Rapid prediction of solid wood lignin content using transmittance near-infrared spectroscopy

A rapid transmittance near-infrared (NIR) spectroscopic method has been developed to characterize the lignin content of solid wood. Using simple, multiple regression, and partial least-squares statistical analysis the lignin contents of wood wafers, taken from increment cores, and synthetic wood, prepared by blending milled wood lignin and holocellulose, were compared and quantified. Strong correlations were obtained between the predicted NIR results and those obtained from traditional chemical methods. In addition to the experimental protocol and method development, NIR results from wood samples with different particle sizes and various lignin contents are discussed.     

Near-infrared spectroscopy for measurement of total dietary fiber in homogenized meals

Near-infrared (NIR) reflectance spectroscopy was investigated as a method for prediction of total dietary fiber (TDF) in mixed meals. Meals were prepared for spectral analysis by homogenization only (HO), homogenization and drying (HD), and homogenization, drying, and defatting (HDF). The NIR spectra (400-2498 nm) were obtained with a dispersive NIR spectrometer. Total dietary fiber was determined in HDF samples by an enzymatic-gravimetric assay (AOAC 991.43), and values were calculated for HD and HO samples. Using multivariate analysis software and optimum processing, partial least squares models (n = 114) were developed to relate NIR spectra to the corresponding TDF values. The HO, HD, and HDF models predicted TDF in independent validation samples (n = 37) with a standard error of performance of 0.93% (range 0.7-8.4%), 1.90% (range 2.2-18.9%), and 1.45% (range 2.8-23.3%) and r(2) values of 0.89, 0.92, and 0.97, respectively. Compared with traditional analysis of TDF in mixed meals, which takes 4 days, NIR spectroscopy provides a faster method for screening samples for TDF. The accuracy of prediction was greatest for the HDF model followed by the HD model.     

Determining Vitreous Subclasses of Hard Red Spring Wheat Using Visible/Near‐Infrared Spectroscopy

The percentage of dark hard vitreous (DHV) kernels in hard red spring wheat is an important grading factor that is associated with protein content, kernel hardness, milling properties, and baking quality. The current visual method of determining DHV and non‐DHV (NDHV) wheat kernels is time‐consuming, tedious, and subject to large errors. The objective of this research was to classify DHV and NDHV wheat kernels, including kernels that were checked, cracked, sprouted, or bleached using visible/near‐infrared (Vis/NIR) spectroscopy. Spectra from single DHV and NDHV kernels were collected using a diode‐array NIR spectrometer. The dorsal and crease sides of the kernels were viewed. Three wavelength regions, 500–750 nm, 750–1,700 nm, and 500–1700 nm were compared. Spectra were analyzed by using partial least squares (PLS) regression. Results suggest that the major contributors to classifying DHV and NDHV kernels are light scattering, protein content, kernel hardness, starch content, and kernel color effects on the absorption spectrum. Bleached kernels were the most difficult to classify because of high lightness values. The sample set with bleached kernels yielded lower classification accuracies of 91.1–97.1% compared with 97.5–100% for the sample set without bleached kernels. More than 75% of misclassified kernels were bleached. For sample sets without bleached kernels, the classification models that included the dorsal side gave the highest classification accuracies (99.6–100%) for the testing sample set. Wavelengths in both the Vis/NIR regions or the NIR region alone yielded better classification accuracies than those in the visible region only.     

