Identifying Plant Part Composition of Forest Logging Residue Using Infrared Spectral Data and Linear Discriminant Analysis

Characterization and Prediction of Mechanical and Chemical Properties of Luanta Fir Wood with Vacuum Hydrothermal Treatment

Since the chemical composition of wood is closely related to its mechanical properties, chemical analysis techniques such as near-infrared (NIR) spectroscopy provide a reasonable non-destructive method for predicting wood strength. In this study, we used NIR spectra with principal component analysis (PCA) to reveal that vacuum hydrothermal (VH) treatment causes degradation of hemicellulose as well as the amorphous region of cellulose, resulting in lower hydroxyl and acetyl group content. These processes increase the crystallinity of the luanta fir wood (Cunninghamia konishii Hayata), which, in turn, effectively increases its compressive strength (σc,max), hardness, and modulus of elasticity (MOE). The PCA results also revealed that the primary factors affecting these properties are the hemicellulose content, hydroxyl groups in the cellulose amorphous region, the wood moisture content, and the relative lignin content. Moreover, the ratios of performance deviation (RPDs) for the σc,max, shear strength (σs,max), hardness, and modulus of rupture (MOR) models were 1.49, 1.24, 1.13, and 2.39, indicating that these models can be used for wood grading (1.0 < RPD < 2.5). Accordingly, NIR can serve as a useful tool for predicting the mechanical properties of VH-treated wood.

Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.

Determination of soybean routine quality parameters using near-infrared spectroscopy

Large differences in quality existed between soybean samples. In order to rapidly detect soybean quality between samples from different areas, we have developed near-infrared spectroscopy (NIRS) models for the moisture, crude fat, and protein content of soybeans, based on 360 soybean samples collected from different areas. Compared with whole kernels, soybean powder with particle sizes of 60 mesh was more suitable for modeling of moisture, crude fat, and protein content. To increase the reproducibility of the prediction model, uniform particle sizes of soybeans were prepared by grinding and sieving soybeans with different sizes and colors. Modeling analysis showed that the internal cross-validation correlation coefficients (Rcv) for the moisture, crude fat, and protein content of soybeans were .965, .941, and .949, respectively, and the determination coefficients (R2) were .966, .958, and .958. NIRS performed well as a rapid method for the determination of routine quality parameters and provided reference data for the analysis of soybean quality using FT-NIRS.

Characterization of postmortem biochemical changes in rabbit plasma using ATR-FTIR combined with chemometrics: A preliminary study

Postmortem interval (PMI) determination is one of the most challenging tasks in forensic medicine due to a lack of accurate and reliable methods. It is especially difficult for late PMI determination. Although many attempts with various types of body fluids based on chemical methods have been made to solve this problem, few investigations are focused on blood samples. In this study, we employed an attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique coupled with principle component analysis (PCA) to monitor biochemical changes in rabbit plasma with increasing PMI. Partial least square (PLS) model was used based on the spectral data for PMI prediction in an independent sample set. Our results revealed that postmortem chemical changes in compositions of the plasma were time-dependent, and various components including proteins, lipids and nucleic acids contributed to the discrimination of the samples at different time points. A satisfactory prediction within 48h postmortem was performed by the combined PLS model with a good fitting between actual and predicted PMI of 0.984 and with an error of ±1.92h. In consideration of the simplicity and portability of ATR-FTIR, our preliminary study provides an experimental and theoretical basis for application of this technique in forensic practice.

Identification of Skin Electrical Injury Using Infrared Imaging: A Possible Complementary Tool for Histological Examination

In forensic practice, determination of electrocution as a cause of death usually depends on the conventional histological examination of electrical mark in the body skin, but the limitation of this method includes subjective bias by different forensic pathologists, especially for identifying suspicious electrical mark. The aim of our work is to introduce Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics as a complementary tool for providing an relatively objective diagnosis. The results of principle component analysis (PCA) showed that there were significant differences of protein structural profile between electrical mark and normal skin in terms of α-helix, antiparallel β-sheet and β-sheet content. Then a partial least square (PLS) model was established based on this spectral dataset and used to discriminate electrical mark from normal skin areas in independent tissue sections as revealed by color-coded digital maps, making the visualization of electrical injury more intuitively. Our pilot study demonstrates the potential of FTIR spectroscopy as a complementary tool for diagnosis of electrical mark.

Nonlinear Fusion of Multispectral Citrus Fruit Image Data with Information Contents

The main issue of vison-based automatic harvesting manipulators is the difficulty in the correct fruit identification in the images under natural lighting conditions. Mostly, the solution has been based on a linear combination of color components in the multispectral images. However, the results have not reached a satisfactory level. To overcome this issue, this paper proposes a robust nonlinear fusion method to augment the original color image with the synchronized near infrared image. The two images are fused with Daubechies wavelet transform (DWT) in a multiscale decomposition approach. With DWT, the background noises are reduced and the necessary image features are enhanced by fusing the color contrast of the color components and the homogeneity of the near infrared (NIR) component. The resulting fused color image is classified with a C-means algorithm for reconstruction. The performance of the proposed approach is evaluated with the statistical F measure in comparison to some existing methods using linear combinations of color components. The results show that the fusion of information in different spectral components has the advantage of enhancing the image quality, therefore improving the classification accuracy in citrus fruit identification in natural lighting conditions.

Rapid Quantitative Analysis of Forest Biomass Using Fourier Transform Infrared Spectroscopy and Partial Least Squares Regression

Fourier transform infrared reflectance (FTIR) spectroscopy has been used to predict properties of forest logging residue, a very heterogeneous feedstock material. Properties studied included the chemical composition, thermal reactivity, and energy content. The ability to rapidly determine these properties is vital in the optimization of conversion technologies for the successful commercialization of biobased products. Partial least squares regression of first derivative treated FTIR spectra had good correlations with the conventionally measured properties. For the chemical composition, constructed models generally did a better job of predicting the extractives and lignin content than the carbohydrates. In predicting the thermochemical properties, models for volatile matter and fixed carbon performed very well (i.e., R² > 0.80, RPD > 2.0). The effect of reducing the wavenumber range to the fingerprint region for PLS modeling and the relationship between the chemical composition and higher heating value of logging residue were also explored. This study is new and different in that it is the first to use FTIR spectroscopy to quantitatively analyze forest logging residue, an abundant resource that can be used as a feedstock in the emerging low carbon economy. Furthermore, it provides a complete and systematic characterization of this heterogeneous raw material.