基于近红外法的紫胶原胶主组分快速预测模型构建

doi:10.3969/j.issn.0253-2417.2020.04.007

Abstract

Abstract:

Chemical methods were used to determine the content of resin, wax and pigment in the sticklac.Fourier transform near infrared spectroscopy (FT-NIR) was used to collect the near infrared spectra of sticklac, and the original spectra were obtained.Spectral pretreatment was employed to eliminate the noise, and partial least squares(PLS) was performed to establish the regression model.Finally, the analysis model of near infrared spectrum of resin, wax and pigment content in sticklac was established.The correction determination coefficients(R_c²) of resin, wax and pigment were 0.961, 0.812, 0.828, respectively. The root mean square errors of cross validation(RMSECV) were 1.96, 0.28, 0.065, respectively. The determination coefficients(R_p²) of validation sets were 0.957, 0.808, 0.793, respectively. And the root mean square errors of prediction(RMSEP) were 0.94, 0.124, 0.101, respectively.The results showed that the accuracy, stability and prediction performance of the model were good, which provided a new idea for the establishment of rapid analysis method for the primary components of sticklac.

Key words: sticklac, near infrared spectrum, primary components, prediction model

CLC Number:

Baoshan TANG,Kun LI,Hong ZHANG,Wenwen ZHANG,Qingfang GUAN,Zhengjun SHI. Rapid Prediction Model of Primary Components of Sticklac Based on Near Infrared Method[J]. Chemistry and Industry of Forest Products, 2020, 40(4): 47-56.

Figures/Tables 8

Table 1

Contents of resin, wax and pigment in sticklac samples from different sources"

序号 No.	紫胶样本来源 sample source	树脂/% resin	蜡质/% wax	色素/% pigment
1	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	80.12±0.63	6.10±0.15	1.19±0.03
2	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	77.70±3.69	5.75±0.36	1.22±0.02
3	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	78.60±0.79	5.79±0.20	1.32±0.03
4	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	77.93±1.71	5.80±0.40	1.35±0.04
5	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	78.58±3.18	5.78±0.15	1.19±0.01
6	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	76.64±0.52	5.84±0.19	1.27±0.04
7	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	76.06±2.56	5.78±0.14	1.21±0.02
8	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	76.66±2.70	5.46±0.24	1.20±0.02
9	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	79.29±0.79	5.33±0.37	1.19±0.08
10	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	75.27±1.97	5.65±0.36	1.21±0.04
11	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	81.37±1.26	5.61±0.16	1.08±0.02
12	中国云县钝叶黄檀树D. obtusifolia of China Yunxian	74.01±1.41	4.55±0.44	0.96±0.03
13	中国云县南岭黄檀树D.balansae of China Yunxian	73.42±1.77	6.18±0.24	1.57±0.04
14	中国云县南岭黄檀树D.balansae of China Yunxian	76.27±2.88	6.19±0.38	1.41±0.05
15	中国云县南岭黄檀树D.balansae of China Yunxian	78.27±3.61	5.54±0.12	1.22±0.01
16	中国绿春县荔枝树Litchichinensis Sonn.of China Lyuchun	72.87±0.08	5.59±0.09	1.64±0.07
17	中国绿春县荔枝树Litchichinensis Sonn.of China Lyuchun	73.44±3.00	5.17±0.61	1.40±0.04
18	中国绿春县南岭黄檀树D.balansae of China Lyuchun	74.00±2.32	4.90±0.24	1.46±0.04
19	中国绿春县南岭黄檀树D.balansae of China Lyuchun	75.91±3.18	4.31±0.07	1.24±0.01
20	中国冬代原胶winter generation of sticklac	67.22±3.12	4.25±0.27	1.02±0.02
21	中国夏代原胶dynasty sticklac	73.19±1.69	5.80±0.47	1.46±0.01
22	越南原胶Vietnamsticklac	74.02±5.66	5.09±0.35	1.47±0.03
23	越南原胶Vietnamsticklac	72.01±3.76	5.19±0.08	1.65±0.05
24	泰国原胶Thailand sticklac	71.05±2.56	5.15±0.25	1.21±0.02
25	泰国原胶Thailand sticklac	70.44±3.76	5.60±0.08	1.13±0.02
26	缅甸原胶Burma sticklac	28.61±1.04	3.59±0.12	1.08±0.03
27	缅甸原胶Burma sticklac	75.62±2.76	5.09±0.50	2.09±0.04
28	印度原胶India sticklac	81.64±4.83	4.37±0.18	1.06±0.01
29	印度原胶India sticklac	74.03±3.43	4.65±0.46	1.21±0.02

