Near-infrared Reflectance Models Research Articles

The mechanisms and prediction of non-structural carbohydrates accretion and depletion after mechanical wounding in slash pine (Pinus elliottii) using near-infrared reflectance spectroscopy

BackgroundThe allocation of non-structural carbohydrates (NSCs) plays a critical role in the physiology and metabolism of tree growth and survival defense. However, little is known about the allocation of NSC after continuous mechanical wounding of pine by resin tapping during tree growth.ResultsHere, we examine the NSC allocation in plant tissues after 3 year lasting resin tapping, and also investigate the use of near-infrared reflectance (NIR) spectroscopy to quantify the NSC, starch and free sugar (e.g., sucrose, glucose, and fructose) concentrations in different plant tissues of slash pine. Spectral measurements on pine needle, branch, trunk phloem, and root were obtained before starch and free sugar concentrations were measured in the laboratory. The variation of NSC, starch and free sugars in different plant tissues after resin tapping was analyzed. Partial least squares regression was applied to calibrate prediction models, models were simulated 100 times for model performance and error estimation. More NSC, starch and free sugars were stored in winter than summer both in tapped and control trees. The position of resin tapping significantly influenced the NSCs allocation in plant tissues: more NSCs were transformed into free sugars for defensive resin synthesis close to the tapping wound rather than induced distal systemic responses. Models for predicting NSC and free sugars of plant tissues showed promising results for the whole tree for fructose (R2CV = 0.72), glucose (R2CV = 0.67), NSCs (R2CV = 0.66) and starch (R2CV = 0.58) estimates based on NIR models. Models for individual plant tissues also showed reasonable predictive ability: the best model for NSCs and starch prediction was found in root. The significance multivariate correlation algorithm for variable selection significantly reduced the number of variables. Important variables were identified, including features at 1021–1290 nm, 1480, 1748, 1941, 2020, 2123 and 2355 nm, which are highly related to NSC, starch, fructose, glucose and sucrose.ConclusionsNIR spectroscopy provided a rapid and cost-effective method to monitor NSC, starch and free sugar concentrations after continuous resin tapping. It can be used for studying the trade-off between growth and production of defensive metabolites.

Model Development for Fat Mass Assessment Using Near-Infrared Reflectance in South African Infants and Young Children Aged 3-24 Months.

Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3–24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3–24 months (all subjects), 0–6 months, and 7–24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.

Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis.

The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO2) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO2 levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO2 level leaves and 100% of the current-CO2 level leaves. The spectral bands accounting for the discrimination of the elevated-CO2 leaves were at 1657 and 1698nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO2, returning to current-CO2 levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO2 levels. The fingerprints suggest that after suspension of elevated-CO2, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

Rapid and simultaneous analysis of five free anthraquinone contents in rhubarb during the stir-frying with rice wine process by near infrared reflectance spectroscopy

Background: The active ingredients of Traditional Chinese Medicines (TCMs) vary greatly with the degree of stir-frying; so, rapid analysis of the active content is very important for the processing of TCMs. Objective: In this study, near infrared reflectance (NIR) spectroscopy was used to develop a new method for the rapid online analysis of five free anthraquinones (aloe-emodin, rhein, emodin, chrysophanol, and physcion) during the stir-frying process for rhubarb. Materials and Methods: With partial least-squares (PLSs) and artificial neural networks (ANN) regression, calibration models were generated based on five free anthraquinone contents, as measured by high-performance liquid chromatography. Results: The results indicated that the 2 types of models were robust, accurate, and repeatable for five free anthraquinones. Moreover, PLS as the linear model was more suitable for developing the NIR models of the five free anthraquinones than ANN. The performance of the optimal models was achieved as follows: the coefficient of determination for prediction (R2pre) for aloe-emodin, rhein, emodin, chrysophanol, and physcion was 0.9161, 0.9699, 0.9655, 0.9611, and 0.9724, respectively; the root mean square error of prediction was 0.0251, 0.0445, 0.3333, 0.0862, and 0.0211, respectively. Conclusion: The established NIR models could apply to determine the content of five free anthraquinones in rhubarb. This work demonstrated that NIR may be an effective online analysis method to reflect the quality of TCM industrial manufacturing processes.

Terpenes associated with resistance against the gall wasp, Leptocybe invasa, in Eucalyptus grandis.

