Near-infrared Reflectance Spectroscopy Calibration Research Articles

Near infrared calibration of chemical constituents of Cajanus cajan (pigeon pea) used as forage

Near infrared reflectance spectroscopy (NIRS) was used to predict quality parameters in Cajanus cajan used as forage for animals. Crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), crude lipid (CL) and organic matter (OM) were determined by chemical methods. The set of samples for analysis was selected using the software SELECT on the basis of the NIR spectra of samples from field plots and consisted of branches and leaves of Cajanus cajan, harvested in two contrasting locations in Zimbabwe. The samples were scanned and screened using an NIR Systems Model 5000 monochromator; 48 samples were used to calibrate and to cross-validate the equations derived. Equations for predicting chemical composition of the species under study were calculated using scores from partial least squares (PLS) as independent variables. Cross-validation procedures indicated good correlations between laboratory values and NIRS estimates. NIRS calibrations obtained from this study could be utilised in current and future programmes for evaluating the quality of Cajanus cajan forage for animal nutrition.

Prediction of the in vitro gas production dynamics of kikuyu grass by near-infrared reflectance spectroscopy using spectrally-structured sample populations

A study was carried out to test: (1) if the prediction of in vitro gas production of kikuyu grass ( Pennisetum clandestinum) samples by near-infrared reflectance spectroscopy (NIRS) could be improved by the use of a spectrally-structured sample population; and (2) if the parameters from exponential models used to describe the kinetics of gas production could be calibrated by NIRS. Forty-one kikuyu grass samples (calibration set) were chosen out of a total of 288 on the basis of their spectral characteristics as representative samples of the whole sample population. Measurements of cumulative in vitro gas production were recorded at 3, 6, 12, 24, 48, 72 and 96 h. Spectra were transformed with 1st, 2nd or 3rd derivative mathematical treatments. NIRS calibration equations were derived for in vitro gas production, with or without scatter correction for particle size using modified partial least squares. The equations were validated using a set of 48 samples previously chosen at random from the total sample population (validation set). Satisfactory calibrations and cross-validations were obtained for all the static measurements of gas production ( R 2 = 0.77−0.86 (S.E. 0.48−2.06) and 0.74−0.82 (S.E. 0.50−2.18), for the gas volumes from 3 to 96 h, respectively), and the use of a spectrally-structured population improved the calibration and cross-validation statistics of the NIRS equations. However, when three exponential models were fitted to the gas production data, only the asymptote values could be satisfactorily calibrated or cross-validated. When the NIRS equations were used in the validation set, the static gas volumes were predicted with R 2 values between 0.60 and 0.71 (S.E. 0.65 to 3.83, for the gas volumes from 3 to 96 h). These results were less accurate than within the calibration set, but they were still better than when the parameters from the exponential models were fitted. The results suggested that the calibration of static gas volumes is a more promising alternative than to fit specific parameters of kinetic fermentation models by NIRS.

Prediction of Cooked Rice Texture Quality Using Near‐Infrared Reflectance Analysis of Whole‐Grain Milled Samples

ABSTRACTRice quality is based on chemical and physical properties affecting its appearance, flavor, and texture characteristics. Sensory quality can be assessed by a combination of descriptive sensory and physicochemical property evaluations. The purpose of the present study was to assess the potential of near‐infrared reflectance spectroscopy (NIRS) and NIRS in combination with other physicochemical measurements for the determination of sensory texture attributes in whole‐grain milled rice samples. Six rice samples representing combinations of variety and growing locations received treatments of two degrees of milling and five drying conditions to achieve final moisture levels of 12 or 15% (n = 120). Quality measurements of the cooked rice included sensory and instrumental texture analyses. Quality measurements of the uncooked rice included amylose and protein (chemical reference), whiteness, transparency, and degree of milling (appearance units of milled rice), and NIRS analyses. Partial least squares (PLS) regression was used to reveal the relationships between the different types of measurements. The sensory texture attributes: manual adhesiveness (MADHES), visual adhesiveness (VADHES), and stickiness to lips (STICKI) were related to deep‐milled samples and positively correlated to amylose, whiteness, and milling degree. The attribute roughness (ROUGH) was related to light‐milled samples and positively correlated to protein and negatively correlated to amylose. The main variation in sensory attributes was a result of amylose and protein contents of the rices. A noise‐compensation value, relative ability of prediction (RAP), was used to express the degree of prediction (1.0 = best possible prediction). NIRS gave the best prediction results for the texture attributes: MADHES, VADHES, and STICKI with an RAP of 0.57, 0.54, and 0.56, respectively. NIRS is best at predicting texture characteristics of cooked rice perceived in the visual, tactile, and initial oral phases of sensory evaluation. The calibration of NIRS plus physicochemical variables did not improve the predictability of sensory texture over NIRS alone. The prediction of sensory texture in rice by NIR needs to be further investigated on a large number of samples with different varieties, growing locations, cultivation methods, harvesting methods, and processing after harvesting.

