.67	88.33
11	85.00	95.00
12	78.33	93.33
13	76.67	90.00
14	76.67	96.67
15	83.33	98.33

Table 1. The first 6~15 eigenvectors and accuracies of PCA and PCA Sort

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Algorithm	Accuracy/%
PCA+KNN	43
PCA+FLDA+KNN	83
PCA Sort+FLDA+KNN	98

Table 2. The highest accuracy of three methods

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