Quantitative Analysis of Soluble Solids and Titratable Acidity Content in Angeleno Plum by Near-Infrared Spectroscopy With BP-ANN and PLS

指标名称	波段/cm^-1	校正集		交叉验证		因子数	剔除样品
指标名称	波段/cm^-1	r	RMSEC	r	RMSECV	因子数	剔除样品
SSC/%	4 000~9 000	0.890 1 0.891 1	1.01 1.01	0.876 3 0.877 5	1.06 1.06	8 8	剔除7个剔除8个
	4 000~8 852	0.901 6 0.901 3 0.904 8	0.96 0.96 0.94	0.888 7 0.887 3 0.893 2	1.01 1.02 0.99	8 8 8	剔除7个剔除8个剔除10个
	4 000~8 000	0.886 2	1.03	0.871 8	1.08	8	剔除6个
	4 000~6 000	0.883 9 0.884 5	1.04 1.03	0.872 9 0.873 0	1.07 1.07	7 7	剔除7个剔除8个
	6 000~9 000	0.888 5	1.02	0.872 8	1.08	7	剔除6个
TA/%	4 000~9 000	0.821 2	0.89	0.811 4	0.90	6	剔除5个
	4 000~6 523	0.827 5	0.87	0.818 7	0.88	5	剔除5个
	4 605~9 000	0.822 3	0.88	0.812 2	0.90	6	剔除5个
	4 605~6 523	0.833 0	0.86	0.824 5	0.87	5	剔除5个
	5 605~9 000	0.824 8	0.88	0.812 3	0.90	6	剔除5个
	5 605~6 523	0.821 7	0.88	0.799 6	0.92	5	剔除6个
	6 410~7 600	0.818 5	0.89	0.810 2	0.90	5	剔除6个

Table 2. Statistics of SSC, TA in plum for calibration set and prediction set
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Table 2. Statistics of SSC, TA in plum for calibration set and prediction set
指标名称样品集样品数数值范围平均值标准偏差
SSC
/% 校正集
预测集 382
191 5.20~16.00
5.40~14.90 10.16
10.19 2.23
2.14
TA/% 校正集
预测集 267
130 6.15~13.45
6.44~12.68 9.88
9.84 1.55
1.52

Table 3. Performance comparison with the SSC model of different spectral pretreatment methods

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Table 3. Performance comparison with the SSC model of different spectral pretreatment methods

光谱预处理	校正集		预测集		因子数
光谱预处理	R_c	RMSEC	R_p	RMSEP	因子数
原始光谱	0.903 9	0.96	0.874 0	1.04	8
消除常数偏移量	0.913 5	0.92	0.875 9	1.02	9
减去一条直线	0.909 0	0.94	0.874 8	1.03	8
SNV	0.911 4	0.93	0.874 0	1.04	8
最大-最小归一化	0.903 4	0.97	0.863 3	1.08	8
MSC校正	0.914 4	0.91	0.878 5	1.00	8
一阶微分+5点平滑	0.876 9	1.08	0.876 6	1.01	4
一阶微分+17点平滑	0.844 0	1.20	0.833 0	1.18	4
一阶微分+25点平滑	0.862 2	1.14	0.874 7	1.03	5
二阶微分+17点平滑	0.869 8	1.11	0.795 5	1.29	4
二阶微分+25点平滑	0.842 6	1.21	0.812 2	1.24	4
一阶微分+减去一条直线+5点平滑	0.904 8	0.95	0.874 1	1.04	4
一阶微分+SNV+25点平滑	0.905 2	0.95	0.876 0	1.02	8
一阶微分+MSC+25点平滑	0.905 6	0.95	0.875 4	1.02	7

Table 4. Performance comparison with the TA model of different spectral pretreatment methods

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Table 4. Performance comparison with the TA model of different spectral pretreatment methods

光谱预处理	校正集		预测集		因子数
光谱预处理	R_c	RMSEC	R_p	RMSEP	因子数
原始光谱	0.840 3	0.85	0.807 2	0.89	5
消除常数偏移量	0.795 2	0.95	0.794 8	0.92	4
减去一条直线	0.830 6	0.87	0.796 3	0.92	5
SNV	0.843 5	0.85	0.819 3	0.87	4
最大-最小归一化	0.826 3	0.88	0.807 7	0.89	5
MSC校正	0.841 7	0.84	0.814 0	0.88	4
一阶微分+17点平滑	0.841 0	0.84	0.813 9	0.88	4
一阶微分+25点平滑	0.815 5	0.91	0.812 0	0.88	4
二阶微分+17点平滑	0.841 2	0.84	0.760 8	0.98	4
二阶微分+25点平滑	0.825 3	0.88	0.790 6	0.93	4
一阶微分+减去一条直线+5点平滑	0.815 1	0.91	0.774 2	0.96	4
一阶微分+SNV+9点平滑	0.860 3	0.80	0.819 6	0.86	6
一阶微分+SNV+25点平滑	0.840 7	0.85	0.816 8	0.87	5
一阶微分+MSC+25点平滑	0.809 2	0.92	0.775 3	0.95	4

