Near Infrared Reflectance Spectroscopy Measurements Research Articles

Effects of ensiling time on corn silage starch ruminal degradability evaluated in situ or in vitro

Accurate measurements of concentration and ruminal degradability of corn silage starch is necessary for formulation of diets that meet the energy requirements of dairy cows. Five corn silage hybrids ensiled for 0 (unfermented), 30, 60, 120, and 150 d were used to determine the effects of ensiling time on starch degradability of corn silage. In addition, the effects of grind size of silage samples on 7-h in vitro starch degradability and the relationship between in vitro, in situ and near-infrared reflectance spectroscopy (NIRS) starch degradability were studied. In situ disappearance of corn silage starch increased from 0 to 150 d of ensiling, primarily as a result of an increase in the washout or rapidly degraded fraction of starch, particularly during the first 60 d of ensiling. When analyzed in vitro and by NIRS, ensiling time increased corn silage starch degradability either linearly or to a greater extent during the first 2 mo of ensiling. Differences in in situ starch disappearance among corn silage hybrids were apparent during the first 2 mo of ensiling but were attenuated as silages aged. No differences among hybrids were detected using a 7-h in vitro starch digestibility approach. Results from the in vitro subexperiment indicate that 7-h in vitro starch degradability was increased by reducing grind size of corn silage from 4 to 1 mm, regardless of ensiling duration. Fine grinding corn silages samples (i.e., 1-mm sieve) allowed distinguishing low- from medium- and high-starch degradability rated hybrids. Correlations among in situ, in vitro and NIRS measurements for starch degradability were medium to high (r ≥0.57); however, agreement among methods was low (concordance correlation coefficient ≤0.15). In conclusion, ensiling time linearly increased degradation rate of corn silage resulting in greater in situ starch disappearance after 150 d of ensiling. Reductions in grind size from 4 to 1 mm resulted in greater in vitro starch degradability, regardless of ensiling duration. Strong correlation but low agreement between starch degradability methods suggest that absolute estimations of corn silage starch degradability will vary, but all methods can be used to assess the effect of ensiling time on starch degradability.

Towards a global arctic-alpine model for Near-infrared reflectance spectroscopy (NIRS) predictions of foliar nitrogen, phosphorus and carbon content

Near-infrared spectroscopy (NIRS) is a high-throughput technology with potential to infer nitrogen (N), phosphorus (P) and carbon (C) content of all vascular plants based on empirical calibrations with chemical analysis, but is currently limited to the sample populations upon which it is based. Here we provide a first step towards a global arctic-alpine NIRS model of foliar N, P and C content. We found calibration models to perform well (R2validation = 0.94 and RMSEP = 0.20% for N, R2validation = 0.76 and RMSEP = 0.05% for P and R2validation = 0.82 and RMSEP = 1.16% for C), integrating 97 species, nine functional groups, three levels of phenology, a range of habitats and two biogeographic regions (the Alps and Fennoscandia). Furthermore, when applied for predicting foliar N, P and C content in samples from a new biogeographic region (Svalbard), our arctic-alpine NIRS model performed well. The precision of the resulting NIRS method meet international requirements, indicating one NIRS measurement scan of a foliar sample will predict its N, P and C content with precision according to standard method performance. The modelling scripts for the prediction of foliar N, P and C content using NIRS along with the calibration models upon which the predictions are based are provided. The modelling scripts can be applied in other labs, and can easily be expanded with data from new biogeographic regions of interest, building the global arctic-alpine model.

