Near-infrared Reflectance Spectra Research Articles

Hyperspectral imaging based method for fast characterization of kidney stone types

The formation of kidney stones is a common and highly studied disease, which causes intense pain and presents a high recidivism. In order to find the causes of this problem, the characterization of the main compounds is of great importance. In this sense, the analysis of the composition and structure of the stone can give key information about the urine parameters during the crystal growth. But the usual methods employed are slow, analyst dependent and the information obtained is poor. In the present work, the near infrared (NIR)-hyperspectral imaging technique was used for the analysis of 215 samples of kidney stones, including the main types usually found and their mixtures. The NIR reflectance spectra of the analyzed stones showed significant differences that were used for their classification. To do so, a method was created by the use of artificial neural networks, which showed a probability higher than 90% for right classification of the stones. The promising results, robust methodology, and the fast analytical process, without the need of an expert assistance, lead to an easy implementation at the clinical laboratories, offering the urologist a rapid diagnosis that shall contribute to minimize urolithiasis recidivism.

Rapid Analysis of Apple Leaf Nitrogen using Near Infrared Spectroscopy and Multiple Linear Regression

A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (Rv), 1.119 g kg−1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with Rv = 0.952, RMSEP = 0.633 g kg−1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.

Rapid Prediction of past Climate Condition from Lake Sediments by Near-Infrared (NIR) Spectroscopy

This study explored the feasibility of rapid, nondestructive near-infrared (NIR) reflection spectroscopy for the prediction of conventional physical properties, carbon-nitrogen-sulfur (CNS) analysis, and concentration of inorganic components in sediment cores from a brackish lake. A long core sample, which consisted of well-preserved annually formed lamina from Lake Ogawara along the Pacific coast in Aomori Prefecture, northeastern Japan, was used to investigate the past environmental record. The core was previously analyzed for physical properties, CNS, and inorganic components. Calibration models were developed from NIR reflection spectra of 149 core samples. Partial least squares (PLS) analysis provided good regression models between measured and predicted values for water content, total nitrogen (TN), total organic carbon (TOC), total sulfur (TS), Al(2)O(3), S/Al(2)O(3), Fe(2)O(3)/Al(2)O(3), Sc/Al(2)O(3), Cu/Al(2)O(3), and Zn/Al(2)O(3) with coefficients of determination (r(2)) for cross-validation of 0.73, 0.89, 0.88, 0.73, 0.92, 0.81, 0.82, 0.75, 0.82, and 0.82, respectively. The variation of predicted component values as a function of depth showed the same trend as that of conventionally measured values. This study also showed the possibility of NIR spectroscopy as an on-site, rapid analytical tool for the identification of tephra (fragmental material produced by a volcanic eruption regardless of composition, fragment size, or emplacement mechanism), which is important for dating.

The effect of high temperatures on the mid-to-far-infrared emission and near-infrared reflectance spectra of phyllosilicates and natural zeolites: Implications for martian exploration

Most phyllosilicates on Mars appear to be associated with ancient terrains. As such, they may have experienced shock heating produced by impacts and could have been significantly altered or melted. We characterized the effects of high temperatures on the mid-to-far-infrared (mid-to-far-IR) emission (100–1400cm−1; 7.1–100μm) and near-infrared (NIR) reflectance (1.2–2.5μm) spectra of phyllosilicates by measuring experimentally calcined (100–900°C) phyllosilicates and also two zeolites. Correlated differential scanning calorimetry (DSC) measurements were also performed on each sample to provide insight into the thermal activities of the phyllosilicates and natural zeolites. Our results indicate that all phyllosilicates exhibit characteristic degradations in both NIR and mid-to-far-IR spectral properties between 400 and 800°C, mainly attributable to the dehydroxylation and recrystallization processes as temperature increases. Spectral features of natural zeolites persist to higher temperatures compared to features of phyllosilicates during heating treatments. The thermal behaviors of phyllosilicate infrared (IR) properties are greatly influenced by the compositions of the octahedral cations: (1) changes in both the NIR and mid-to-far-IR spectra of phyllosilicates tend to occur at lower temperatures (300–400°C) in the Fe3+-rich samples as compared to the Al3+-rich types (400–600°C); (2) Mg2+-trioctahedral phyllosilicates hectorite, saponite, and sepiolite all display major mid-to-far-IR spectral changes at 700°C, corresponding to the formation of enstatite; (3) phyllosilicates that have minor replacement of Mg2+ for Al3+ in octahedral positions (e.g. cheto-type montmorillonite and palygorskite) show an absorption band at ∼920cm−1 that becomes strong at 900°C. Inconsistency between spectral behaviors in the mid-to-far-IR and NIR regions is also discussed for phyllosilicates. Results from this study have provided suggestive evidence for the scenario that some phyllosilicates could lose all original spectral features in mid-to-far-IR region while maintaining their characteristic hydration bands in NIR region in the same temperature range.

