Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy

Xu ZHANG; Tian-gang ZHANG; Wei-song MU; Ze-tian FU; Xiao-shuan ZHANG

doi:10.3964/j.issn.1000-0593(2021)01-0229-07

Journals >Spectroscopy and Spectral Analysis >Volume 41 >Issue 1 >Page 229 > Article

Spectroscopy and Spectral Analysis
Vol. 41, Issue 1, 229 (2021)

Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy

Xu ZHANG^1、1, Tian-gang ZHANG^1、1, Wei-song MU^1、1, Ze-tian FU^1、1, and Xiao-shuan ZHANG^1、1

Author Affiliations

¹[in Chinese]

¹1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

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DOI: 10.3964/j.issn.1000-0593(2021)01-0229-07 Cite this Article

Xu ZHANG, Tian-gang ZHANG, Wei-song MU, Ze-tian FU, Xiao-shuan ZHANG. Prediction of Soluble Solids Content for Wine Grapes During Maturing Based on Visible and Near-Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(1): 229 Copy Citation Text

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Fig. 1. Sampling sites of wine grape

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Fig. 2. The absorbance spectra of grape berries

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Fig. 3. The absorbance spectra of canopy leaves of grape

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Fig. 4. PCA score plot of Chardonnay grape

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Fig. 5. Results of calibration and validation of SSC prediction model for Chardonnay grape

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Fig. 6. Results of calibration and validation of SSC prediction model for Petit Manseng grape

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Fig. 7. Results of calibration and validation of SSC prediction model for Merlot grape

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Fig. 8. Results of calibration and validation of SSC prediction model for Cabernet Sauvignon grape

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Fig. 9. Results of calibration and validation of SSC prediction model for Cabernet Franc grape

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Fig. 10. Results of calibration and validation of SSC prediction model for Chardonnay leaves

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Fig. 11. Results of calibration and validation of SSC prediction model for Petit Manseng leaves

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Fig. 12. Results of calibration and validation of SSC prediction model for Merlot leaves

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Fig. 13. Results of calibration and validation of SSC prediction model for Cabernet Sauvignon leaves

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Fig. 14. Results of calibration and validation of SSC prediction model for Cabernet Franc leaves

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样品名称	类别	样品子集	样本数量	范围/%	均值 /%	标准误差/%
霞多丽	浆果	校正集	64	17.0~22.9	19.76	0.78
霞多丽	浆果	验证集	21	18.4~20.8	19.72	0.66
小芒森	浆果	校正集	64	19.9~23.4	21.67	0.72
小芒森	浆果	验证集	20	20.5~22.6	21.55	0.63
梅洛	浆果	校正集	66	17.0~22.9	20.22	1.03
梅洛	浆果	验证集	22	18.3~22.2	20.50	1.00
赤霞珠	浆果	校正集	60	19.1~20.9	20.17	0.46
赤霞珠	浆果	验证集	20	18.8~21.3	20.05	0.64
品丽珠	浆果	校正集	66	17.2~21.3	19.67	0.80
品丽珠	浆果	验证集	22	18.0~21.2	19.53	0.78
霞多丽	叶片	校正集	50	18.0~21.4	19.54	0.72
霞多丽	叶片	验证集	16	18.5~21.3	19.71	0.56
小芒森	叶片	校正集	50	19.9~23.6	21.87	1.03
小芒森	叶片	验证集	17	20.8~23.0	21.81	0.56
梅洛	叶片	校正集	53	17.2~23.7	20.47	1.54
梅洛	叶片	验证集	16	19.5~22.8	20.56	0.91
赤霞珠	叶片	校正集	49	18.1~23.2	20.12	1.03
赤霞珠	叶片	验证集	15	18.6~21.4	19.83	0.79
品丽珠	叶片	校正集	53	18.1~21.2	19.61	0.73
品丽珠	叶片	验证集	17	18.8~20.7	19.85	0.55