Determination of Lignin Monomer Contents in Rice Straw Using Visible and Near-infrared Reflectance Spectroscopy

Genetic modification of plant lignin composition is an important strategy for improving biomass enzymatic digestibility without sacrificing the normal growth of the plant. However, the absence of fast and convenient methods for rapid determination of lignin composition has impeded corresponding research. Near-infrared reflectance spectroscopy (NIRS) analysis has potential as a solution for this dilemma, while the NIRS measurement for expediently assaying lignin composition in rice straws is still lacking. In this study, visible and near-infrared reflectance spectroscopy (VIS/NIRS) and modified partial least squares (MPLS) method were combined to develop calibration models for predicting the lignin monomer contents in a diverse rice population. Four optimal equations for predicting the content of p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) lignin units and their total amount (H + S + G) were generated with acceptable determination coefficients for calibration (0.85 to 0.93), cross-validation (0.75 to 0.88), and external validation (0.82 to 0.88), and the ratio performance deviation (RPD, 2 to 3.01). This study was the first to demonstrate that VIS/NIRS could give a sufficiently accurate prediction of lignin monomer contents in rice and could be applied for rapid assessments of large-scale rice straw samples.

Use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid compositional analysis of di-, tri-, and tetraploid eastern oysters (Crassostrea virginica)

The oyster aquaculture industry in the USA, especially the Chesapeake Bay, has shown rapid growth over the last decade. Breeding has played a large role in this growth in the Chesapeake Bay, with emphasis on the selection for disease resistance, growth and the development of triploid Crassostrea virginica. With major gains realized in both of these traits, we are attempting to add more, such as, compositional traits. A specific interest for the Aquaculture Genetics and Breeding Technology Center is to derive compositional data for use with our selective breeding program. Near Infrared Reflectance Spectroscopy (NIRS) estimates of moisture and glycogen may lead to the definition of these new traits for selection to accompany those traditional measures of survival, meat yield, and growth rate. This study describes the development of NIRS models to estimate moisture and glycogen in the eastern oyster C. virginica.NIR spectra of 138 homogenized oyster meats were modeled against physicochemically measured moisture and glycogen data. The robustness of resulting models was assessed by an independent validation that showed a high coefficient of determination for validation (R2val=0.95) and high residual predictive deviation (RPD≥4.1) for both parameters, making them suitable for quantification. In a second step, these models have also been validated to obtain compositional data on different levels of ploidy in C. virginica (diploid, triploid, and tetraploid). These parameters will be applied to our family based selection program where heritabilities will be calculated. Statement of relevanceFor the first time we have developed NIRS models to determine compositional analysis in C. virginica. NIRS estimates of moisture and glycogen may lead to the definition of new traits for selection to accompany other traditional measures (survival, meat yield, and growth rate). NIRS measurements will be applied to our family based selection program where heritabilities will be calculated.

Fast and Inexpensive Detection of Total and Extractable Element Concentrations in Aquatic Sediments Using Near-Infrared Reflectance Spectroscopy (NIRS)

Adequate biogeochemical characterization and monitoring of aquatic ecosystems, both for scientific purposes and for water management, pose high demands on spatial and temporal replication of chemical analyses. Near-infrared reflectance spectroscopy (NIRS) may offer a rapid, low-cost and reproducible alternative to standard analytical sample processing (digestion or extraction) and measuring techniques used for the chemical characterization of aquatic sediments. We analyzed a total of 191 sediment samples for total and NaCl-extractable concentrations of Al, Ca, Fe, K, Mg, Mn, N, Na, P, S, Si, and Zn as well as oxalate- extractable concentrations of Al, Fe, Mn and P. Based on the NIR spectral data and the reference values, calibration models for the prediction of element concentrations in unknown samples were developed and tested with an external validation procedure. Except Mn, all prediction models of total element concentrations were found to be acceptable to excellent (ratio of performance deviation: RPD 1.8–3.1). For extractable element fractions, viable model precision could be achieved for NaCl-extractable Ca, K, Mg, NH4 +-N, S and Si (RPD 1.7–2.2) and oxalate-extractable Al, Fe and P (RPD 1.9–2.3). For those elements that showed maximum total values below 3 g kg−1 prediction models were found to become increasingly critical (RPD <2.0). Low concentrations also limited the performance of NIRS calibrations for extracted elements, with critical concentration thresholds <0.1 g kg−1 and 3.3 g kg−1 for NaCl and oxalate extractions, respectively. Thus, reliable NIRS measurements of trace metals are restricted to sediments with high metal content. Nevertheless, we demonstrated the suitability of NIRS measurements to determine a large array of chemical properties of aquatic sediments. The results indicate great potential of this fast technique as an analytical tool to better understand the large spatial and temporal variation of sediment characteristics in an economically viable way.