Analysis of Amino and Fatty Acid Composition in Soybean Seed, Using Near Infrared Reflectance Spectroscopy

AbstractSoybean [Glycine max (L.) Merr.] is an important animal and human food source, mainly because of its high protein and oil concentrations. However, there is a need to improve the quality of soybean protein and oil. The objectives of this study were to (i) determine the potential use of near infrared reflectance spectroscopy (NIRS) for amino and fatty acid analysis in soybean seed and (ii) compare NIRS amino and fatty acid equations developed with ground‐seed and whole‐seed soybean samples. A total of 90 NIRS calibration samples and 26 NIRS validation samples were used for NIRS equation development and validation, respectively. All 116 soybean samples were grown at St. Paul and Rosemount, MN, in 1994 and 1995. Seventeen amino acid and five fatty acid NIRS equations for both groundseed and whole‐seed soybean samples were developed using the calibration samples. Validation of these NIRS equations showed a wide range for bias (−1.7 to 3.2 g kg−1), standard error of prediction corrected for bias [SEP(C)] (1.4 to 13.1 g kg−1), and coefficient of determination (R2) (0.38 to 0.85) for all ground‐seed NIRS equations, and a broad range for bias (−3.1 to 4.2), SEP(C) (2.6 to 17.8), and R2 (0.06 to 0.83) for all whole‐seed NIRS equations. It was concluded that NIRS could be used as a gross screening method for many amino and fatty acids contained in soybean seed. Additionally, NIRS was more accurate for amino and fatty acid analysis of ground‐seed than wholeseed soybean samples.

Prediction of the chemical composition of white clover by near‐infrared reflectance spectroscopy

The aim of this study was to assess near‐infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for estimating quality parameters in white clover (Trifolium repens). NIRS calibrations were derived for ash, crude protein (CP), crude fibre (CF), neutral detergent fibre (NDF), acid detergent fibre (ADF) acid detergent lignin (ADL). To maximize variability, the set of samples for analysis was selected from field plots, harvested in two contrasting environmental locations over a period of 5 years. The samples were scanned using a NIR‐Systems model 5000 monochromator. A total of 145 samples was used to calibrate and cross‐validate the equations derived. A separate independent set (sixty‐five samples) was used for testing the predictions. Standard errors of calibration and cross‐validation, expressed as percentages of the mean, were 3·5 and 5·5 for ash, 3·0 and 4·2 for CP, 4·4 and 5·0 for CF, 3·7 and 4·7 for NDF, 3·3 and 4·2 for ADF and 14 and 16 for ADL respectively. The NIRS calibrations obtained from this study have the potential to be used in breeding programmes to assess the quality of white clover and for agronomic evaluations.

Factors affecting the prediction of organic matter Digestibility of grass silage by near infrared reflectance spectroscopy

Earlier studies (Barber et al., 1990) showed the superiority of near infrared reflectance spectroscopy (NIRS) for predicting the organic matter digestibility (OMD) in vivo of grass silage over fibre and in vitro procedures. However, during routine application occasional erroneous values were predicted for which there were no obvious reasons. Baker and Barnes (1990) reported that the likely sources of the problems contributing to the errors were instrumental and environmental noise, sample particle size effects and variable moisture content of the samples. These authors also reported that standard normal variate - detrend (SNV-D) scatter correction procedure of Barnes et al. (1989) could be used to reduce the effects of particle size variation and they also emphasised the need to test NIRS calibrations for repeatability. The purpose of the present work was to evaluate the use of the SNV-D scatter correction procedure, the techniques for reducing the sensitivity of calibrations to residual moisture and methods to improve the repeatability of the predicted OMD in vivo values of grass silage. In addition, a further objective was to compare three calibration methods, namely modified stepwise regression (MSR), modified partial least squares (MPLS) and principal component analysis (PCA).