An Innovative Measurement of Extractable Proteins from Concentrated Latex Containing Eggshell Calcium Oxide Compounds by Near-Infrared Spectroscopy

ABSTRACT Near-infrared reflectance (NIR) spectroscopy, in the spectral range of 1100–2500 nm, was used to measure the amount of the directly extractable proteins (EP) where a small amount can cause a latex allergy to patients. The NIR spectra revealed an amino or a peptide (N-H) of the extractable proteins from the concentrated rubber latex added with sodium dodecyl sulphate (SDS) and calcium chloride, the latter being obtained from the pyrolysis of eggshells at 900°C for 2 hr and dissolving in 2 M HCl. The extractable proteins measured by NIR are equal to 5306.58 ± 1727.00 µg/g (0.53%) close to the value obtained by the modified Lowry method, which is 5566.02 ± 717.39 µg/g (0.56%). A partial least square regression model (PLSR) of the NIR spectra and the extractable protein contents yields a correlation coefficient of .72, a root mean square error of calibration (RMSEC) of 1298 µg/g, and a small bias value of −0.0002 µg/g. The absorbance peaks at the wavelengths of 1520 nm and 1980 nm are related to the first overtone N-H (υ1N-H) and the asymmetric combination of N-H, respectively. Furthermore, the absorbance peaks at the wavelengths of 1450 nm and 1920 nm correspond to the first overtone of O-H (υ1O-H) of the concentrated latex compounds. The results demonstrate the NIR spectroscopy potential as a fast and noninvasive measurement technique.

Sensing of Moisture Content of In-Shell Peanuts by NIR Reflectance Spectroscopy

It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are in their shells, it would save lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low levels of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, Virginia and Valencia, were conditioned to different moisture levels between 6% and 26% (wet basis), and separated into calibration and validation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm intervals were collected from both groups. Measurements were obtained on 30 replicates within each moisture level. Reference MC values for each moisture level in these groups were obtained using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were developed. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the validation sets. Predicted MC values of the validation samples were compared with their standard air-oven moisture values. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 value obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for Valencia and 1.57 for Virginia type in-shell peanuts was selected as the best model. The corresponding R2 values were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR reflectance method, and would be useful for the peanut and allied industries.

Selection of representative calibration sample sets for near-infrared reflectance spectroscopy to predict nitrogen concentration in grasses

The effect of using representative calibration sets with fewer samples was explored and discussed. The data set consisted of near-infrared reflectance (NIR) spectra of grass samples. The grass samples were taken from different years covering a wide range of species and cultivars. Partial least squares regression (PLSR), a chemometric method, has been applied on NIR spectroscopy data for the determination of the nitrogen (N) concentration in these grass samples. The sample selection method based on NIR spectral data proposed by Puchwein and the CADEX (computer aided design of experiments) algorithm were used and compared. Both Puchwein and CADEX methods provide a calibration set equally distributed in space, and both methods require a minimum prior of knowledge. The samples were also selected randomly using complete random, cultivar random (year fixed), year random (cultivar fixed) and interaction (cultivar × year fixed) random procedures to see the influence of different factors on sample selection. Puchwein's method performed best with lowest RMSEP followed by CADEX, interaction random, year random, cultivar random and complete random. Out of 118 samples of the complete calibration set, 19 samples were selected as minimal number of representative samples. RMSEP values obtained for subsets selected using Puchwein, CADEX and using full calibration set were 0.099% N, 0.109% N and 0.092% N respectively. The result indicated that the selection of representative calibration samples can effectively enhance the cost-effectiveness of NIR spectral analysis by reducing the number of analyzed samples in the calibration set by more than 80%, which substantially reduces the effort of laboratory analyses with no significant loss in prediction accuracy.

Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis

The goal of this study was to explore the potential of near-infrared (NIR) hyperspectral imaging in combination with multivariate analysis for the prediction of some quality attributes of lamb meat. In this study, samples from three different muscles ( semitendinosus (ST), semimembranosus (SM), longissimus dorsi (LD)) originated from Texel, Suffolk, Scottish Blackface and Charollais breeds were collected and used for image acquisition and quality measurements. Hyperspectral images were acquired using a pushbroom NIR hyperspectral imaging system in the spectral range of 900–1700 nm. A partial least-squares (PLS) regression, as a multivariate calibration method, was used to correlate the NIR reflectance spectra with quality values of the tested muscles. The models performed well for predicting pH, colour and drip loss with the coefficient of determination ( R 2) of 0.65, 0.91 and 0.77, respectively. Image processing algorithm was also developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of quality parameter in the imaged lamb samples. In addition, textural analysis based on gray level co-occurrence matrix (GLCM) was also conducted to determine the correlation between textural features and drip loss. The results clearly indicated that NIR hyperspectral imaging technique has the potential as a fast and non-invasive method for predicting quality attributes of lamb meat.

Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database

Large soil spectral libraries compiling thousands of NIR (Near Infrared) reflectance spectra have been created encompassing a wide diversity and heterogeneity of spectra. Among the many chemometric approaches to the calibration of chemical and physical properties from these large libraries, local calibrations have the advantage of being able to select the most similar spectra to the spectrum of a target sample. This is particularly relevant when dealing with highly heterogeneous media such as soils, where the mineral matrix has a strong influence on spectral features. A crucial step in the implementation of local calibration procedures is the construction of local neighbourhoods. In this study, we investigate the influence of index computation and neighbour selection on calibration results using local PLSR models on a large soil spectral database. Our indices combine two spectral compression methods (Principal Component Analysis or Fast Fourier Transform) with two distinct distance metrics (Mahalanobis distance or correlation coefficient). Based on a large collection of soil samples provided by the French National Soil Quality Monitoring programme, we constructed calibration models to estimate two chemical (organic carbon and cationic exchange capacity) and two physical (clay and sand content) factors. After neighbour selection, local Partial Least Squares regressions were applied to the selected spectra. Our results highlight the utility of the Fourier transformation of the spectra compared to the classical PCA compression method in achieving a more appropriate neighbourhood selection. We propose an index based on the coefficient correlation with FFT compression that led to a neighbourhood selection giving the best prediction results for the four considered soil constituents.

Near-infrared reflection from Al-doped ZnO films prepared by multi-target reactive sputtering

Thin films of aluminium-doped zinc oxide (ZnO:Al) as heat reflective coatings were prepared by multi-target reactive sputtering using metallic Zn and Al targets. An optimization of Al content and a reduction in oxygen partial pressure were crucial in increasing the carrier concentration Ne and the Hall mobility μ. The ZnO:Al film with the highest Ne achieved the shortest plasma wavelength λp of 1375 nm, which shifted the near-infrared reflectance spectrum closer to the visible region. The high μ reduced the optical absorption and enhanced the reflectance. Moreover, the multi-target system enabled intermittent doping of Al, which was applied to stack multilayers consisting of non-doped and Al-doped ZnO layers. A drop in the refractive indices n above λp for the ZnO:Al layers formed the periodic distribution of n in the thickness direction, which provided a high reflectance zone from 1000 to 1400 nm in wavelength.

Discrimination of meat patés according to the animal species by means of near infrared spectroscopy and chemometrics Discriminación de muestras de paté de carne según tipo de especie mediante el uso de la espectroscopia en el infrarrojo cercano y la quimiometria

Commercial meat paté samples, comprised of 100% pork (n = 7), 100% beef (n = 5) meat, and binary mixtures (beef and pork, w/w) (n = 18) were used. Fresh samples were analysed in a scanning spectrophotometer NIRSystems 6500 in reflectance mode (1100–2500 nm). Principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to classify samples according to the animal species based on their near infrared reflectance (NIR) spectra. Full cross validation was used as validation method when classification models were developed. Both beef and pork paté samples were classified correctly (100%) while binary mixture samples only achieved 72% of correct classification using SLDA technique. The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method to classify paté samples according to meat type. Nevertheless, NIR spectroscopic methods might provide initial screening in the food chain and enable more costly methods to be used more efficiently. Se utilizaron muestras de patés de carne comerciales, compuestos de 100% carne de cerdo (n = 7), 100% carne de buey (n = 5) y mezclas binarias (buey y cerdo) (n = 18). Las muestras frescas se analizaron en un espectrofotómetro de escaneo NIRSystems 6500 en modo reflectante (1100–2500 nm). El análisis de componente principal (PCA) y el análisis discriminante lineal progresivo (SLDA) se usaron para clasificar muestras de según las especie animal basándose en sus espectros NIR. La validación cruzada completa se utilizó como método de validación cuando se desarrollaron métodos de clasificación. Las muestras de ambos patés, de buey y cerdo, se clasificaron correctamente (100%), mientras las muestras de mezcla binaria sólo alcanzaron un 72% de clasificación correcta usando la técnica SLDA. Los resultados mostraron la utilidad del espectro NIR combinado con quimometria como un objetivo y método rápido para clasificar muestras de paté según el tipo de carne. Sin embargo, los métodos espectroscópicos NIR podrían proveer una revisión inicial de la cadena de comida y permitir que métodos costosos fuesen usados más eficientemente.