Individual detection of genetically modified maize varieties in non-identity-preserved maize samples

In many countries, the labeling of grains and feed- and foodstuffs is mandatory if the genetically modified organism (GMO) content exceeds a certain level of approved GM varieties. The GMO content in a maize sample containing the combined-trait (stacked) GM maize as determined by the currently available methodology is likely to be overestimated. However, there has been little information in the literature on the mixing level and varieties of stacked GM maize in real sample grains. For the first time, the GMO content of non-identity-preserved (non-IP) maize samples imported from the United States has been successfully determined by using a previously developed individual kernel detection system coupled to a multiplex qualitative PCR method followed by multichannel capillary gel electrophoresis system analysis. To clarify the GMO content in the maize samples imported from the United States, determine how many stacked GM traits are contained therein, and which GM trait varieties frequently appeared in 2005, the GMO content (percent) on a kernel basis and the varieties of the GM kernels in the non-IP maize samples imported from the United States were investigated using the individual kernel analysis system. The average (+/-standard deviation) of the GMO contents on a kernel basis in five non-IP sample lots was determined to be 51.0+/-21.6%, the percentage of a single GM trait grains was 39%, and the percentage of the stacked GM trait grains was 12%. The MON810 grains and NK603 grains were the most frequent varieties in the single GM traits. The most frequent stacked GM traits were the MON810xNK603 grains. In addition, the present study would provide the answer and impact for the quantification of GM maize content in the GM maize kernels on labeling regulation.     

Protein Content of Bulk Wheat from Near‐Infrared Reflectance of Individual Kernels

Protein content of wheat by near‐infrared (NIR) reflectance of bulk samples is routinely practiced. New instrumentation that permits automated NIR analysis of individual kernels is now available, with the potential for rapid NIR‐based determinations of color, disease, and protein content, all on a single kernel (sk) basis. In the event that the protein content of the bulk sample is needed rather than that of the individual kernels, the present study examines the feasibility of estimating bulk sample protein from sk spectral readings. On the basis of 318 wheat samples of 10 kernels per sample, encompassing five U.S. wheat classes, the study demonstrates that with as few as 300 kernels bulk sample protein content may be estimated by sk NIR reflectance spectra at an accuracy equivalent to conventional bulk kernel NIR instrumentation.     

Assessment of Heat-Damaged Wheat Kernels Using Near-Infrared Spectroscopy

Heat damage is a serious problem frequently associated with wet harvests because of improper storage of damp grain or artificial drying of moist grain at high temperatures. Heat damage causes protein denaturation and reduces processing quality. The current visual method for assessing heat damage is subjective and based on color change. Denatured protein related to heat damage does not always cause a color change in kernels. The objective of this research was to evaluate the use of nearinfrared (NIR) reflectance spectroscopy to identify heat-damaged wheat kernels. A diode-array NIR spectrometer, which measured reflectance spectra (log (1/R)) from 400 to 1,700 nm, was used to differentiate single kernels of heat-damaged and undamaged wheats. Results showed that light scattering was the major contributor to the spectral characteristics of heat-damaged kernels. For partial least squares (PLS) models, the NIR wavelength region of 750–1,700 nm provided the highest classification accuracy (100%) for both cross-validation of the calibration sample set and prediction of the test sample set. The visible wavelength region (400–750 nm) gave the lowest classification accuracy. For two-wavelength models, the average of correct classification for the classification sample set was >97%. The average of correct classification for the test sample set was generally >96% using two-wavelength models. Although the classification accuracies of two-wavelength models were lower than those of the PLS models, they may meet the requirements for industry and grain inspection applications.     

Determination of alcohol and extract concentration in beer samples using a combined method of near-infrared (NIR) spectroscopy and refractometry

A new approach of combination of near-infrared (NIR) spectroscopy and refractometry was developed in this work to determine the concentration of alcohol and real extract in various beer samples. A partial least-squares (PLS) regression, as multivariate calibration method, was used to evaluate the correlation between the data of spectroscopy/refractometry and alcohol/extract concentration. This multivariate combination of spectroscopy and refractometry enhanced the precision in the determination of alcohol, compared to single spectroscopy measurements, due to the effect of high extract concentration on the spectral data, especially of nonalcoholic beer samples. For NIR calibration, two mathematical pretreatments (first-order derivation and linear baseline correction) were applied to eliminate light scattering effects. A sample grouping of the refractometry data was also applied to increase the accuracy of the determined concentration. The root mean squared errors of validation (RMSEV) of the validation process concerning alcohol and extract concentration were 0.23 Mas% (method A), 0.12 Mas% (method B), and 0.19 Mas% (method C) and 0.11 Mas% (method A), 0.11 Mas% (method B), and 0.11 Mas% (method C), respectively.     