Table 1. Statistics results of SSC of sample sets

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样品种类	预处理方法	PCS	校正集		验证集
样品种类	预处理方法	PCS	R_C	RMSEC	R_V	RMSEV
霞多丽	S-G	2	0.72	0.76	0.73	0.64
	S-G+FD	2	0.78	0.49	0.83	0.40
	S-G+SNV+FD	2	0.83	0.44	0.85	0.36
	S-G+MSC+FD	3	0.93	0.30	0.86	0.36
小芒森	S-G	4	0.61	0.58	0.41	0.63
	S-G+FD	3	0.95	0.22	0.86	0.33
	S-G+SNV+FD	3	0.95	0.22	0.86	0.33
	S-G+MSC+FD	3	0.95	0.22	0.86	0.30
梅洛	S-G	7	0.73	0.70	0.76	0.70
	S-G+FD	7	1.00	0.09	0.83	0.54
	S-G+SNV+FD	4	0.96	0.30	0.88	0.48
	S-G+MSC+FD	5	0.96	0.29	0.88	0.48
赤霞珠	S-G	4	0.91	0.19	0.87	0.35
	S-G+FD	1	0.93	0.18	0.85	0.37
	S-G+SNV+FD	2	0.97	0.12	0.88	0.31
	S-G+MSC+FD	7	0.98	0.02	0.82	0.40
品丽珠	S-G	6	0.71	0.56	0.67	0.33
	S-G+FD	3	0.81	0.37	0.71	0.66
	S-G+SNV+FD	4	0.93	0.27	0.81	0.37
	S-G+MSC+FD	4	0.96	0.22	0.86	0.44

Table 2. Comparison of PLS prediction models with four different pretreatment methods

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样品种类	预处理方法	PCS	校正集		验证集
样品种类	预处理方法	PCS	R_C	RMSEC	R_V	RMSEV
霞多丽	S-G	1	0.69	0.65	0.36	0.79
	S-G+FD	1	0.70	0.52	0.55	0.68
	S-G+SNV+FD	1	0.71	0.66	0.61	0.55
	S-G+MSC+FD	1	0.76	0.46	0.67	0.44
小芒森	S-G	2	0.61	0.96	0.41	0.75
	S-G+FD	3	0.66	0.83	0.59	0.70
	S-G+SNV+FD	2	0.75	0.79	0.60	0.58
	S-G+MSC+FD	2	0.80	0.61	0.66	0.48
梅洛	S-G	1	0.62	0.70	0.52	0.97
	S-G+FD	2	0.72	0.69	0.61	0.86
	S-G+SNV+FD	2	0.72	0.90	0.61	0.80
	S-G+MSC+FD	2	0.78	0.96	0.66	0.75
赤霞珠	S-G	1	0.47	1.26	0.51	1.27
	S-G+FD	2	0.61	0.99	0.61	0.99
	S-G+SNV+FD	1	0.70	0.86	0.62	0.77
	S-G+MSC+FD	1	0.73	0.71	0.69	0.70
品丽珠	S-G	3	0.60	0.90	0.41	1.65
	S-G+FD	2	0.69	0.77	0.59	0.99
	S-G+SNV+FD	2	0.71	0.59	0.60	0.75
	S-G+MSC+FD	2	0.78	0.46	0.65	0.44

Table 3. Comparison of PLS prediction models with four different pretreatment methods

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样品名称	样品类别	数量	范围/%	绝对误差/%	相对误差/%
霞多丽	浆果	20	18.4~20.8	0.08	-0.44
小芒森	浆果	20	20.5~22.6	-0.02	-0.06
梅洛	浆果	20	19.1~22.2	-0.09	-0.35
赤霞珠	浆果	20	18.8~20.9	-0.01	-0.05
品丽珠	浆果	20	18.0~21.2	0.07	0.43

Table 4. External validation results of samples of berry and canopy leaves

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