Fecal NIRS Equations for Predicting Diet Quality of Free-Ranging Cattle

The usefulness of near infrared reflectance spectroscopy (NIRS) for predicting diet quality of free-ranging cattle through fecal analysis was examined. Diet samples were obtained with esophageal fistulated steers; subsequently, study areas were grazed with nonfistulated lactating and dry cows to provide fecal samples representing differing forage diet quality. Diet samples, which were analyzed by conventional laboratory procedures for in vivo corrected digestible organic matter (DOM) and crude protein (CP), provided dependent variable reference data while fecal sample spectra provided independent variable data for development of NIRS predictive equations by stepwise regression. Equations were developed from a data set at one location with subsequent equation development using expanded data ranges obtained by adding samples from a second location. Standard errors of calibration (SEC) and validation (SEV) for the DOM equation developed from the expanded data range were 1.66 and 1.65, respectively; these values were nearly equivalent to the laboratory standard error (SEL) of 1.68. SEC and SEV for the CP equation developed from the expanded data range were 0.89 and 0.93, respectively, compared to the 0.44 SEL. Coeffkients of determination for DOM and CP equations were 0.80 and 0.92, respectively. These statistical parameters developed from fecal spectra to predict forage diet quality are equal to or better than statistics reported in the literature for NIRS equations developed using forage spectra. Furthermore, equation standard errors were within acceptable limits for NIRS calibrations. No effects of physiological stage of animals on calibration were noted in this study. Results are interpreted to This manuscript was published with the approval of the Director, Texas Agricultural Experiment Station, Texas AM Evelyn Kapes for work in the laboratory; Drs. J.E. Huston, J.W. Holloway, and C.W. HanL and the Behmann Foundation, Corpus Christi, Texas, for providing funds to support this research. Manuscnpt accepted 5 Sept. 1991.

Quantitative analysis of stevioside in the leaves of Stevia rebaudiana by near infrared reflectance spectroscopy

AbstractStevioside, a diterpenic glycoside extracted from the leaves of Stevia rebaudiana (Bert) Bertoni, is a natural sweetener 300 times sweeter than sucrose. HPLC is routinely used to measure stevioside content in Stevia leaves. Our objectives were to investigate the use of near infrared reflectance spectroscopy (NIRS) to analyse stevioside concentration in Stevia leaves. The NIRS calibrations were developed from 64 samples covering the range normally found in Stevia leaves (4‐13%). Another 30 samples were used for validation. Standard error of calibration and coefficient of multiple determination (R2) were 1.47 and 0.90 respectively, using the first derivative mathematical treatment. The validation results, although less accurate, suggest that NIRS is a precise and simple method for routine stevioside determination in Stevia leaves.

A comparison of near-infrared reflectance spectroscopy with three in vitro techniques to predict the digestibility in vivo of untreated and ammonia-treated cereal straws

The results of a comparison of three in vitro procedures and near-infrared reflectance spectroscopy (NIRS) to predict the organic matter digestibility (OMD) of cereal straws measured in vivo are reported. The straws used comprised 101 untreated (54 wheat, 43 barley, four oat) and 22 ammonia-treated (nine barley, nine wheat, four oat). The in vitro digestibility techniques tested used rumen fluid-pepsin (RFP), pepsin-cellulase (PC) and neutral detergent-cellulase (NDC). A number of NIRS calibration equations were developed, although the best single equation (BSE) contained five spectral terms with wavelength segments centred on 2218, 2300, 1562, 2266 and 1688 nm. On calibration, the NIRS-BSE, RFP, NDC and PC methods accounted for 74.1%, 61.2%, 60.8% and 60.2% of the variability in OMD with NIRS and PC describing the OMD of untreated and ammonia-treated straws with single regression lines. The most accurate prediction of the OMD of a blind test population of straws (35 untreated, seven ammonia-treated) was obtained by the NIRS-BSE ( R 2 = 64.7%; standard error of prediction (SEP), 0.0371). It was concluded that the use of NIRS to predict the OMD of cereal straws can be more accurate than in vitro laboratory procedures.