Mapping of Total Carbon and Clay Contents in Glacial Till Soil Using On-the-Go Near-Infrared Reflectance Spectroscopy and Partial Least Squares Regression

Characterizing spatial variability of soil attributes, using traditional soil sampling and laboratory analysis, is cost prohibitive. The potential benefit of managing soils on a site-specific basis is well established. High variations in glacial till soil render detailed soil mapping difficult with limited number of soil samples. To overcome this problem, this paper demonstrates the feasibility of soil carbon and clay mapping using the newly developed on-the-go near-infrared reflectance spectroscopy (NIRS). Compared with the geostatistics method, the partial least squares regression (PLSR), with NIRS measurements, could yield a more detailed map for both soil carbon and clay. Further, by using independent validation dataset, the accuracy of predicting could be improved significantly for soil clay content and only slightly for soil carbon content. Owing to the complexity of field conditions, more work on data processing and calibration modeling might be necessary for using on-the-go NIRS measurements.

Estimation of Fatty Acid Content in Intact Seeds of Oilseed Rape (Brassica napus L.) Lines Using Near-Infrared Spectroscopy

Based on NIRS (near infrared reflectance spectroscopy) measurements carried out in a collection of 262 samples of winter oilseed rape with a different content of fatty acids (FA) in oil, calibration equations for the laboratory instrument Foss-NIRSystem 6500 were developed. Calibration was focused on the possibility of screening seed samples of different composition of oleic acid (C18:1), linoleic acid (C18:2) and linolenic acid (C18:3) using NIRS analysis. The reference method was gas chromatography (GC). The content of FA in segregating F&lt;sub&gt;2&lt;/sub&gt; generations after crossing between lines with different contents of FA and lines with standard content of FA in oil ranged from 32.3 to 82.0% for C18:1, 10.2&amp;ndash;26.8% for C18:2 and 3.3&amp;ndash;11.8% for C18:3. The verification of a validation equation in 50 randomly selected samples of F&lt;sub&gt;2&lt;/sub&gt; generation in the year 2006 proved high correlation coefficients (r) between NIRS analysis and GC values, r = 0.86 for C18:1, r = 0.82 for C18:2 and r = 0.85 for C18:3. Non-destructive NIRS analysis enables rapid and reliable selection of materials with different composition of FA in the seed of oilseed rape (lines with desirable high content of C18:1 and low content of C18:3). &amp;nbsp;

Determination of Kavalactones in Dried Kava (Piper methysticum) Powder Using Near-Infrared Reflectance Spectroscopy and Partial Least-Squares Regression

Kava (Piper methysticum Forst F.), or àwa in the Hawaiian language, has been used for thousands of years by the people of the South Pacific Islands, in particular Fiji, Vanuatu, Tonga, and Samoa, for social and ceremonial occasions. Kava has the unique ability to promote a state of relaxation without the loss of mental alertness. Kava recently became part of the herbal pharmacopoeia throughout the United States and Europe because of its anxiolytic properties. The active compounds are collectively called kavalactones (or kava pyrones). The need for a less time-consuming and costly method to determine the concentration of kavalactones in dried kava is urgent. The combination of near-infrared reflectance spectroscopy (NIRS) and partial least-squares (PLS) methods has been found to be a convenient, versatile, and rapid analytical tool for determination of kavalactones in dried kava powder. Calibration equations were developed based on the analyses of 110 samples with variable physical and chemical properties collected over time from Hawaii kava growers and validated by analyses of a set of 12 samples with unknown kavalactones concentration. All six major kavalactones and the total kavalactones were measured using NIRS with accuracy acceptable for commercial use. The NIRS measurements are reproducible and have a repeatability on a par with HPLC methods.