Table 5. Parameters of the BP-ANN models for SSC, TA in plums
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Table 5. Parameters of the BP-ANN models for SSC, TA in plums
参数 SSC TA
输入层函数 tansig tansig
隐含层函数 logsig logsig
输出层函数 purelin tansig
训练函数 trainlm traingdx
显示间隔 50 50
学习速率 0.05 0.05
训练目标 1×10^-3 1×10^-3

Table 6. Influence of nodes in hidden layer on quantitative results of SSC in plum

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Table 6. Influence of nodes in hidden layer on quantitative results of SSC in plum

隐含层节点数	校正集		预测集
隐含层节点数	R_c	RMSEC	R_p	RMSEP
10	0.935 1	0.84	0.719 3	1.15
11	0.945 1	0.82	0.671 2	1.25
12	0.925 4	0.85	0.660 2	1.29
13	0.950 8	0.81	0.755 3	1.15
14	0.955 7	0.81	0.782 0	1.04
15	0.960 4	0.80	0.797 9	1.04
16	0.964 1	0.79	0.854 0	1.02
17	0.964 3	0.79	0.871 9	1.01
18	0.976 7	0.75	0.889 7	0.99
19	0.967 2	0.78	0.713 2	1.16
20	0.965 9	0.78	0.675 9	1.22

Table 7. Influence of nodes in hidden layer on quantitative results of TA in plum

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Table 7. Influence of nodes in hidden layer on quantitative results of TA in plum

隐含层节点数	校正集		预测集
隐含层节点数	R_c	RMSEC	R_p	RMSEP
10	0.941 0	0.66	0.806 4	0.88
11	0.947 9	0.65	0.787 7	0.91
12	0.959 1	0.64	0.817 7	0.86
13	0.958 4	0.64	0.800 6	0.91
14	0.962 1	0.63	0.806 0	0.87
15	0.956 7	0.64	0.830 7	0.85
16	0.955 2	0.64	0.795 4	0.91
17	0.956 7	0.64	0.806 2	0.87
18	0.960 8	0.63	0.877 3	0.84
19	0.974 3	0.62	0.897 7	0.83
20	0.968 4	0.63	0.867 6	0.84

Table 8. Quantitative results of the established models using PLS and BP-ANN methods for SSC, TA in plum
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Table 8. Quantitative results of the established models using PLS and BP-ANN methods for SSC, TA in plum
检测指标建模方法校正集预测集
R_c RMSEC R_p RMSEP
SSC/% PLS
BP-ANN 0.914 4
0.976 7 0.91
0.75 0.878 5
0.889 7 1.00
0.99
TA/% PLS
BP-ANN 0.860 3
0.974 3 0.80
0.62 0.819 6
0.897 7 0.86
0.83

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. Quantitative Analysis of Soluble Solids and Titratable Acidity Content in Angeleno Plum by Near-Infrared Spectroscopy With BP-ANN and PLS[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2836

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Paper Information

Category: Research Articles

Received: Aug. 22, 2021

Accepted: Mar. 17, 2022

Published Online: Nov. 17, 2022

The Author Email:

DOI:10.3964/j.issn.1000-0593(2022)09-2836-07

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison on performance of different model bands

Table 1. Comparison on performance of different model bands

Table 2. Statistics of SSC, TA in plum for calibration set and prediction set

Table 2. Statistics of SSC, TA in plum for calibration set and prediction set

Table 3. Performance comparison with the SSC model of different spectral pretreatment methods

Table 3. Performance comparison with the SSC model of different spectral pretreatment methods

Table 4. Performance comparison with the TA model of different spectral pretreatment methods

Table 4. Performance comparison with the TA model of different spectral pretreatment methods

Table 5. Parameters of the BP-ANN models for SSC, TA in plums

Table 5. Parameters of the BP-ANN models for SSC, TA in plums

Table 6. Influence of nodes in hidden layer on quantitative results of SSC in plum

Table 6. Influence of nodes in hidden layer on quantitative results of SSC in plum

Table 7. Influence of nodes in hidden layer on quantitative results of TA in plum

Table 7. Influence of nodes in hidden layer on quantitative results of TA in plum

Table 8. Quantitative results of the established models using PLS and BP-ANN methods for SSC, TA in plum

Table 8. Quantitative results of the established models using PLS and BP-ANN methods for SSC, TA in plum