Screening Sericea Lespedeza Germplasm for Herbage Quality

AbstractSericea lespedeza [Lespedeza cuneata (Dumont) G. Don] is recognized as being a relatively poor quality forage because of high concentrations of tannins in its leaves. Therefore, an accurate assessment of tannins in the leaves is essential for the selection of low tannin lines. The objectives of our research were to investigate improved methods of tannin analysis for the screening of the lespedeza germplasm, and to determine the relationship between in vitro dry matter digestibility (IVDMD), crude protein (CP), and leaf tannins. Field experiments containing 81 lespedeza entries were established at two locations. Herbage samples were harvested twice during the growing season for 2 yr and the leaves analyzed for IVDMD, CP, and tannin concentration by near infrared reflectance spectroscopy (NIRS). Results of an HCI‐vanillin analysis of aqueous acetone extracts of fresh‐frozen samples (ACE‐FF) and oven‐dried samples (ACE‐OD), and methanolic extracts of oven‐dried samples (METOD) were used to derive the NIRS tannin equations. Standard errors of calibration (SEC) were lowest for the MET‐OD data and highest for the ACE‐FF data. However, coefficients of variation (CV) were inversely proportional to SEC. Coefficients of variation of field experiments were also lowest when samples were analyzed by the NIRS tannin equation derived from ACE‐FF data. Differences in tannin concentration, CP, and IVDMD existed among entries, locations, and seasons. Differences between locations and seasons were due to changes in moisture and temperature. Strong negative correlations were found for the ACE‐FF tannin data and IVDMD (r = −0.90 to −0.92), and ACE‐FF data and CP (r = −0.66 to −0.70) relationships. Our results indicate that the tannin analysis of ACE‐FF is a better reference method for NIRS calibration and subsequent screening of the lespedeza germplasm, and that the relationships between tannins, CP and IVDMD, suggest that a significant improvement in herbage quality may be achieved.

Use of residual maximum likelihood to evaluate accuracy of two NIRS calibration procedures, relative to Kjeldahl, for determining nitrogen concentration of soybeans

The accuracy of estimates of plant properties based on near-infrared reflectance spectroscopy (NIRS) varies with many factors including the biological material in question and the method used to calibrate the NIRS instrument. This study investigated the accuracy, relative to Kjeldahl analysis, of NIRS analysis based on two calibration methods in estimating nitrogen concentration of four stages and/or parts of soybean (Glycine max (L.) Merr.) plants. Samples of whole top growth at anthesis, whole top growth at maturity, whole top growth at maturity excluding seeds, and seeds were obtained from two field trials and one phytotron experiment. Two Kjeldahl determinations of nitrogen concentration were obtained for each sample, as well as reflectance values at each of 19 infrared wavelengths, using a Technicon InfraAlyser 400R. Different subsets of the sample data were used for calibration and assessment of accuracy. The instrument was calibrated using stepwise multiple linear regression (SMLR) and principal component regression (PCR). The residual maximum likelihood procedure was useful in showing that NIRS estimates based on either SMLR or PCR were at least as accurate as Kjeldahl estimates for all stages and/or parts except whole top growth at maturity excluding seeds. Key words: Calibration, principal component regression, stepwise regression

Moisture Analysis in Forage by Near-Infrared Reflectance Spectroscopy: Collaborative Study of Calibration Methodology

Abstract Fifteen collaborating laboratories analyzed 16 forage samples including 3 blind duplicate pairs for moisture by air-oven (AO) method 7.007 (14th Ed.; 930.15, 15th Ed.) and nearinfrared reflectance spectroscopy (NIRS). Laboratories performed method 7.007 on 50 calibration samples and applied the NIRS calibration method Independently. NIRS moisture equations were used to predict the 16 test samples, and the values were compared to those for method 7.007. Moisture concentration of the test samples ranged from approximately 6 to 16%. Within-laboratory repeatability (sr) ranged from 0.10 to 0.18% and 0.16 to 0.39% for NIRS and method 7.007, respectively. Between-laboratory reproducibility (sR) ranged from 0.22 to 0.57 and 0.29 to 0.57 for NIRS and method 7.007, respectively. Repeatability relative standard deviations (RSDr) for the NIRS and AO methods ranged from 1.18 to 1.50% and 1.84 to 3.68%, respectively. The range in the average reproducibility relative standard deviations (RSDR) for the NIRS and AO methods were 1.29-7.49% and 3.64-6.66%, respectively. The NIRS method demonstrated consistently lower wlthln-laboratory RSDr agreement and between-laboratory variabilities equal to method 7.007. Thereby, we demonstrated that NIRS can be used as a standard method for the determination of 6-16% moisture In forages. The method has been adopted official first action by A0AC.