Rapid prediction of gross energy and utilizable energy in cereal food products using near-infrared reflectance spectroscopy

Near-infrared (NIR) spectroscopy has been used in foods for the rapid assessment of several macronutrients; however, little is known about its potential for the evaluation of the utilizable energy of foods. Using NIR reflectance spectra (1104-2494 nm) of ground cereal products (n = 127) and values for energy measured by bomb calorimetry, chemometric models were developed for the prediction of gross energy and available energy of diverse cereal food products. Standard errors of cross-validation for NIR prediction of gross energy (range = 4.05-5.49 kcal/g), energy of samples after adjustment for unutilized protein (range = 3.99-5.38 kcal/g), and energy of samples after adjustment for unutilized protein and insoluble dietary fiber (range = 2.42-5.35 kcal/g) were 0.053, 0.053, and 0.088 kcal/g, respectively, with multiple coefficients of determination of 0.96. Use of the models on independent validation samples (n = 58) gave energy values within the accuracy required for U.S. nutrition labeling legislation. NIR spectroscopy, thus, provides a rapid and accurate method for predicting the energy of diverse cereal foods.     

Rapid determination of the main organic acid composition of raw Japanese apricot fruit juices using near-infrared spectroscopy

The potential of near-infrared (NIR) spectroscopy to rapidly determine citric and malic acid contents of raw Japanese apricot (Japanese "ume", also known as the Japanese plum) fruit juice was investigated. In total, 314 raw juice samples with different organic acid compositions were collected over a long growth period, and spectra (1100-1850 nm) of these samples were acquired using an NIR spectrophotometer with a 1-mm path length. Calibrations were performed using a partial least-squares regression method based on a calibration sample set (211 samples), while validations were performed based on a validation sample set (103 samples). The results revealed good agreement between NIR spectroscopy and capillary electrophoresis, including the correlation coefficient (r2), standard error of prediction (SEP), and bias; no statistically (p = 0.05) significant differences were found for these parameters. Moreover, standard deviation ratios of reference data in the validation sample set to the SEP were higher than 3, indicating that NIR spectroscopy may represent an acceptable method for quantitative evaluation of citric and malic acids in raw Japanese apricot fruit juice.     

基于近红外光谱的灌浆期玉米籽粒水分小样本定量分析

玉米灌浆期含水率测定是考种育种的重要指标。为了节约样本且快速准确测定灌浆期玉米水分,该文应用近红外光谱技术,提出了基于小样本条件下的自举算法(Bootstrap)与基于x-y距离结合的样本划分方法(SPXY,sample set partitioning based on joint x-y distances)相结合的样本优化方法的偏最小二乘(PLS,partial least square)水分定量分析模型Bootstrap-SPXY-PLS模型。试验结果表明,当Bootstrap重抽样本次数等于500,样本数量大于等于10时,模型的性能稳定,并且随着样本数量增加,重抽样本次数相对减少;样本数量为10和50时,全谱Bootstrap-SPXY-PLS模型的预测均方根误差(RMSEP,root-mean-square error of prediction)均值分别为0.38%和0.40%,预测相关系数(correlation coefficients of prediction)分别为0.975 1和0.968 5,决定系数R~2分别为0.999 9和0.993 6;基于竞争性自适应重加权采样算法(CARS,competitive adaptive reweighed sampling)波长变量筛选后的CARS-Bootstrap-SPXY-PLS模型的预测均方根误差RMSEP均值分别为0.36%和0.35%,预测相关系数分别为0.973 6和0.975 0,模型决定系数R~2分别为0.924 5和0.918 0。因此,全谱Bootstrap-SPXY-PLS模型和CARS-Bootstrap-SPXY-PLS模型均具有稳定的预测能力,为玉米育种时灌浆期种子水分测定提供了一种稳定、高效的方法。     