근적외선분광법을 이용한 옥수수 사일리지의 소화율 및 에너지 평가

본 시험의 목적은 옥수수 사일리지의 소화율 및 에너지가치를 신속하고 정확하게 평가하는 방법으로서 근적외선분광법(NIRS)의 이용성을 확대하고 동시에 더욱 정확한 검량식을 유도하기 위하여 수행되었다. 112점의 옥수수 사일리지 시료를 이용하여 근적외선분광기를 이용하여 스펙트럼을 수집하였다. 검량기법은 변형부분 최소자승회귀법(MPLS), 산란보정법은 SNV-D 또한 1,4,4,1 수처리 방법을 이용하여 검량식을 작성하였다. 옥수수 사일리지의 소화율 측정방법에 따른 근적외선분광법의 예측 능력은 IVDMD, IVTD 및 CDMD 함량에서 각각 <TEX>$SEP=1.57% (R^2v=0.70),\;SEP=1.13%(R^2v=0.73)$</TEX> 및 <TEX>$SECV=1.74%\;(R^2v=0.77)$</TEX>로 나타났으며 에너지 가치를 예측하기 위한 검량식 작성 및 검증 결과는 TDN, NEL 및 ME 함량에서 각각 SECV=0.69% <TEX>$(R^2v=0.85)$</TEX>, SECV=0.02% (R2v=0.88) 및 SECV=0.02% <TEX>$(R^2v=0.88)$</TEX>로 비교적 양호한 결과를 나타냈다. This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination<TEX>$(R^2)$</TEX> and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation<TEX>$(R^2v)$</TEX> on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

Comparison of Near‐Infrared Reflectance Spectroscopy and Texture Analyzer for Measuring Wheat Bread Changes in Storage

ABSTRACTBread staling affects bread texture properties and is one of the most common problems in bread storage. Bread firmness, as measured in compression mode by a texture analyzer (TA) has been commonly used to measure bread staling. This study investigated the potential of visible and near‐infrared reflectance spectroscopy (NIRS) to detect bread changes during storage by comparing NIRS results with those obtained by TA. Twenty‐five loaves of commercial wheat white pan bread from one batch were studied over five days. NIRS and TA measurements were made on the same slice at approximately the same time. The experiment was repeated five times using the same kind of commercial samples from five different batches. NIRS measurements of slices, loaf averages, and daily averages were compared with TA measurements. NIRS spectra had a high correlation to TA firmness. NIRS measurements correlated better with the actual storage time and had smaller standard deviations than the TA measurements. The batch differences had less effect on NIRS measurements than on the TA measurements. The results indicate that NIRS could follow bread changes during storage more accurately than the TA. NIRS is probably based on both physical and chemical changes during bread staling, unlike the TA method that only measures bread firmness, which is only one aspect of the staling phenomenon.

Rapid methods to determine potentially mineralizable nitrogen in broiler litter.

Although broiler (chicken, Gallus gallus domesticus) litter has long been used as a fertilizer, estimating the rate required to supply a desired amount of plant-available N is still hampered by the lack of rapid methods to estimate potentially mineralizable nitrogen (PMN). Previous research has suggested that near infrared reflectance spectroscopy (NIRS) and certain poultry litter characteristics, such as water-soluble organic nitrogen (WSON), may be useful for estimating PMN. The objectives of this study were to evaluate NIRS and WSON as tools to estimate PMN in broiler litter. Sixty sieved (2 mm) and freeze-dried broiler litter samples were mixed with Cowarts sandy loam soil (fine-loamy, kaolinitic, thermic Typic Kanhapludult) and incubated at 25 degrees C for 112 d. Cumulative net N mineralized with time was fitted to a single-pool exponential model to determine PMN for each broiler litter sample. The PMN values obtained were regressed against NIRS (780 to 2500 nm) and WSON measurements. We found strong relationships between measured- and NIRS-predicted PMN (R2 = 0.82), and between measured PMN and WSON (R2 = 0.87). These results demonstrate the feasibility of using either of these two methods to estimate PMN in broiler litter. Future work should further test both methods for their ability to estimate mineralizable N in whole, moist broiler litter under field conditions.