PMSG-dose in Finnsheep embryo production

Interest in measuring faecal glucocorticoid metabolites (FGM) as indicators of physiological homeostasis and performance in wildlife is increasing. However, current reference techniques, specifically enzyme immunoassays (EIAs) and radioimmunoassays (RIAs), are expensive, time-consuming, reagent-based, and the samples are destroyed during their application. Conversely, near infrared reflectance spectroscopy (NIRS) is a rapid, reagent-free and non-destructive technique, which, once calibrated by standard laboratory methods, can be used at a low cost. The objectives of this study were to evaluate the feasibility of using NIRS to predict glucocorticoid metabolite concentrations in red deer (Cervus elaphus) faeces, as well as the effect of lyophilisation and oven drying on FGM quantification. Seventy-eight fresh faecal samples were collected directly from the rectum of hunter-harvested red deer and then divided into two equal portions; one portion of each individual sample was lyophilised and the other portion was oven-dried. After dehydration, all faecal samples were ground and then analysed by RIA (standard laboratory technique) and scanned with an NIR spectrophotometer. Modified partial least squares regression was used to generate NIRS calibration equations for both lyophilised and oven-dried samples and a cross-validation procedure was employed for their optimisation. Near infrared reflectance spectroscopy proved to be a feasible, acceptably accurate and reliable technique for predicting FGM concentrations in red deer faeces subjected either to lyophilisation or to oven drying. Calibration and cross-validation results indicated that predictive equations for lyophilised faeces were slightly more precise and robust than for the oven-dried ones (lyophilised: R2 = 0.90, r2cv = 0.81, RPD = 2.72; oven-dried: R2 = 0.88, r2cv = 0.79, RPD = 2.26; CV: cross-validation, RPD: ratio of performance to deviation). Nevertheless, oven-dried faeces may be used as an alternative to lyophilised ones to quantify FGM levels accurately, provided that an appropriate combination of dehydration time and temperature is used during the desiccation process. High degrees of association and statistically significant positive correlations (p < 0.001) were found between the lyophilised and oven-dried samples regarding their FGM content, both for RIA assays and NIRS analyses. This study provides a new approach for assessing stress levels in free-ranging populations and has practical implications concerning wildlife monitoring as it makes it possible to improve the efficiency and reduce the cost and time constraints of current analytical techniques.

Carbon and Nitrogen Analysis of Soil Fractions Using Near‐Infrared Reflectance Spectroscopy

AbstractA nondestructive method to determine total C and N concentrations in soil size fractions is desirable when a limited sample is available. Near‐infrared reflectance spectroscopy (NIRS) was used to determine the total C and N concentrations in silt (50.0–2.0 µm) and coarse clay (2.0–0.2 µm), separated from 12 surface soils, by regressing the diffuse reflectance of near‐infrared radiation with constituent concentrations determined using combustion techniques. The correlation coefficients (R2) of the calibration equations were 0.93 for C and 0.89 for N, and the standard errors associated with NIRS predictions were 6.2 g kg−1 soil for total C and 0.6 g kg−1 for total N. Equation development with only silt samples improved the accuracy of NIRS calibration equations. Coefficients of variation [CV = (standard error of performance ÷ mean of the combustion procedure) × 100] for validation sample sets ranged from 14 to 19%, which is within the acceptable range for determining inorganic elements in plant tissues. We conclude that NIRS can be used to predict C and N concentrations in soil size fractions.