Table 1

Table 2

Fig.1

Table 3

Fig.2

Fig.3

Fig.4

Table 4

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成分 component	参数 parameter	样本数 sample umber	质量分数mass fraction/%
成分 component	参数 parameter	样本数 sample umber	范围scope	平均值average value	极差range
树脂resin	校正集calibration set	24	28.61~81.64	74.45	53.03
树脂resin	验证集validation set	5	67.22~74.02	71.05	6.80
蜡质wax	校正集calibration set	24	3.59~6.19	5.36	2.60
蜡质wax	验证集validation set	5	4.25~5.80	5.10	1.55
色素pigment	校正集calibration set	24	0.96~2.09	1.29	1.13
色素pigment	验证集validation set	5	1.02~1.65	1.36	0.63

组分 components	最佳光谱预处理方法选择 selection of the best spectral pretreatment method				最优波段选择 optimal band selection
组分 components	预处理方法 pretreatment method	主因子数 principal factor number	R_c²	RMSECV	波数范围/cm^-1 wave number range	主因子数 principal factor number	R_c²	RMSECV
树脂 resin	VN	8	0.961	1.96	9403.7~5446.3, 4601.6~4246.7	8	0.961	1.96
	ECO	10	0.950	2.23	9403.7~5446.3, 5450.1~4246.7	9	0.958	2.03
	MSC	10	0.938	2.48	9403.7~5446.3, 4601.6~4424.1	8	0.958	2.04
	M-MN	10	0.922	2.77	9403.7~6098.1, 5450.1~4246.7	9	0.948	2.27
	None	7	0.898	3.18	9403.7~4246.7	8	0.948	2.28
	FD+VN	8	0.885	3.37	9403.7~7498.3, 6102.0~4597.7	10	0.947	2.28
蜡质 wax	M-MN	10	0.812	0.28	9403.7~7498.3, 6102.0~5446.7	10	0.812	0.28
	None	10	0.787	0.29	9403.7~4246.7	9	0.769	0.31
	ECO	8	0.699	0.30	9403.7~5446.3, 4601.6~4246.7	9	0.768	0.31
	VN	9	0.769	0.31	9403.7~7498.3, 6102.0~5446.3, 4601.6~4424.1	9	0.755	0.32
	MSC	7	0.704	0.39	9403.7~7498.3, 6102.0~5774.1, 4601.6~4424.1	10	0.710	0.35
	FD+VN	2	0.631	0.48	8451.0~7498.3, 4601.6~4424.1	5	0.679	0.37
色素 pigment	FD+VN	6	0.828	0.065	7502.1~6098.1, 5450.1~4246.7	6	0.828	0.065
	None	5	0.796	0.071	9403.7~7498.3, 5450.1~4246.7	6	0.827	0.066
	ECO	4	0.770	0.075	7502.1~6800.1, 5450.1~4246.7	6	0.824	0.066
	MSC	4	0.750	0.078	9403.7~6098.1, 5450.1~4246.7	6	0.813	0.068
	VN	3	0.750	0.079	9403.7~4597.7	6	0.810	0.068
	M-MN	3	0.732	0.081	9403.7~7498.3, 6102.0~4246.7	6	0.806	0.069

项目 item	样品1 sample 1			样品2 sample 2
项目 item	化学值 chemical values/%	P值 P values	平均差异 average difference	化学值 chemical values/%	P值 P values	平均差异 average difference
树脂resin	77.70	0.91	0.04	74.01	0.89	0.08
蜡质wax	5.60	0.99	0.03	4.90	0.98	0.10
色素pigment	1.22	0.98	0.01	1.46	0.99	0.02

Rapid Prediction Model of Primary Components of Sticklac Based on Near Infrared Method

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