Near Infrared Reflectance Spectroscopy Prediction Research Articles

Determination of potential denitrification in a range of tropical topsoils using near infrared reflectance spectroscopy (NIRS)

Microbial denitrification plays a central role in nitrous oxide (N 2O)-emitting processes, which are involved in ecosystem services such as crop production and climate regulation. Field characterization of N 2O-emitting processes being time-consuming due to great variability, laboratory determination of potential denitrification (upon incubation) is often used as a valuable test. Near infrared reflectance spectroscopy (NIRS) is a time- and cost-effective approach that has been reported to allow accurate determination of several soil properties. The objective of the present study was to assess the interest of NIRS for determining potential denitrification over a set of 460 topsoils sampled under crops, tree plantations, savanna or rainforest, originating from Madagascar, Congo-Brazzaville, Brazil, and French Guiana. Prediction of potential denitrification using NIRS was satisfying over the total set, especially with LOCAL calibration, which builds a model for each sample separately using its spectral neighbours in the calibration subset ( R 2 = 0.79 for validation). For the other sets, either textural or geographical, global calibration only was performed, involving for each set a unique prediction model built with all calibration samples. The accuracy of NIRS determination depended on the sample set, decreasing in the following order: Malagasy clayey set > total set ≈ Brazilian sandy loam set > coarse-textured set (Congo-Brazil) ≈ Guianese sandy clay loam set ≈ Congolese sandy set > non-clayey set (Congo-Brazil-Guiana), with cross-validation R 2 ranging from 0.88 to 0.44 (external validation was not carried out for small-sized sets). Thus NIRS prediction was more accurate over the clayey homogeneous set than over the non-clayey heterogeneous set. As a result of global calibration, potential denitrification was expressed as a linear combination of absorbance at every wavelength. Wavelengths that contributed most to NIRS prediction of soil potential denitrification corresponded to wavelengths that literature has assigned to organic nitrogenous compounds, amide-containing ones especially, and to carbonaceous compounds such as cellulose or including CH 3 or CH 2 groups. This related to the importance of amides in soil organic nitrogen and microbial biomass, and to the dependence of denitrification on soil organic matter. In short, NIRS is a time- and cost-effective approach that proved relevant for determining soil potential denitrification with acceptable accuracy, especially for clayey samples or when LOCAL calibration was performed.

Mehlich 3 extractable nutrients as determined by near-infrared reflectance spectroscopy

Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R2), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R2 ≥ 0.7, RPD ≥ 1.75, and RER ≥ 8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R2 < 0.7, RPD < 1.75, and RER < 8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 ≤ slope ≤ 1.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.5 ± 0.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity. Key words: Soil analysis, Mehlich 3, near-infrared reflectance spectroscopy, calibration

Opportunities for, and limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review

Near infrared reflectance spectroscopy (NIRS) is a cost- and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r2), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.Key words: Near infrared reflectance spectroscopy, soil analysis, calibration

Near‐Infrared Reflectance Spectroscopy Prediction of Soil Properties: Effects of Sample Cups and Preparation

Most methods for soil analysis are based on wet chemistry. Near infrared reflectance spectroscopy (NIRS) is a cost‐effective and environmentally sound alternative technique. This study evaluated the effect of sample fineness (0.2, 0.5, 1, and 2 mm) and sample cups (transport versus spinning) on the accuracy of NIRS predictions of soil texture, cation‐exchange capacity, pH, total C and N, organic C, and potentially mineralizable N (Nmin) using 150 air‐dried samples collected from a 15‐ha site dominated by Humaquept, Endoaquept, and Dystrochrept soils. The best spectral pretreatment was determined for each property. Principal component analysis (PCA) was used to select samples in calibration and validation sets. Calibration equations were developed using the modified partial least square regression. The accuracy of NIRS prediction was evaluated using three statistics for the prediction set: coefficient of determination (R2), ratio of performance deviation (RPD), and ratio error range (RER). Across the factorial designed treatments, successful calibrations were observed for clay, sand, and Nmin (R2 ≥ 0.90, RPD ≥ 3, RER ≥ 15). Prediction accuracy of pH was poor (0.51 ≤ R2 ≤ 0.74, 1.39 ≤ RPD ≤ 1.92, 6.13 ≤ RER ≤ 8.33), while it was intermediate for remaining properties. Sample fineness of 2 mm appeared to be sufficient since finenesses of 0.2, 0.5, or 1.0 mm did not improve calibration accuracy. These findings at small scale should not be extrapolated and further investigations are required to validate them at a larger scale.