Determination of cell wall carbohydrates in forages by near infrared reflectance spectroscopy

The use of conventional techniques to analyze neutral sugar constituents of cell wall polysac-charides in forages has been time consuming and not suited for routine analysis. The purpose of this study was to determine the ability of near infrared reflectance spectroscopy (NIRS) to analyze pectin, arabinose, xylose and glucose from forage cell walls. The concentration of pectin, arabinose, xylose and glucose was determined by conventional fractionation and quantification methods on whole-plant tissue from six cool season legumes, two cool season grasses and four warm season grasses commonly grown through the southeastern U.S.A. Forages were harvested at vegetative, first bloom or mature stages of growth. Calibration equations were obtained by multiple linear regression of conventional laboratory analysis values on NIR spectra from 72 randomly chosen spectra using a scanning monochromator NIR spectro-computer system. Calibration equations were verified with 24 additional samples from the original population. From four to eight wavelengths were needed to develop the best equation for the carbohydrate fractions. The coefficients of multiple determination ranged from 0.86 to 0.92. The standard errors of calibration were 0.98, 0.83, 2.48 and 2.21 for pectin, arabinose, xylose and glucose, respectively. Analysis of residuals indicated that error from NIRS prediction was associated with the experimental design. Forage × maturity interactions were significant for arabinose, xylose and glucose residuals. The precision of NIRS analysis was moderately high, but not as precise as previous reports using a single forage species. Better accuracy will probably be achieved by developing NIRS calibration equations for individual species or groups of similar forage species.

Near Infrared Reflectance Spectroscopy. I. Calibration Techniques for Forage Quality Assessment

Abstract It is very time consuming and resource costly to rank forages by wet laboratory methods; therefore, near infrared reflectance spectroscopy (NIRS) was used to evaluate hybrid bermudagrass (Cynodon dactylon (L.) Pers.) cultivars for improvements In quality. Quality was assessed by the determination of acid detergent fiber (AOF), crude protein (CP), dry matter (DM), and in vitro dry matter digestibility (iVDMD). The samples were taken from plant breeding trials from 1983,1984,1985, and 1986. They included 32 cultivars, 4 cuttings, and 5 replications each for a total of 640 possible observations per year. This paper describes the process by which the NIR spectrometer was calibrated for each constituent and the effect of various methods of calibration on the performance of the NIRS system to determine quality in bermudagrass hybrids. Excellent one-year equations were obtained from calibrations using the bermudagrass parents in the 1983 samples (R2 = 0.83, 0.97, 0.70; SEC = 0.94, 0.76, 3.64, for ADF, CP, and IVDMD, respectively). Attempts to use outside populations did not produce equivalent results for the first year. The 1983 equations did not fit the 1984 and 1985 samples because of changes that were made to the spectrometer and the yearly differences in the samples. The instrument was then recalibrated with a diverse set of legumes and grass hay samples that contained some bermudagrass. These open population calibrations adequately ranked the bermudagrass entries. We documented that bermudagrass genotypes can be quickly and precisely ranked for forage quality traits via use of open population NIRS calibration equation.

Prediction of Botanical Composition Using NIRS Calibrations Developed from Botanically Pure Samples

Point quadrat, hand separation, and histological methods for determining botanical composition are tedious, time consuming, and expensive. Near‐infrared reflectance spectroscopy (NIRS) was used as a rapid method to predict botanical composition of grass‐legume mixtures clipped from field plots. Our objective was to determine if a group of samples, each consisting of one of three botanical components, could be used for calibration in lieu of samples containing mixtures of the components. Calibration equations were developed for NIRS prediction of a number of legumes, Caucasian bluestem [(Bothriochloa caucasica (Trin.) C.E. Hubb.], and cheatgrass (Bromus tectorum L.). Hand separations of clipped mixtures were used to validate the calibration equations. All forage samples were grown in silty soils of the Dale series. Some evidence existed for an interaction between light scatter and mixing of two pure species, but it was eliminated effectively by second derivative transformation of the spectra. Biases existed for all components (36‐61 g/kg) but were smaller than standard errors of performance (52‐104 g/kg), except for legumes. Linearity was excellent for prediction for percentage legume, and validation was more precise for legume than for Caucasian bluestem or cheatgrass. This method of calibration for a closed population is recommended. Care must be taken to validate equations using mixed samples to insure linearity of unknowns between the extremes. Further, unknowns should be scanned along with calibration samples so that the same day to day instrument variation is associated with both sample sets.