Leptocybe invasa is an insect pest causing gall formation on oviposited shoot tips and leaves of Eucalyptus trees leading to leaf deformation, stunting, and death in severe cases. We previously observed different constitutive and induced terpenes, plant specialized metabolites that may act as attractants or repellents to insects, in a resistant and susceptible clone of Eucalyptus challenged with L.invasa. We tested the hypothesis that specific terpenes are associated with pest resistance in a Eucalyptus grandis half-sib population. Insect damage was scored over 2 infestation cycles, and leaves were harvested for near-infrared reflectance (NIR) and terpene measurements. We used Bayesian model averaging for terpene selection and obtained partial least squares NIR models to predict terpene content and L.invasa infestation damage. In our optimal model, 29% of the phenotypic variation could be explained by 7 terpenes, and the monoterpene combination, limonene, α-terpineol, and 1,8-cineole, could be predicted with an NIR prediction ability of .67. Bayesian model averaging supported α-pinene, γ-terpinene, and iso-pinocarveol as important for predicting L.invasa infestation. Susceptibility was associated with increased γ-terpinene and α-pinene, which may act as a pest attractant, whereas reduced susceptibility was associated with iso-pinocarveol, which may act to recruit parasitoids or have direct toxic effects.

Potential of near infrared spectroscopy to monitor variations in soluble sugars in Loblolly pine seedlings after cold acclimation

The freeze tolerance of seedlings can be affected by cold acclimation. This acclimation can in turn alter the seedlings absorbance spectra due to variation of soluble sugar content (SSC). If models developed from the absorbance at wavelengths in the near infrared reflectance (NIR) region could provide information concerning the variation in SSC, nursery managers could assess the freeze tolerance of each seedling before planting. The objective of this research was to investigate the potential to use NIR based analysis to study the variation of SSC in Loblolly pine (Pinus taeda L.) seedlings after cold acclimation. Seventy seedlings were frozen using different cold treatments and 10 seedlings were designated as controls. The standard wet chemistry method was employed to collect the original SSC data, including glucose, fructose and galactose. NIR spectroscopy was completed by using a NIR machine, with a range of 10,000–4000cm−1. The results were analyzed using partial least squares regression in an effort to determine the SSC in the leaves, stems and roots. Results showed that the freeze tolerance of seedlings increased after cold acclimation and was positively related to the presence of SSC. After cold acclimation, leaves had the most SSC, and the galactose content was more than that of glucose or fructose in seedlings. Based on the determination coefficient (R2) and the residual predictive deviation (RPD), the predicted results of NIR models were evaluated. The model for leaf galactose (R2=0.88 and RPD=2.17) was the most stable and accurate model. This paper demonstrates that NIR coupled with chemometric modeling can be a useful tool in the monitoring of SSC variation and this information could be useful in the prediction of freeze tolerance.

Organic substrate, clay type, texture, and water influence on NIR carbon measurements

Near-infrared reflectance (NIR) spectroscopic detection of soil organic carbon (SOC) is an alternative to more resource-intensive methods. However, interrelated impacts from specific soil characteristics on NIR performance are not well understood. To address this, a population of artificial soils was created based on several primary soil characteristics, and a single optimized NIR model's predictive capability was compared by each soil characteristic subset. Objectives were to determine how NIR prediction of SOC is affected by: i) SOC substrate and primary soil constituents [quartz sand, leonardite, and manufactured humic acid (MHA)]; ii) clay mineral [i.e. two standards (kaolinite and smectite)] and percent composition [three textures per clay type (0, 50, and 98.6%)]; and, iii) hydroscopic soil water (hydrated and non-hydrated) content, with three replications. The RPD statistic (ratio of performance to deviation) further compared prediction accuracies within soil subsets, and was 2.04 for the model across the entire set. NIR predicted SOC in the MHA better (R2 = 0.86) than naturally derived leonardite (R2 = 0.74). The greatest factor was texture, with prediction improvement for lower clay content (from R2 = 0.79 to 0.92 for 98.6% to 0% clay, respectively). Clay type and hydration affected accuracy of SOC prediction for samples created with leonardite. Amongst leonardite samples, SOC prediction improved in kaolinitic-hydrated (hydroscopic water) compared to smectitic soils. Conversely, in oven-dry soils, SOC was almost equally accurate for smectitic (R2 = 0.77) and kaolinitic (R2 = 0.78). Consequently, NIR calibration sets developed per soil population textural characteristics should improve SOC prediction.