Practicable group testing method to evaluate weight/weight GMO content in maize grains

Because of the increasing use of maize hybrids with genetically modified (GM) stacked events, the established and commonly used bulk sample methods for PCR quantification of GM maize in non-GM maize are prone to overestimate the GM organism (GMO) content, compared to the actual weight/weight percentage of GM maize in the grain sample. As an alternative method, we designed and assessed a group testing strategy in which the GMO content is statistically evaluated based on qualitative analyses of multiple small pools, consisting of 20 maize kernels each. This approach enables the GMO content evaluation on a weight/weight basis, irrespective of the presence of stacked-event kernels. To enhance the method's user-friendliness in routine application, we devised an easy-to-use PCR-based qualitative analytical method comprising a sample preparation step in which 20 maize kernels are ground in a lysis buffer and a subsequent PCR assay in which the lysate is directly used as a DNA template. This method was validated in a multilaboratory collaborative trial.     

Fast screening method for volatile compounds of oak wood used for aging wines by headspace SPME-GC-MS (SIM)

A headspace solid-phase microextraction (HS-SPME) method is proposed for analyzing the main volatile components from a sensory standpoint (furfural, oak lactones, eugenol, vanillin, and syringaldehyde) present in nontoasted and toasted oak wood of different origins. To maximize the yield of compounds extracted from wood chips and to obtain a good precision of the method, the most important variables affecting HS-SPME have been studied. The best results were obtained when the sample was heated at 70 degrees C and the headspace extracted for 40 min with a DVB/CAR/PDMS fiber, which gave the overall best recovery. The values for the repeatability ranged from 6.4 to 7.8%, and those for the reproducibility from 5.4 to 8.7%. The precision of the results obtained makes the proposed technique appropriate for its use in characterizing oak wood samples of different origins and in the selection of the most suitable oak wood to age wines and spirits, on the basis of the chemical composition of the wood samples.     

Near-infrared imaging spectroscopy based on sugar absorption band for melons.

A method for visualizing the sugar content in the flesh of melons was developed. This method was based on the sugar absorption band in the near-infrared (NIR) region to avoid bias caused by the color information of a sample. NIR spectroscopic analysis revealed that each of the two second-derivative absorbances at 874 and 902 nm had a high correlation with the sugar content of melons. A high-resolution cooled charged couple device camera with band-pass filters, which included the above two wavelengths, was used to capture the spectral absorption image of a half-cut melon. A color distribution map of the sugar content on the surface of the melon was constructed by applying the NIR spectroscopy theory to each pixel of the acquired images.     

Near-infrared spectroscopic determination of degradation in vegetable oils used to fry various foods

Near-infrared (NIR) spectroscopic methods for measuring degradation products, including total polar materials (TPMs) and free fatty acids (FFAs), in soy-based frying oil used for frying various foods have been successfully developed. Calibration models were developed using forward stepwise multiple linear regression (FSMLR) and partial least-squares (PLS) regression techniques and then tested with an independent set of validation samples. The results show that the quality of oil used for frying different foods can be measured with a single model. First-derivative treatments improved results for TPM measurement. In addition, PLS models gave better prediction results than FSMLR models. For PLS models, the best correlations (r) between the NIR-predicted data and the chemical method data for TPMs and FFAs in oils were 0.995 and 0.981, respectively. For FSMLR models, the best r values for TPMs and FFAs in oils were 0.993 and 0.963, respectively.     