QUALITY OF ALFALFA HERBAGE ESTIMATED BY A PREPARED CELLULASE SOLUTION AND NEAR INFRARED REFLECTANCE SPECTROSCOPY

Little information is available on estimating in vitro dry matter digestibility (IVDMD) of alfalfa (Medicago sativa L.) herbage by a prepared cellulase solution (PCS) and then using these IVDMD estimates to calibrate near infrared reflectance spectroscopy (NIRS) equations. Objectives were to compare PCS digestion to that by two rumen fermentation procedures, including true in vitro digestibility (TIVD), and develop NIRS equations to estimate TIVD, neutral detergent fiber, and acid detergent fiber of alfalfa hay. Seventy-eight alfalfa samples, having a wide range in herbage quality, were analyzed for IVDMD using five different PCS procedures and two rumen fermentation procedures (true and apparent in vitro digestibility). The best NIRS calibration equation for TIVD had R2 of 0.92 and a standard error of selection of 20.7 g kg−1. Correlations between IVDMD and TIVD obtained by the various PCS assays ranged from 0.91 to 0.96 (P &lt; 0.01), with regression coefficients ranging from 0.94 to 0.98. We concluded that PCS gave rapid and accurate estimates of TIVD and that NIRS could accurately estimate TIVD of a wide range of alfalfa herbage quality.Key words: Acid detergent solubles, fungal cellulase solubles, in vitro digestible dry matter, Medicago sativa L., neutral detergent solubles, alfalfa

Pasture Sampling Requirement for near Infrared Reflectance Spectroscopy Estimates of Botanical Composition

Near infrared reflectance spectroscopy (NIRS) has been used predict species composition and plant components in hay and pasture samples. Two measures of error, bias and standard error of prediction (SEP), are important to consider with respect to accuracy of NIRS predictions of laboratory values of samples. Our objectives were to determine how sample size affects the precision and accuracy of the NIRS estimate of the mean of pasture samples. In Exp. I, four grazed pastures of pure and mixed alfalfa (Medicago sativa L.) and tall fescue (Festuca arundinacea Schreb.) were sampled over 12‐d grazing period in July 1987. Samples were separated into percentage of tall fescue, alfalfa leaf, and alfalfa stem and used for development of NIRS calibration equations. The SEP was used to calculate boundary limits, which should contain the NIRS estimates for 1, 5, 8, or 10 pasture samples. Boundary limits decreased from 11.0 to 2.3% units as sample number increased. The increase in accuracy was inversely related to the square root of the sample size. Experiment 2 was similar to Exp. I, except all samples gathered on the first day of grazing within one of the pasture mixtures were used as a verification set for the NIPS calibration equations. Pasture means and standard deviations were similar for laboratory and NIPS values. We conclude that the pasture sample size needed to obtain an acceptable confidence interval of the pasture mean for botanical composition is a sufficient sample size for accurate and precise NIPS prediction of that mean.

Comparison Between Liquid and Paste Karl Fischer Extractions for NIRS Water Calibrations

Abstract The acceptance of near infrared reflectance spectroscopy (NIRS) for the routine analysis of water in forage was made possible by the application of Karl Fischer (KF) water analysis as the primary reference method during calibration. Water is extracted by ball milling the sample in the presence of methanol (1:50), and an aliquot of the liquid phase is sampled and titrated. An alternative extraction utilizes a 20 g sample with 20 mL methanol, and an aliquot of the paste is sampled and titrated. The objectives of this study were to compare extraction procedures (i.e., liquid vs paste) on the determination of water in 75 milled grains and concentrate rations, and to develop and compare NIRS calibrations for water on the basis of both extraction methods. The majority of the samples (N = 61) had a 1.05 percentage unit greater water content (P &amp;lt; 0.05) from the paste method than from the liquid method. Bias between the extraction procedures was positively correlated (r2 = 0.84) to starch content. The lowered extraction efficiency of the liquid method is explained by its inability to account for water bound to starch. NIRS calibration and equation validation statistics were superior with water data from the paste method. The increased calibration errors with the liquid method also appear to reflect the inability of the method to accurately measure the total water concentration of the samples in relation to the water measurement based on the NIRS signals. The study concludes that the KF paste method is a superior primary reference method for the determination of water in flour obtained from cereal grains and concentrate rations.