Chemical Composition and Calorific Value Prediction of Wheat Straw at Different Maturity Stages Using Near-Infrared Reflectance Spectroscopy

Rapid assessment of wheat straw at different maturity stages will help to reveal its growth mature and enable better process control that will optimize the sustainable value-added usage. This study explored the potential of near-infrared reflectance (NIR) spectroscopy to quantitatively and qualitatively analyze the multiple chemical composition and calorific value of wheat straw at different maturity stages. Partial least-squares (PLS) and genetic algorithm and partial least-squares (GA-PLS) models were used for NIR spectroscopy analysis. Results showed that PLS models and GA-PLS models could be both used for the estimation of chemical composition and calorific value of wheat straw at three maturity stages, and the GA-PLS method reduced the spectral variables for modeling and provided sensitive spectral variables correlated well with chemical composition and calorific value. NIR spectroscopy could successfully detect the contents of water-soluble carbohydrates (WSC), crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), ash, and nitrogen (N). It was also able to quantify dry matter (DM), cellulose (Cel), moisture (Moist), volatile matter (VM), higher heating value (HHV), and lower heating value (LHV). The NIR models for hemicellulose (Hem), lignin (Lig), carbon (C), sulfur (S), and hydrogen (H) had moderate accuracy, and could be used for qualitative analysis, whereas for fixed carbon (FC), it was suitable for screening purposes.

Rapid assessment of coniferous biomass lignin–carbohydrates with near-infrared spectroscopy

The main objective of this research was to construct accurate near-infrared reflectance (NIR) models of wood chemistry. Wet chemistry procedures and high-performance liquid chromatography methods were employed to analyze the chemical composition of southern pine. The NIR spectra were collected from 21 wood samples, which were milled down to different particle size classes. NIR calibration and prediction models were established using two modeling methods with different pretreatments. Furthermore, the spectrum range used in the NIR models was refined to achieve higher prediction accuracy. Results showed that NIR model precision could be improved considerably by decreasing the particle size to a very fine powder coupled with a targeted spectrum range. Superior prediction models for lignin and holocellulose content were constructed, while models for extractives and cellulose contents were also strong.

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.

Rapid Characterization of Woody Biomass Digestibility and Chemical Composition Using Near-infrared SpectroscopyFree Access

Rapid determination of the properties of lignocellulosic material is highly desirable for biomass production and utilization. In the present study, measurements of woody biomass digestibility and chemical composition using near-infrared reflectance (NIR) spectroscopy were calibrated. Poplar and eucalyptus materials were recorded in NIR spectrum as well as determined for their chemical compositions of Klason lignin, α-cellulose, holocellulose, lignin syringyl/guaiacyl (S/G) ratio and enzymatic digestibility. Fitting of the NIR information with chemical properties and digestibility by partial least-squares (PLS) regression generated a group of trained NIR models that were able to be used for rapid biomass measurement. Applying the models for woody biomass measurements led to a reliable evaluation of the chemical composition and digestibility, suggesting the feasibility of using NIR spectroscopy in the rapid characterization of biomass properties.

Design, Construction, and Testing of an Automated NIR In-line Analysis System for Potatoes. Part I: Off-line NIR Feasibility Study for the Characterization of Potato Composition

An off-line near-infrared reflectance (NIR) feasibility study was conducted to explore the critical steps in the NIR determination of the major potato constituents (dry matter, starch, and protein) in relatively large (10 kg) potato samples. The results were important for the design of an automated industrial analysis system for potatoes with in-line NIR. The 10-kg potato samples were pulped with an industrial rotary saw blade rasp. A critical step in the NIR measurements was the occurrence of phase separation in the potato pulp. Phase separation manifests itself directly after pulping the potatoes and significantly affects the NIR spectrum. Therefore, during the NIR measurements, the potato pulp had to be stirred continuously. The NIR spectra (1,100-2,500 nm) were measured by applying an optical fiber NIR probe (EDAPT-1) connected to the NIR spectrophotometer (Technicon Infralyzer IA 500). NIR models for the concentration of dry matter, starch, and coagulating protein in potatoes have been developed. With the partial least squares regression procedure, promising NIR models were calculated. The NIR models were validated using an independent validation set of potato samples. The root mean square error in prediction of the samples in the validation set was 0. 5% (w/w) for dry matter, 0. 63 (w/w) for starch concentration, and 0. 06% (w/w) for the coagulating protein. © 2010 EAPR.