Measurement of Wheat Hardness by Seed Scarifier and Barley Pearler and Comparison with Single‐Kernel Characterization System

A new procedure based on a seed scarifier (SS) for measuring wheat hardness was described and investigated along with methods using a barley pearler (BP) and the single kernel characterization system (SKCS). Hardness measured by SS and BP is expressed as a percentage of kernel weight remaining after abrading and defined as abrasion resistance index (ARI). For a given sample weight, increased abrading time decreased ARI but improved the ability to differentiate variation among samples. The effect of sample moisture was also statistically significant. For improved performance of SS and BP, based on distinct patterns of relationships between surface removal rates and surface removal levels among soft and hard wheats, a combination of parameters that produces ARI values in the range of 80–20, and a run for a set of reference material are recommended. Differences in measured hardness values from SS, BP, and SKCS existed within a wheat group, but they were very much method‐dependent. Nevertheless, all methods were able to differentiate variations between soft and hard wheat groups. Because of low cost, durability, simplicity, repeatability, and aforementioned ability, SS and BP, although limited by lack of standardization and calibration procedures, can still be useful for grain hardness measurement, particularly when and where instruments for contemporary popular methods such as SKCS and near‐infrared reflectance (NIR) spectroscopy are not readily available.     

Using Interlaboratory Proficiency Data to Guide NIR/MIR Calibrations

There is growing interest in the use of near-range and/or midrange infrared (IR) diffuse reflectance spectroscopy (NIR and MIR) as nondestructive alternatives to chemical testing of soils. This trend is supported by research on how best to correlate IR spectral data with results obtained by conventional laboratory measurements. While for soils there is growing interest in developing local and national calibrations using “legacy” data, the proven analytical performance of provider laboratories now and earlier, the moisture status of reported results, and the method of soil preparation warrant greater attention. Examples for soil carbon (C) and total soil nitrogen (N) from Australasian interlaboratory proficiency testing across multiple years from 1993 are provided to demonstrate the magnitude of past and present measurement uncertainties, including the effects of method and different concentrations. The evidence is sufficient to require those commissioned to develop NIR and MIR calibrations to subject their prototype calibrations to external peer review by participating in credible, independent interlaboratory proficiency testing programs for ≥12 months, including checks on soil moisture status and possible effects of sample preparation. To rate as credible for most uses, the prototype results should be within the interquartile range for each sample and ideally there should be no outliers and few stragglers. Across the period of assessment (1993–2008), users of Walkley and Black organic C and Kjeldahl digestion for total soil N (Kjeldahl method does not measure total N, but most of the organic N plus an undetermined proportion of nitrate and nitrate present in the sample; quantitative inclusion of both requires a modification of the Kjeldahl procedure) declined as use of furnace technologies for soil C and N increased linearly. There is a strong case to commission two or three well-performing and experienced laboratories to reanalyze samples in “legacy” soil collections prior to finalizing predictive relationships with NIR/MIR spectra for the same samples.     

(Methyl)Mercury,Arsenic, and Lead Contamination of the World’s Largest Wastewater Irrigation System: the Mezquital Valley (Hidalgo State—Mexico)

We investigate the potential of near-infrared (NIR) spectroscopy to predict some heavy metals content (Zn, Cu, Pb, Cr and Ni) in several soil types in Stara Zagora Region, South Bulgaria, as affected by the size of calibration set using partial least squares (PLS) regression models. A total of 124 soil samples from the 0–20 and 20–40 cm layers were collected from fields with different cropping systems. Total Zn, Cu, Pb, Cr and Ni concentrations were determined by Atomic Absorption Spectrometry. Spectra of air dried soil samples were obtained using an FT-NIR Spectrometer (spectral range 700–2,500 nm). PLS calibration models were developed with full-cross-validation using calibration sets of 90 %, 80 %, 70 % and 60 % of the 124 samples. These models were validated with the same prediction set of 12 samples. The validation of the NIR models showed Cu to be best predicted with NIR spectroscopy. Less accurate prediction was observed for Zn, Pb and Ni, which was classified as possible to distinguish between high and low concentrations and as approximate quantitative. The worst model performance in cross-validation and prediction was for Cr. Results also showed that values of root mean square error in cross-validation (RMSE_cv) increased with decreasing number of samples in calibration sets, which was particularly clear for Cu, Pb, Ni and Cr content. A similar tendency was observed in the prediction sets, where RMSEP values increased with a decrease in the number of samples, particularly for Pb, Ni and Cr content. This tendency was not clear for Zn, while even an increase in RMSEP for Cu with the sample size was observed. It can be concluded that NIR spectroscopy can be used to measure heavy metals in a sample set with different soil type, when sufficient number of soil samples (depending on variability) is used in the calibration set.     