Improvement of classification using robust soft classification rules for near-infrared reflectance spectral data

The aim of this work was to propose a quick and cost-effective procedure, which could help to identify the types of fat (rapeseed, a mixture of rapeseed and soybean, and lard oils) added to feed used for raising pigs. For this purpose, liver samples were examined and their near-infrared reflectance spectra served as data for the construction of classic and robust soft independent modeling of class analogy (SIMCA) models. The results showed that the near-infrared reflectance spectra contained information sufficient to build good classification models that enabled three types of fat additions to be distinguished. The best classification results were obtained from robust SIMCA, indicating its superior performance in terms of high sensitivity and specificity in comparison with classic SIMCA. Specifically, robust models had sensitivities of 100% and specificities of 96.05%, 97.73% and 100%, for rapeseed, mixture of rapeseed and soybean, and lard enriched feed, respectively.

Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks

In wheat and flour processing, the quality control needs quick analytical tools for predicting physical, rheological, and chemical properties. In this study, near infrared reflectance (NIR) spectroscopy combined with artificial neural network (ANN) was used to predict the flour quality parameters that are protein content, moisture content, Zeleny sedimentation, water absorption, dough development time, dough stability time, degree of dough softening, tenacity (P), extensibility (L), P/G, strength, and baking test (loaf volume and loaf weight). A total of 79 flour samples of different wheat varieties grown in different regions of Turkey were chemically analyzed, and the results of both NIR spectrum (400–2,498 nm) and chemical analysis were used to train/test the network by applying various ANN architectures. Prediction of protein, P, P/G, moisture content, Zeleny sedimentation, and water absorption in particular gave a very good accuracy with coefficient of determination (R2) of 0.952, 0.948, 0.933, 0.920, 0.917, and 0.832, respectively. The results indicate that NIR combined with the ANN can successfully be used to predict the quality parameters of wheat flour.

Varietal Differentiation of Grape Juice Based on the Analysis of Near- and Mid-infrared Spectral Data

The aim of this study was to evaluate the usefulness of visible (VIS), near-infrared reflectance (NIR) and mid-infrared (MIR) spectroscopy combined with pattern recognition methods as tools to differentiate grape juice samples from commercial Australian Chardonnay (n = 121) and Riesling (n = 91) varieties. Principal component analysis (PCA), partial least squares discriminant analysis and linear discriminant analysis (LDA) were applied to classified grape juice samples according to variety based on both NIR and MIR spectra using full cross-validation (leave-one-out) as a validation method. Overall, LDA models correctly classify 86% and 80% of the grape juice samples according to variety using MIR and VIS-NIR, respectively. The results from this study demonstrated that spectral differences exist between the juice samples from different varietal origins and confirmed that the infrared (IR) spectrum contains information able to discriminate among samples. Furthermore, analysis and interpretation of the eigenvectors from the PCA models developed verified that the IR spectrum of the grape juice has enough information to allow the prediction of the variety. These results also suggested that IR spectroscopy coupled with pattern recognition methods holds the necessary information for a successful classification of juice samples of different varieties.

Remote compositional analysis of lunar olivine-rich lithologies with Moon Mineralogy Mapper (M3) spectra