Determination of soil content in chlordecone (organochlorine pesticide) using near infrared reflectance spectroscopy (NIRS)

Chlordecone is a toxic organochlorine insecticide that was used in banana plantations until 1993 in the French West Indies. This study aimed at assessing the potential of near infrared reflectance spectroscopy (NIRS) for determining chlordecone content in Andosols, Nitisols and Ferralsols from Martinique. Using partial least square regression, chlordecone content conventionally determined through gas chromatography–mass spectrometry could be correctly predicted by NIRS ( Q 2 = 0.75, R 2 = 0.82 for the total set), especially for samples with chlordecone content <12 mg kg −1 or when the sample set was rather homogeneous ( Q 2 = 0.91, R 2 = 0.82 for the Andosols). Conventional measures and NIRS predictions were poorly correlated for chlordecone content >12 mg kg −1, nevertheless ca. 80% samples were correctly predicted when the set was divided into three or four classes of chlordecone content. Thus NIRS could be considered a time- and cost-effective method for characterising soil contamination by chlordecone.

Near-infrared reflectance spectroscopy prediction of neutral detergent-soluble carbohydrates in timothy and alfalfa

Carbohydrates in forage crops can be divided into neutral detergent-insoluble fiber and neutral detergent-soluble carbohydrates (NDSC); the latter includes organic acids (OA), total ethanol:water-soluble carbohydrates (TESC), starch, and neutral detergent-soluble fiber (NDSF). The accurate and efficient estimation of NDSC in forage crops is essential for improving the performance of dairy cattle. In the present study, visible and near-infrared reflectance spectroscopy (NIRS) were applied to evaluate the feasibility of predicting OA, TESC, starch, NDSF, NDSC, and all related constituents used to calculate these 5 carbohydrate fractions in timothy and alfalfa. Forage samples (n=1,008) of timothy and alfalfa were taken at the first and second harvests at 2 sites in 2007; samples were dried, ground, and then scanned (400 to 2,500nm) using an NIRSystems 6500 monochromator. A calibration (n=60) and a validation (n=15) set of samples were selected for each species and then chemically analyzed. Concentrations of TESC and NDSC in timothy, as well as starch in alfalfa, were successfully predicted, but many other carbohydrate fractions were not predicted accurately when calibrations were performed using single-species sample sets. Both sets of samples were combined to form new calibration (n=120) and validation (n=30) sets of alfalfa and timothy samples. Calibration and validation statistics for the combined sets of alfalfa and timothy samples indicated that TESC, starch, and NDSC were predicted successfully, with coefficients of determination of prediction (RP2) of 0.92, 0.89, and 0.93, and a ratio of prediction to deviation (RPD) of 3.3, 3.1, and 3.6, respectively. The NDSF prediction was classified as moderately successful RP2=0.88, RPD=2.8. The NIRS prediction of OA was unsuccessful RP2=0.38, RPD=1.3. All related constituents were predicted successfully RP2>0.90, RPD>3.0 by NIRS except ethanol-insoluble residual OM, with RP2=0.75 and RPD=1.9. Our results confirm the feasibility of using NIRS to predict NDSC, its fractions, and other related constituents, except for OA and ethanol-insoluble residual OM, in timothy and alfalfa forage samples.

Quantification of Kavalactones and Determination of Kava (Piper methysticum) Chemotypes Using Near-Infrared Reflectance Spectroscopy for Quality Control in Vanuatu

Kava ( Piper methysticum Forst f., Piperaceae) has anxiolytic properties and the ability to promote a state of relaxation without the loss of mental alertness. The rapid growth of the nutraceutical market between 1998 and 2000 has been stopped by a ban in Europe and Australia because of some suspicion of liver toxicity. It is now important to develop a fast, cheap, and reliable quality test to control kava exports. The aim of this study is to develop a calibration of the near-infrared reflectance spectroscopy (NIRS) using partial least-squares (PLS) regression. Two hundred thirty-six samples of kava roots, stumps, and basal stems were collected from the Vanuatu Agricultural Research and Technical Centre germplasm collection and from four villages. These samples, representing 45 different varieties, were analyzed using NIRS to record their absorption spectra between 400 and 2500 nm. A set of 101 selected samples was analyzed for their kavalactone content using HPLC. The results were used for PLS calibration of the NIRS. The NIRS prediction of the kavalactone content and the dry matter were in agreement with the HPLC results. There were good correlations between these two series of results, and coefficients ( R (2)) were all close to 1. The measurements were reproducible and had repeatability on par with the HPLC method. The NIRS system has been calibrated for the six major kavalactone content measurements, and it is suggested that this method could be used for quality control in Vanuatu.

