Prediction of the basic density of tropical woods by near-infrared spectroscopy

Medeiros, Dayane Targino de; Melo, Rafael Rodolfo de; Cademartori, Pedro Henrique Gonzalez de; Batista, Felipe Gomes; Mascarenhas, Adriano Reis Prazeres; Scatolino, Mário Vanoli; Hein, Paulo Ricardo Gherardi

doi:10.1590/01047760202329013262

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TECHNOLOGY OF FOREST PRODUCTS • CERNE 29 • 2023 • https://doi.org/10.1590/01047760202329013262 copy

Prediction of the basic density of tropical woods by near-infrared spectroscopy

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Background:

Determining the basic density of the wood is usually defined as a simple process, but it requires caution and the operator’s skill to avoid errors in the analysis. In addition, it involves sample preparation and time to saturate the wood until obtaining the dry sample mass. The development of alternative measurement techniques could reduce the time to obtain the results and provide reliable values. Therefore, this study aimed to develop multivariate models to estimate the basic density of native woods using near-infrared spectra (NIR). Basic densities were determined by the water immersion method, and the values were associated with NIR signatures. Spectra were directly collected on the wood transversal and radial faces with an integrating sphere. Partial least squares regression (PLS-R) was calibrated and validated to estimate basic density based on spectral signatures.

Results:

In the cross-validation and prediction of the models, the results were promising. The coefficients of determination varied from 0.87 to 0.93 with a standard error of 0.01 %. The partial least squares discriminant analysis (PLS-DA) efficiently classified the wood species. The ratio of performance to deviation obtained satisfactory values, a minimum of 2.81 and a maximum of 4.20.

Conclusion:

The statistical parameters of the models based on NIR spectra showed potential for density measurements in floors, furniture, and solid wood products.

Keywords:
Multivariate statistics; Non-destructive analysis; Amazonian species; wood identification; classification.

HIGHLIGHTS

The NIRS technique was effective in predicting the basic density of tropical woods. The determination coefficients ranged from 0.87 to 0.93 with a standard error of 0.01%. For all models, PC1 explained more than 90% of the data variation, and the PLS-DA classified the species with 100% success. Bowdichia nitida showed higher NIR absorbance due to the high density of the wood (0.81 g.cm-3).

Scientific name	Family	Basic density (g.cm^-³)
Bowdichia nitida	Fabaceae	0.81 ± 0.01 a
Manilkara huberi	Sapotaceae	0.80 ± 0.02* a
Caryocar villosum	Caryocaraceae	0.76 ± 0.01 b
Piptadenia suaveolens	Fabaceae	0.71 ± 0.01 c

Treatment	PC1	PC2	Total
Untreated	99.49	0.45	99.94
1d	98.76	0.71	99.47
2d	90.62	2.78	93.40

Validation type	NIR (spectra classification by species)					Correct classification
Validation type	Species	B. nitida	M. huberi	C. villosum	P. suaveolens	N°	%
Cross-validation	B. nitida	100				100	100
	M. huberi		100			100	100
	C. villosum			100		100	100
	P. suaveolens				100	100	100
Test set	B. nitida	40				40	100
	M. huberi		40			40	100
	C. villosum			40		40	100
	P. suaveolens				40	40	100

Face	Model	Treatments	LV	Cross-validation (n = 200)			Test set validation (n = 80)
Face	Model	Treatments	LV	R²cv	RMSEcv	RPD	R²p	RMSEp	RPD
Transversal	1	Untreated	10	0.93	0.01	3.80	0.93	0.01	4.20
	2	1d	8	0.93	0.01	3.76	0.93	0.01	3.93
	3	2d	6	0.89	0.01	3.00	0.87	0.01	2.82
Radial	4	Untreated	8	0.91	0.01	3.42	0.89	0.01	3.02
	5	1d	6	0.92	0.01	3.62	0.92	0.01	3.56
	6	2d	6	0.88	0.01	2.90	0.87	0.01	2.81

UFLA - Universidade Federal de Lavras Universidade Federal de Lavras - Departamento de Ciências Florestais - Cx. P. 3037, 37200-000 Lavras - MG Brasil, Tel.: (55 35) 3829-1706, Fax: (55 35) 3829-1411 - Lavras - MG - Brazil
E-mail: cerne@dcf.ufla.br

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[1] Corresponding author: rafael.melo@ufersa.edu.br