[1] A systematic approach for deconvolving remotely sensed lunar olivine-rich visible to near-infrared (VNIR) reflectance spectra with the Modified Gaussian Model (MGM) is evaluated with Chandrayaan-1 Moon Mineralogy Mapper (M3) spectra. Whereas earlier studies of laboratory reflectance spectra focused only on complications due to chromite inclusions in lunar olivines, we develop a systematic approach for addressing (through continuum removal) the prominent continuum slopes common to remotely sensed reflectance spectra of planetary surfaces. We have validated our continuum removal on a suite of laboratory reflectance spectra. Suites of olivine-dominated reflectance spectra from a small crater near Mare Moscoviense, the Copernicus central peak, Aristarchus, and the crater Marius in the Marius Hills were analyzed. Spectral diversity was detected in visual evaluation of the spectra and was quantified using the MGM. The MGM-derived band positions are used to estimate the olivine's composition in a relative sense. Spectra of olivines from Moscoviense exhibit diversity in their absorption features, and this diversity suggests some variation in olivine Fe/Mg content. Olivines from Copernicus are observed to be spectrally homogeneous and thus are predicted to be more compositionally homogeneous than those at Moscoviense but are of broadly similar composition to the Moscoviense olivines. Olivines from Aristarchus and Marius exhibit clear spectral differences from those at Moscoviense and Copernicus but also exhibit features that suggest contributions from other phases. If the various precautions discussed here are weighed carefully, the methods presented here can be used to make general predictions of absolute olivine composition (Fe/Mg content).

Determination of total soil organic C and hot water-extractable C from VIS-NIR soil reflectance with partial least squares regression and spectral feature selection techniques

The calibration of soil organic C (SOC) and hot water-extractable C (HWE-C) from visible and near-infrared soil reflectance spectra is hindered by the complex spectral interaction of soil chromophores that usually varies from one soil or soil type to another. The exploitation of spectral variables from spectroradiometer data is further affected by multicollinearity and noise. In this study, a set of soil samples (Fluvisols, Podzols, Cambisols and Chernozems; n = 48) representing a wide range of properties was analysed. Spectral readings with a fibre-optics visible to near-infrared instrument were used to estimate SOC and HWE-C contents by partial least squares regression (PLS). In addition to full-spectrum PLS, spectral feature selection techniques were applied with PLS (uninformative variable elimination, UVE-PLS, and a genetic algorithm, GA-PLS). On the basis of normalized spectra (mean centring + vector normalization), the order of prediction accuracy was GA-PLS ≫ UVE-PLS > PLS for SOC; for HWE-C, it was GA-PLS > UVE-PLS, PLS. With GA-PLS, acceptable cross-validated (cv) prediction accuracies were obtained for the complete dataset (SOC, , RPDcv = 2.42; HWE-Ccv, , RPDcv = 2.13). Splitting the soil data into two groups with different basic properties (Podzols compared with Fluvisols/Cambisols; n = 21 and n = 23, respectively) improved SOC predictions with GA-PLS distinctly (Podzols, , RPDcv = 3.14; Fluvisols/Cambisols, , RPDcv = 3.64). This demonstrates the importance of using stratified models for successful quantitative approaches after an initial rough screening. GA selection frequencies suggest that the spectral region over 1900 nm, and in particular the hydroxyl band at 2200 nm are of great importance for the spectral prediction of both SOC and HWE-C.

Measurement of luster in Suri alpaca fiber

A study was conducted to evaluate the ability of two instruments for measuring luster in Suri alpaca fiber and to compare the results with subjectively assessed luster scores. In addition, the study was designed to determine the correlations between luster and other value-determining, objectively measured physical properties of Suri alpaca fiber. For this purpose, 205 Suri alpaca fleece samples were submitted by 49 breeders that represented a broad cross-section of Suri alpaca in terms of visually assessed luster, color, and fiber characteristics. Raw staples and “washed-and-aligned” staples were independently and subjectively assessed for luster by 2 alpaca judges certified by the Alpaca Owners and Breeders Association (AOBA). The fiber samples were measured for luster and luminance using a SAMBA Hair System and were scanned using a near-infrared reflectance spectrometer. Standard objective test methods were used to measure mean fiber diameter and variability (at mid-staple and overall), prickle factor, comfort factor, mean fiber curvature and variability, medullation (white, beige, and light fawn samples only), laboratory scoured yield, mean staple length and variability, and mean staple strength and variability. The precision of the luster measurement (MLUS) was estimated using CV based on 3 measurements per subsample and averaged 3.9% across all colors. In contrast, precision of the luminance measurement (MLUM) was higher with an average CV of 2.3%. Agreement between the subjective luster scores of 2 certified alpaca judges was low (r2=0.17 and 0.10 for raw and washed-and-aligned samples, respectively). Despite this, evidence was obtained linking instrument-measured luster and luminance with subjectively assessed luster scores. Two systems were proposed for expressing instrument-measured luster that will permit comparisons between alpacas of different color. Small negative correlations were present between mean fiber diameter and subjectively assessed luster scores. Small positive correlations were present between mean fiber diameter and objectively measured luster values. A strong correlation (r=−0.94) existed between instrument-measured luster and log10luminance. Prediction equations derived from near-infrared reflectance spectra (NIRS) produced precise and accurate estimates of log10luminance. The NIRS estimates of instrument-measured luster were less precise and less accurate, and estimates of subjectively assessed luster were neither accurate nor precise. The SAMBA Hair System is capable of producing estimates of luster that are accurate enough to be useful to Suri alpaca breeders who intend to select for this trait.

