Influence of moisture on the identification of tropical wood species by NIR spectroscopy DOI
Jhennyfer Nayara Nogueira Gomes, Dayane Targino de Medeiros, Lívia Maria Mello Viana

и другие.

Holzforschung, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Abstract Solutions for species discrimination are important monitoring native timber harvesting. Near-infrared (NIR) spectroscopy has shown promise identifying wood in real time. The influence of moisture content on the model’s performance classifying is not well known. objective was to evaluate effect predictive capacity models based NIR spectra using a benchtop and portable spectrometer. First, signatures were collected radial face specimens at equilibrium (EMC) 11 from Amazonia both equipments. After saturation, new maximum condition subsequently every 10 % water mass loss during drying. Partial least squares discriminant analysis (PLS-DA) developed discriminate according their spectral signatures. Principal component data obtained EMC able depending density gradient specimens. Moisture had no significant impact signal. PLS-DA successfully classified unknown samples by with over 91 accuracy, regardless content. Both devices show strong potential use forest inspections.

Язык: Английский

Influence of moisture on the identification of tropical wood species by NIR spectroscopy DOI
Jhennyfer Nayara Nogueira Gomes, Dayane Targino de Medeiros, Lívia Maria Mello Viana

и другие.

Holzforschung, Год журнала: 2025, Номер unknown

Опубликована: Март 24, 2025

Abstract Solutions for species discrimination are important monitoring native timber harvesting. Near-infrared (NIR) spectroscopy has shown promise identifying wood in real time. The influence of moisture content on the model’s performance classifying is not well known. objective was to evaluate effect predictive capacity models based NIR spectra using a benchtop and portable spectrometer. First, signatures were collected radial face specimens at equilibrium (EMC) 11 from Amazonia both equipments. After saturation, new maximum condition subsequently every 10 % water mass loss during drying. Partial least squares discriminant analysis (PLS-DA) developed discriminate according their spectral signatures. Principal component data obtained EMC able depending density gradient specimens. Moisture had no significant impact signal. PLS-DA successfully classified unknown samples by with over 91 accuracy, regardless content. Both devices show strong potential use forest inspections.

Язык: Английский

Процитировано

0