Design, Construction, and Testing of an Automated NIR In-line Analysis System for Potatoes. Part II. Development and Testing of the Automated Semi-industrial System with In-line NIR for the Characterization of Potatoes

An automated semi-industrial system with in-line near-infrared reflectance (NIR) for the characterization of the chemical composition of potatoes was designed and constructed, and its performance was tested. The system consisted of the following subsystems: sample crate manipulator, weighing unit for the gross sample weight, potato washing machines with a washing water recycling system, belt for visual inspection of the potatoes, unit for measuring the underwater weight (UWW), industrial rotary saw blade rasp for pulping the potatoes equipped with a sulfite dosage system for inhibiting enzymatic browning of the pulped potatoes, and industrial NIR system for the measurement of the potato composition. The whole system was controlled and operated by a programmable logic controller and process personal computer system. The system was able to process 12 potato samples per hour. Measurements were done to establish the sample carry-over in the system. The carry-over was proven to be well below the maximum acceptable level of 2%. The UWW values established with the automatic system corresponded very well with the UWW data obtained by manual weighting. The day-to-day reproducibility of the UWW system was tested with golf balls. These balls have about the same specific gravity and size as potatoes. The day-to-day reproducibility coefficient of variation of the UWW unit was 0. 4%. As a principle of proof, two tentative partial least squares calibration models, one for the starch concentration and one for the coagulating protein concentration in the potato samples, were calculated, applying leaving one out cross-validation. Both models were very promising. The by NIR-predicted starch concentrations showed to be at least as good or even better than the by UWW-obtained starch concentrations. The average difference between the by NIR-predicted and the chemically measured starch concentration was 0. 0 ± 0. 3% (w/w). For the coagulating protein concentration, the average difference between the by NIR-predicted and the chemically measured concentration was 0. 00 ± 0. 06% (w/w). In future years, potatoes of a wider range of varieties, growing locations, and growing seasons have to be added to the present tentative model, in order to get a robust NIR model. © 2010 EAPR.

Predicting Protein Composition, Biochemical Properties, and Dough‐Handling Properties of Hard Red Winter Wheat Flour by Near‐Infrared Reflectance

ABSTRACTBreadmaking quality in wheat is one of several considerations that plant breeders face when developing new cultivars. In routine breeding programs, quality is assessed by small‐scale dough‐handling and bake tests, and to some extent, by biochemical analysis of gluten proteins. An alternative, not yet fully examined, method for wheat flour quality assessment is near‐infrared reflectance (NIR) spectrophotometry. The present study was performed on 30 genotypes of hard red winter wheat grown during two crop years at eight to nine locations in the Great Plains area of the United States. Biochemical testing consisted of measuring protein fractions from size‐exclusion HPLC (M r > 100k, M r 25–100k, and M r < 25k designated as glutenin, gliadins, and albumin and globulins, respectively), pentosan content, and SDS sedimentation volume. Dough‐handling properties were measured on a mixograph and recorded as the time to peak dough development, the peak resistance, the width of the mixing curve, and the width of the curve at 2 min past peak. Partial least squares analyses on diffuse NIR spectra (1,100–2,498 nm) were developed for each constituent or property. When applied to a separate validation set, NIR models for glutenin content, gliadin content, SDS sedimentation volume, and mixograph peak resistance demonstrated reference vs. predicted correlations ranging from r = 0.87 to r = 0.94. Such models were considered sufficiently accurate for screening purposes in breeding programs. NIR spectra were responsive to each constituent or property at a level higher than expected from a correlation between the constituent or property and protein content (recognizing that protein content is modeled by NIR with high accuracy).

Calibration of near infrared reflectance spectroscopy to estimate nitrogen concentration in potato tissues

Calibrations are provided to relate near infrared reflectance (NIR) data to total N concentration in potato leaves, stems, and tubers, estimated by Dumas combustion. Separate calibrations are necessary for each tissue-type. the NIR models simulate Dumas [N] estimates accurately, quickly and cheaply and have been shown to be stable. The methods of deriving the calibrations, correcting them and then providing independent tests are described.