Effects of Loblolly Pine Wood and Pulp Properties on Sheet Characteristics

The effects of wood properties on the strength of bleachable and linerboard grade kraft pulps from 13-year-old loblolly pine (Pinus taeda) trees were investigated. Eighteen trees were selected based on breast height wood cores to represent specified ranges of specific gravity and lignin content. Air-dry density and stiffness (modulus of elasticity [MOE]), tracheid coarseness, radial diameter, tangential diameter, specific surface area, wall thickness, and microfibril angle (MFA) were estimated using SilviScan wood analysis technology and near infrared reflectance (NIR) spectroscopy. NIR spectra collected in 10 mm sections from the surface radial strips correlated very well with air-dry density, MFA, MOE, and tracheid wall thickness and were used to develop whole tree predictions. In addition, chemical composition, fiber properties, and handsheet strength were measured for both pulp grades. Statistical analysis indicated that wood density, wood fiber coarseness, and pulp fiber length had the greatest effects on sheet properties.

Rapid detection of Rosa laevigata polysaccharide content by near-infrared spectroscopy

A method of rapid detecting Rosa laevigata polysaccharide content on the basis of near infrared spectroscopy (NIRS) was established to achieve the purpose of controlling quality of R. laevigata. A total of 129 batches of R. laevigata samples were randomly divided into calibration set and prediction set, number of which were 65 and 64 respectively. The polysaccharide content was measured according to the method provided by Chinese pharmacopoeia, and was 26.05 ± 5.44(%). The Near-infrared diffuse reflectance spectra of R. laevigata were preprocessed by first-order derivative and autoscaling, and was built model with PLS. When 6 Latent variables (LVs) were used in model, the smallest root mean square error of cross validation ( RMSECV) was 1.18%, and root mean square error of prediction ( RMSEP) was 1.21%. The uninformative variables in spectrum were eliminated by UVE-PLS, and 383 variables were obtained. The prediction accuracy was improved, and RMSECV and RMSEP were 0.90% and 0.99% respectively. Then, 383 variables were further optimized by genetic algorithm (UVE-GA-PLS), and 179 variables were obtained; under this condition, the RMSECV and RMSEP were 0.93% and 1.07% respectively. In this work, result of UVE-PLS was the best. Analyzing variables VIP score in PLS, variable selected by UVE-PLS and UVE-GA-PLS, spectrum region from 7200 cm −1 to 6700 cm −1 of the combinations of first and second overtone of C–H and from 5300 cm −1 to5000 cm −1 related to the first overtone of O–H could play an important role in the detection R. laevigata polysaccharide by NIR. Therefore, it was feasible of rapid detection of R. laevigata polysaccharide content by NIRS.

Near Infrared Reflectance Calibration Optimisation to Predict Lignocellulosic Compounds in Sugarcane Samples with Coarse Particle Size

Frequent variations in spectral intensity due to particle size and/or of particle size distribution are observed in plant products processed in powder form and scanned with near infrared reflectance (NIR). In this study, two grinders, with differences in time consumption, practicality and providing homogenates with different particle size range and distribution, were tested to evaluate their effects on NIR spectra. Optimisation of NIR calibration was necessary before predicting lignocellulosic compounds in sugarcane ( Saccharum spp.) samples with coarse particle size to supply a pre-existing ecophysiological growth model. Sixty samples from three varieties, grown in four contrasting pedoclimatic areas and from five anatomical parts were scanned and then analysed by biochemical fractionation. Different calibration methods, resulting in a combination of multiple linear regressions (MLR) applied to three calibration sets (fine, coarse and mixed particle sizes) treated with six data pretreatments—first derivative (D), second derivative (D2), multiplicative scatter correction (MSC), standard normal variate and detrend (SNVD), standard normal variate and detrend successively followed by first derivative (SNVD-D) or second derivative (SNVD-D2)—were investigated. The best NIR model statistical values were obtained by calibration developed on a mixed calibration set treated by SNVD-D2. Results confirmed that NIR spectroscopy could be an accurate and efficient method to predict lignocellulosic compounds in different botanical parts of sugarcane samples when used as input to an ecophysiological growth model.