Estimation of phosphorus-based flame retardant in wood by hyperspectral imaging—a new method

Abstract

It is recognised that flame retardant chemicals degrade and leach out of flame-protected wood claddings when exposed to natural weathering. However, the ability to survey the current state of a flame retardant treatment applied to a wood cladding, an arbitrary length of time after the initial application, is limited today. In this study, hyperspectral imaging in the near infrared to short-wavelength infrared region is used to quantify the amount of flame retardant present on wooden surfaces. Several sets of samples were treated with various concentrations of a flame retardant chemical and scanned with a push broom hyperspectral camera. An inductively coupled plasma (ICP) spectroscopy analysis of the outermost layer of the treated samples was then carried out in order to determine each sample’s phosphorus content, the active ingredient in the flame retardant. Spectra from the hyperspectral images were pre-processed with extended multiplicative scatter correction, and the phosphorus content was modelled using a partial least squares (PLS) regression model. The PLS regression yielded robust predictions of surface phosphorus content with a coefficient of determination, R2, between 0.8 and 0.9 on validation data regardless of whether the flame retardant chemical had been applied to the surface of the wood or pressure-impregnated into it. The result from the study indicates that spectral imaging around the 2400–2531 nm wavelength region is favourable for quantifying the amount of phosphorus-based flame retardant contained in the outermost layer of non-coated wooden claddings. The results also reveal that the uptake of phosphorus-based flame retardant does not occur uniformly throughout the wood surface, but is to a larger extent concentrated in the earlywood regions than in the latewood.

Fast method for GA‐PLS with simultaneous feature selection and identification of optimal preprocessing technique for datasets with many observations

“…The size of the design matrix X containing all the absorbance data is 10 5 × 256. For a more comprehensive description of this dataset the reader is referred to The second dataset consists of six hyperspectral time series sequences.…”

Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

“…As a next step, we recommend that principal component analysis (PCA) be performed. PCA is used in practically every scientific discipline that acquires multivariate signals or large numbers of spectra 33,37–42 . Indeed, PCA is ubiquitous to multivariate analysis, and understanding PCA facilitates an understanding of other factor‐based analysis methods.…”

“…PCA is used in practically every scientific discipline that acquires multivariate signals or large numbers of spectra. 33,[37][38][39][40][41][42] Indeed, PCA is ubiquitous to multivariate analysis, and understanding PCA facilitates an understanding of other factor-based analysis methods. We believe it represents the next level of analysis/ sophistication that can be applied to large XPS data sets.…”

Comparative Performance of NIR-Hyperspectral Imaging Systems

“…Studies have also examined chemical composition variation across the surface of discs [29,30] and across small sections (e.g., 4 mm 2 area) across radial, tangential and transverse surfaces [31]. Wood degradation either by fungi [32] or weathering processes [33][34][35] and treatments to protect wood, including acetylation [36,37], oil impregnation [38], thermal modification [39], and the concentration of phosphorus-based flame retardants [40], have all been investigated. Hyperspectral images have also been used for species classification [41,42], characterization of juvenile and mature wood [43], particleboard identification [44], and to investigate how the presence of knots and holes influences veneer modulus of elasticity [45].…”