基于近红外光谱分析技术的桉木纤维施胶量高低快速判别

doi:10.11707/j.1001-7488.20210814

Abstract

Abstract:

Object: In order to provide technical support for applying glue working procedure and determination of resin content, this paper proposed a method by near infrared spectroscopy (NIR). Method: Eucalyptus fibers were used as the research objects and resin content was used as the discriminative index. For the same batch of eucalyptus fiber, its moisture content was about 5%. Four kinds of resin contents, including 0%, 3%, 12% and 20%, were established for four categories of none, low, medium and high level, respectively. The partial least squares regression (PLS) model between the four categories and those NIR was established. Partial least-squares discrimination analysis (PLS-DA) was used to detect the effects of collecting spectra (dynamic and static) and fibers setting time. Result: 1) With the increase of resin content, the absorbance of NIR increased. Four categories of eucalyptus fibers could be distinguished. The spectrum data were established into high relevance and accuracy models, the accuracy of the models were 100%. 2) Collecting spectra (dynamic and static) did not affect the modeling, the accuracy of the models were 100%. If fibers were set a long time, the accuracy of the models were nearly 0%. Conclusion: According to the wood fiber of different resin contents, the absorbance of NIR was different, the resin content of wood fibers could be accurately determined by PLS-DA. Through the laboratory simulation experiment, dynamic or static fibers did not affect the accuracy of model establishment and discrimination. It was expected to provide some ideas for online determination of resin content of wood fiber.

Key words: eucalyptus wood fiber, fiberboard, resin content, near infrared spectroscopy (NIR)

CLC Number:

S784

Hanwen Zhu,Guanben Du,Zhong Yang,Bin Lü,Shenggao Lu. Determination of Resin Content of Eucalyptus Wood Fiber by Near Infrared Spectroscopy[J]. Scientia Silvae Sinicae, 2021, 57(8): 141-146.

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项目Item	施胶量Resin content(%)
项目Item	0	3	12	20
施胶量 Resin content	无None	低Low	中Middle	高High
分类变量 Category variables	N[1 0 0 0]	L[0 1 0 0]	M[0 0 1 0]	H[ 0 0 0 1]

样本集 Sample		施胶量Resin content(%)
样本集 Sample		0	3	12	20
校正 Calibration	R²	0.98	0.89	0.86	0.95
	SEC	0.06	0.14	0.16	0.10
	Bias	-5.29×10^-8	1.17×10^-7	0	-7.23×10^-8
验证 Validation	R²	0.97	0.80	0.68	0.91
	SEP	0.08	0.19	0.24	0.13
	Bias	1.97×10^-3	1.62×10^-3	-7.70×10^-3	4.11×10^-3

类别 Category	预测值 Predictions(Y_P)	偏差 Deviation(D)	类别 Category	预测值 Predictions(Y_P)	偏差 Deviation(D)
N	0.98	0.08	M	0.75	0.18
N	0.88	0.10	M	0.94	0.27
N	0.96	0.08	M	0.76	0.22
N	0.99	0.10	M	1.12	0.19
N	0.99	0.11	M	1.12	0.21
N	0.99	0.10	M	0.77	0.21
N	0.93	0.08	M	0.74	0.22
N	0.98	0.08	M	0.86	0.24
N	0.78	0.13	M	0.68	0.23
N	1.03	0.09	M	0.97	0.23
L	0.75	0.13	H	0.95	0.08
L	0.74	0.17	H	0.77	0.09
L	0.73	0.17	H	0.97	0.08
L	0.98	0.15	H	1.03	0.09
L	0.81	0.21	H	0.66	0.11
L	0.89	0.17	H	0.88	0.10
L	1.00	0.20	H	1.04	0.09
L	0.63	0.17	H	1.01	0.14
L	0.66	0.23	H	0.85	0.10
L	0.92	0.17	H	0.88	0.11

样本集 Sample		施胶量Resin content(%)
样本集 Sample		0	3	12	20
校正 Calibration	R²	0.99	0.92	0.84	0.96
	SEC	0.05	0.12	0.17	0.09
	Bias	-3.40×10^-8	0	1.13×10^-7	-7.10×10^-8
验证 Validation	R²	0.98	0.85	0.73	0.95
	SEP	0.06	0.16	0.23	0.10
	Bias	-2.77×10^-4	3.29×10^-3	-3.33×10^-3	3.19×10^-4

类别 Category	预测值 Predictions(Y_P)	偏差 Deviation(D)	类别 Category	预测值 Predictions(Y_P)	偏差 Deviation(D)
N	0.95	0.06	M	0.71	0.19
N	0.95	0.07	M	0.71	0.20
N	0.90	0.07	M	0.81	0.17
N	1.03	0.08	M	0.59	0.19
N	0.93	0.06	M	0.96	0.24
N	0.97	0.07	M	1.07	0.20
N	1.03	0.07	M	0.65	0.18
N	1.04	0.04	M	0.92	0.19
N	1.02	0.08	M	0.93	0.20
N	0.98	0.07	M	1.01	0.25
L	0.75	0.21	H	0.94	0.06
L	0.98	0.19	H	0.89	0.07
L	1.06	0.17	H	0.98	0.08
L	1.01	0.13	H	1.12	0.11
L	1.06	0.17	H	1.02	0.09
L	0.78	0.16	H	0.88	0.08
L	1.11	0.18	H	0.93	0.08
L	0.95	0.13	H	0.98	0.08
L	0.91	0.21	H	0.99	0.07
L	0.95	0.19	H	1.02	0.08

Determination of Resin Content of Eucalyptus Wood Fiber by Near Infrared Spectroscopy

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预测模型Prediction model(n=10)	N	L	M	H
正确个数Correct number	10	10	10	10
错误个数Wrong number	0	0	0	0
判别正确率Accuracy(%)	100	100	100	100

类别 Category	P-M_6h		P-M_24h
类别 Category	预测值 Predictions (Y_P)	偏差 Deviation (D)	预测值 Predictions (Y_P)	偏差 Deviation (D)
M_6h	0.28	0.32	0.29	0.33
M_6h	-0.06	0.38	-0.12	0.25
M_6h	0.14	0.38	0.34	0.27
M_6h	0.21	0.30	0.16	0.24
M_6h	0.35	0.37	0.39	0.33
M_6h	0.15	0.30	0.36	0.24
M_6h	0.11	0.24	0.26	0.22
M_6h	-0.02	0.36	0.10	0.28
M_6h	-0.10	0.34	0.31	0.31
M_6h	0.14	0.30	0.59	0.32
M_24h	0.02	0.27	0.33	0.30
M_24h	0.10	0.29	0.26	0.27
M_24h	0.47	0.31	0.21	0.32
M_24h	-0.23	0.32	0.03	0.27
M_24h	0.10	0.28	0.28	0.35
M_24h	-0.02	0.33	0.11	0.31
M_24h	0.06	0.27	0.35	0.27
M_24h	0.40	0.35	0.68	0.30
M_24h	-0.08	0.43	0.05	0.27
M_24h	0.29	0.34	0.38	0.31

预测模型 Prediction model(n=20)	6 h	24 h
正确个数Correct number	0	2
错误个数Wrong number	20	18
判别正确率Accuracy(%)	0	10

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