The use of near‐infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter-corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R2) and standard errors of cross-validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg−1 on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry

The use of near-infrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition of whole maize plants (Zea mays L) in breeding programmes at INIA La Estanzuela, Uruguay. Four hundred samples (n = 400) were scanned from 400 to 2500 nm in an NIRS 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Modified partial least squares (MPLS) regression was applied to scatter-corrected spectra (SNV and detrend). Calibration models for NIRS measurements gave multivariate correlation coefficients of determination (R2) and standard errors of cross-validation (SECV) of 0.72 (SECV 9.5), 0.96 (SECV 7.7), 0.98 (SECV 16.5), 0.96 (SECV 34.3), 0.98 (SECV 17.8) and 0.98 (SECV 6.1) for dry matter (DM), crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), in vitro organic matter digestibility (IVOMD) and ash in g kg−1 on a dry weight basis respectively. This paper shows the potential of NIRS to predict the chemical composition of whole maize plants as a routine method in breeding programmes and for farmer advice. © 2000 Society of Chemical Industry

Simultaneous Determination of Fecal Fat, Nitrogen and Water by Fourier Transform near Infrared Reflectance Spectroscopy through a Polyethylene/Polyaminde Film

The determination of fecal fat, nitrogen and water is important to get evidence for malassimilation and for estimating the efficacy of treatment with pancreatic enzymes. Standard methods for the determination of these parameters (van-de-Kamer method for fat determination, Kjeldahl method for nitrogen determination) are expensive, time-consuming and cumbersome for laboratory assistants. Near infrared (NIR) reflectance spectroscopy was evaluated as a potentially attractive alternative method, especially because the simultaneous measurement of fat, nitrogen and water content is possible. After homogenisation parts of stool samples were packed and thermowelded in a bag of polyethylene/polyamide (PE/PA) film to optimize the handling in the laboratory. Two optical systems were tested: 1. Fiber optic, In-Ga-As-detector, weavelength range 1000–2500 nm, area of measurement diameter 4–mm; 2. Integrating sphere, Ge-detector, wavelength range 1000–1800 nm, area of measurement diameter 10 mm. Forty stool samples were used for calibration, another 20 for validation from both healthy children and patients with cystic fibrosis in an age range from 5 to 18 years. The concentrations of fecal compounds were calculated using the chemometric Partial Least Square (PLS) method with the NIR reflectance spectroscopy measurement data. The calibration were carried out based on results of chemical analysis with standard methods. The regression equations of the external NIR reflectance spectroscopy validations were as follows:1. for the fiber optic system: fat determination y = 0.9737x + 5.7261r = 0.989, nitrogen determination y = 1.0092x+0.0731 r = 0.933, water determination y = 0.9699x+2.0703 r = 0.993;2. for the integrating sphere system: fat determination y = 1.0308x–1.6797 r = 0.998, nitrogen determination y = 0.9529x+0.5302 r = 0.959, water y = 1.0301x–1.8193 r = 0.993.The NIR reflectance spectroscopy method is a precise and alternative method for the determination of fecal fat, nitrogen and water. Moreover, handling is simple, time of analysis is short (4 minutes on average) and all calibrated constituents can be analyzed simultaneously. Therefore, we conclude that NIR reflectance spectroscopy is a reliable and useful method for analysis of fecal components in laboratory medicine.

Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants

Near infrared reflectance spectroscopy (NIRS) was compared with analytical methods for predicting chemical composition, digestibility and energy content of compound feeds for swine and ruminants. The study investigated numerous widely diversified feeds with known in vivo measurements (87 swine and 80 ruminant feeds). Accuracy of prediction of the energy values of swine and ruminant feeds using NIRS was slightly better than using empirical models from chemical composition or enzymatic methods. The two populations had to be modelled individually. Despite the need of a large number of samples for calibration, NIRS measurements are non-destructive, non-polluting and can be carried out during a short period of time. The most significant wavelengths selected by the step-up model for in vivo digestibility of swine and ruminant feeds lay in the range 1600–1700 nm and 2200 nm, respectively.