A challenge for tree breeders and wood quality researchers of today is to respond appropriately to a complex environment demanding more productivity, higher quality, and a quicker adaptation of their crops to rapid changes (
In order to accurately measure the wood traits, there is the need to have systems capable of evaluating the wood properties rapidly, precisely, and at low cost without altering the end-use potential of the wood material. Among the current techniques, Near Infrared (NIR) spectroscopy has come of age and is now prominent among major analytical technologies after the NIR region was discovered in 1800, revived and developed in the early 1950s and put into practice in the 1970s (
NIR spectroscopy is a fast, non-destructive technique (measuring time: 1 min or less) applicable to any biological material, including on-line processes, demanding little or no sample preparation (
In this review, the main challenges for successfully applying NIR spectroscopy in wood are presented. A range of studies dealing with moisture content and portability of NIR devices are examined and the use of NIR-based models in hyperspectral imaging and genetic studies are discussed.
NIR spectroscopy has been appointed as an emerging technology that could provide large data set of wood measurements helping to understand how genetic and environmental factors induce variation in woods (
NIR spectroscopy with aid of multivariate statistics and computational systems is useful for quantitative but even more for qualitative applications, including classification of wood and other biological materials (
For qualitative analysis, the main multivariate technique is Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) while for quantitative studies, the most common techniques for calibrating a predictive model are Principal Component (PC) and Partial Least Squares (PLS) regressions (
The first works that used the NIR spectroscopy technique to characterize wood concentrated on properties directly related to wood chemistry and were based on milled chips obtained from composite whole-tree samples.
Later, NIR spectroscopy approach has been also extended to assess non-chemical characteristics of solid wood samples showing that NIR technology is capable to also estimate physical, mechanical, and anatomical features.
Wood, like many natural materials, is hygroscopic; it takes on moisture from the surrounding environment. Moisture content (MC) exchange between wood and air depends on the relative humidity and temperature of the air and the current amount of water in the wood (
NIR spectroscopy has been widely used to evaluate many wood traits covering a wide range of applications. Many review articles describing a range of applications of NIR spectroscopy in the forest and wood researches are available.
In short, many research teams have been dedicated to the NIR applications on wood and its products, among which we like to highlight the following leading teams: Saturo Tsuchikawa (Japan), Manfred Schwanninger -
Portable NIR systems offer a low-cost alternative to laboratory systems. According to
They also successfully tested calibration transfer of chemometric models between laboratory and in-field instruments. Their findings clearly demonstrated that both instruments may be used for detection of wood defects (abnormal wood) and for their classification (knot, resin pocket and compression wood), although further studies are required for ensuring sufficient reliability of models, compensating variability in the conditions during measurement.
The reliability of the NIR predictions can be verified by validation of models (
Despite many papers have demonstrated that NIR models present good performance in cross-validations or independent validations for wood density (
Spectral imaging is a new technology combining spectral reflectance measurement and image processing technologies (
The idea of hyperspectral imaging is to obtain a NIR spectrum for each pixel in the image with the purpose of identifying variations in wood properties and detecting defects or other characteristics. As the natural variations on the wood quality affect its use in industry, wood is highly suitable for NIR hyperspectral imaging. Several studies have been carried out in regard to this issue in wood and its products.
According to
Few studies have investigated the genetic and environmental control from the NIR signature variations of vegetal materials. The advantage of the estimation of heritability of wood or trees from NIR spectra is that this approach does not require traditional wet chemistry analysis of wood by standard methods for selecting potential trees (and their wood), which are expensive and time consuming.
In perennial crops,
The development of rapid, accurate and industrially feasible methods have become necessary for characterization and classification of raw material in the forestry-related industry, especially for pulp and paper or sawn wood, since these companies require methods able to monitor the quality of a large number of samples.
The main challenges to be overcome in order to make the NIR spectroscopy an applicable, reliable technique in field conditions are moisture and portability. Fresh wood, bark, fruits, seeds and leaves can present high levels of moisture and NIR spectra is very sensitive from 1400 to 1900 nm, where two main hydroxyl absorption peaks occurs. Moreover, there is a need for portable NIR equipment capable to record NIR spectra with low noise and low sensitivity to temperature and humidity variations of the air, as well as other common sources of variation to forest environments. Studies concerning the sample preparation effects on NIR calibrations and investigations about the robustness of the calibrations also are required.
Many researches have demonstrated that NIR spectroscopy and multivariate analysis work well in laboratories where the conditions are controlled. However, there is still a gap between laboratory research and real situations concerning the performance of NIR models in wood. Studies showing the success of NIR-based models in real situations, taking into account variations in moisture, granulometry, surface quality, temperature, and other sources of variation present in factories conditions would be useful.