High‐Throughput Near‐Infrared Reflectance Spectroscopy for Predicting Quantitative and Qualitative Composition Phenotypes of Individual Maize Kernels

Near‐infrared reflectance (NIR) spectroscopy can be used for fast and reliable prediction of organic compounds in complex biological samples. We used a recently developed NIR spectroscopy instrument to predict starch, protein, oil, and weight of individual maize (Zea mays) seeds. The starch, protein, and oil calibrations have reliability equal or better to bulk grain NIR analyzers. We also show that the instrument can differentiate quantitative and qualitative seed composition mutants from normal siblings without a specific calibration for the constituent affected. The analyzer does not require a specific kernel orientation to predict composition or to differentiate mutants. The instrument collects a seed weight and a spectrum in 4–6 sec and can collect NIR data alone at a 20‐fold faster rate. The spectra are acquired while the kernel falls through a glass tube illuminated with broad spectrum light. These results show significant improvements over prior single‐kernel NIR systems, making this instrument a practical tool to collect quantitative seed phenotypes at high throughput. This technology has multiple applications for studying the genetic and physiological influences on seed traits.     

Differentiating Vitreous and Nonvitreous Durum Wheat Kernels by Using Near‐Infrared Spectroscopy

The vitreousnss of durum wheat is used by the wheat industry as an indicator of milling and cooking quality. The current visual method of determining vitreousness is subjective, and classification results between inspectors and countries vary widely. Thus, the use of near‐infrared (NIR) spectroscopy to objectively classify vitreous and nonvitreous single kernels was investigated. Results showed that classification of obviously vitreous or nonvitreous kernels by the NIR procedure agreed almost perfectly with inspector classifications. However, when difficult‐to‐classify vitreous and nonvitreous kernels were included in the analysis, the NIR procedure agreed with inspectors on only 75% of kernels. While the classification of difficult kernels by NIR spectroscopy did not match well with inspector classifications, this NIR procedure quantifies vitreousness and thus may provide an objective classification means that could reduce inspector‐to‐inspector variability. Classifications appear to be due, at least in part, to scattering effects and to starch and protein differences between vitreous and nonvitreous kernels.     

Comparison of Near Infrared Reflectance Spectroscopy with Combustion and Chemical Methods for Soil Carbon Measurements in Agricultural Soils

As interest in soil organic carbon (SOC) dynamics increases, so do needs for rapid, accurate, and inexpensive methods for quantifying SOC. Objectives were to i) evaluate near infrared reflectance (NIR) spectroscopy potential to determine SOC and soil organic matter (SOM) in soils from across Tennessee, USA; and ii) evaluate potential upper limits of SOC from forest, pasture, no-tillage, and conventional tilled sites. Samples were analyzed via dry-combustion (SOC), Walkley–Black chemical SOM, and NIR. In addition, the sample particle size was classified to give five surface roughness levels to determine effects of particle size on NIR. Partial least squares regression was used to develop a model for predicting SOC as measured by NIR by comparing against SOM and SOC. Both NIR and SOM correlated well (R² > 0.9) with SOC (combustion). NIR is therefore considered a sufficiently accurate method for quantifying SOC in soils of Tennessee, with pasture and forested systems having the greatest accumulations.Abbreviations SOC, soil organic carbon; NIR, Near Infrared Reflectance Spectroscopy; MTREC, Middle Tennessee Research and Education Center; RECM, Research and Education Center at Milan; PREC, Plateau Research and Education Center; PLS, Partial least squares.