Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on Wavelet Analysis

doi:10.3964/j.issn.1000-0593(2022)10-3235-08

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 10 >Page 3253 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 10, 3253 (2022)

Estimation of Total Nitrogen Content in Fresh Tea Leaves Based on Wavelet Analysis

DOI: 10.3964/j.issn.1000-0593(2022)10-3235-08 Cite this Article

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Fig. 1. Spatial distribution of study area

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Fig. 2. Reflectance spectra with different pre-processing methods

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Fig. 3. Spectral characteristics of wavelet transform

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Fig. 4. Correlation analysis of wavelet transform

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Fig. 5. The variables selected by SPA, CARS and VCPA

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Fig. 6. Fitting diagrams of the optimal models of tea nitrogen based on (a) CWT (1 scale)-SPA-PLS, (b) CWT (1 scale)-CARS-PLS and (c) CWT (1 scale)-VCPA-PLS

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模型	光谱处理	主成分	建模集		预测集
模型	光谱处理	主成分	$R_{C}^{2}$	RMSE_C	$R_{P}^{2}$	RMSE_P
	SG	8	0.50	0.42	0.37	0.48
	Detrending	9	0.59	0.35	0.58	0.38
SPA-PLSR	1st	7	0.63	0.37	0.61	0.35
	SNV	10	0.71	0.31	0.60	0.39
	MSC	8	0.62	0.38	0.68	0.29
	SG	5	0.62	0.35	0.55	0.41
	Detrending	10	0.75	0.30	0.71	0.29
CARS-PLSR	1st	11	0.79	0.26	0.73	0.32
	SNV	11	0.71	0.33	0.68	0.29
	MSC	11	0.73	0.29	0.62	0.39
	SG	6	0.81	0.24	0.65	0.37
	Detrending	10	0.83	0.25	0.73	0.31
VCPA-PLSR	1st	9	0.86	0.18	0.84	0.25
	SNV	7	0.81	0.25	0.67	0.35
	MSC	6	0.82	0.24	0.72	0.32

Table 1. Models based on different preprocessing methods and variables selection methods

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模型	分解尺度	主成分	建模集		预测集
模型	分解尺度	主成分	$R_{C}^{2}$	RMSE_C	$R_{P}^{2}$	RMSE_P
SPA-PLSR	1	5	0.83	0.25	0.72	0.37
	2	7	0.72	0.30	0.68	0.36
	3	10	0.74	0.31	0.69	0.33
	4	11	0.78	0.28	0.67	0.35
	5	11	0.79	0.26	0.60	0.41
	6	11	0.65	0.33	0.60	0.42
CARS-PLSR	1	7	0.91	0.19	0.88	0.24
	2	8	0.81	0.24	0.76	0.32
	3	12	0.81	0.25	0.73	0.31
	4	8	0.82	0.26	0.69	0.29
	5	10	0.78	0.28	0.64	0.35
	6	11	0.73	0.32	0.68	0.32
VCPA-PLSR	1	7	0.95	0.16	0.90	0.23
	2	7	0.83	0.23	0.82	0.27
	3	10	0.81	0.26	0.79	0.26
	4	8	0.82	0.24	0.73	0.30
	5	8	0.81	0.25	0.77	0.30
	6	7	0.73	0.29	0.74	0.35

Table 2. Models based on different decomposition scales and variables selection methods

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