Hot Topic: Application of Support Vector Machine Method in Prediction of Alfalfa Protein Fractions by Near Infrared Reflectance Spectroscopy

The object of this study was to explore the potential for support vector machine (SVM) to improve the precision of predicting protein fractions by near infrared reflectance spectroscopy (NIRS). Generally, most protein fractions determined in Cornell Net Carbohydrate and Protein System (CNCPS), especially the neutral detergent insoluble protein (NDFCP) and acid detergent insoluble protein (ADFCP), could not be accurately predicted by the commonly used partial least squares (PLS) method. A recently developed chemometric method, SVM, was applied in NIRS prediction of alfalfa protein fractions in this study. Two hundred thirty alfalfa samples were scanned on a near infrared reflectance spectrophotometer, and analyzed for crude protein (CP), true protein precipitated in tungstic acid (TCP), borate-phosphate buffer-insoluble protein (BICP), NDFCP, and ADFCP. These 5 laboratory proteins and the CNCPS protein fractions A, B1, B2, B3, and C were predicted by NIRS using the PLS and SVM methods. According to PLS-NIRS regression, CP, TCP, BICP, A, and B2 obtained the determination coefficient of prediction (Rp2) of 0.96, 0.91, 0.94, 0.94, and 0.93, and the ratios of standard deviation of prediction samples: standard error of prediction samples (RPD) values were 5.07, 3.31, 3.98, 3.96, and 3.91. Neutral detergent insoluble protein, ADFCP (fraction C), B1, and B3 were predicted with Rp2 of 0.75, 0.83, 0.30, and 0.62, and RPD values of 1.98, 2.42, 1.20, and 1.62; Calibrated by the SVM-NIRS method, Rp2 values of CP, TCP, BICP, NDFCP, ADFCP(C), A, and B2 achieved 0.99, 0.97, 0.97, 0.90, 0.93, 0.97, and 0.97, respectively. The RPD values of those fractions were 8.68, 8.26, 6.11, 3.08, 3.69, 5.97, and 5.81, respectively. The Rp2 and RPD values of fractions B1 and B3 were 2.67 and 0.87 (B1) and 2.51 and 0.75 (B3) directly predicted by SVM-NIRS model. In this study, the chemical analysis results of B1 and B3 were also correlated with calculated results from TCP-BICP and NDFCP-ADFCP, which were predicted by SVM-NIRS models. The B1 protein fraction achieved Rp2 and RPD values of 0.87 and 3.61, whereas values for B2 were 0.75 and 2.00. Data suggested that use of SVM methods in NIRS technology could improve the accuracy of predicting protein fractions. This study showed the potential of increasing the NIRS prediction accuracy to a level of practical use for all protein fractions, except B3.

Application of Near-Infrared Reflectance Spectroscopy to the Evaluation of Rutin andD-chiro-Inositol Contents in Tartary Buckwheat

Tartary buckwheat [Fagopyrum tataricum (L.) Gaench] is rich in rutin and D- chiro-inositol (DCI), which have beneficial effects in the treatment of hemorrhagic diseases and insulin-resistant diseases, respectively. The current methods of extraction and detection of rutin and DCI are complex and time-consuming; a simple way of analyzing these compounds in the native matrix would be desirable. In this work, near-infrared reflectance spectroscopy (NIRS) was applied to determine the contents of rutin and DCI in tartary buckwheat. The spectral data were compared with those determined from high-performance liquid chromatography (HPLC) methods. Models for predicting rutin and DCI contents in buckwheat were developed using a partial least-squares algorithm. Cross-validation procedures indicated good correlations between HPLC data and NIRS predictions (R2 = 0.76 for rutin and R2 = 0.86 for DCI). The rutin content ranged from 0.998 to 1.75%, while the DCI content covered 0.179-0.200%. The results showed that NIRS, a well-established and widely applied technique, could be applied to determine rutin and DCI in tartary buckwheat rapidly and nondestructively.

Effect of preservatives addition on the shelf‐life extensions and quality of flat bread as determined by near‐infrared spectroscopy and texture analysis

SummaryMany people do not have refrigeration or frozen storage to inhibit mould growth and keep the Arabic bread fresh for more than a few days. Therefore, shelf‐life extension is necessary for this type of bread. The present study hypothesised that the addition of preservatives may be done in Arabic flat bread (AFB) to extend shelf‐life. Thus, objectives of this study were to evaluate selected preservatives to inhibit mould growth and to employ physical techniques, to monitor bread aging. Three preservatives, fumaric acid (0.2%, F), sodium propionate (0.3%, P), and sodium propionate‐fumaric acid mixture (PF) were used. Tensile tests, and near‐infrared reflectance spectroscopy (NIRS) were used to monitor bread ageing. The addition of PF in the AFB formula significantly increased the time of tearing at 0 day. For all treatments, the NIRS results showed high R2‐values between the actual storage days and NIRS predictions. The NIRS and texture analysis are valuable tools to detect the effect of the preservatives on AFB shelf‐life and quality.

Near Infrared Reflectance Spectroscopy as a Tool to Predict Pesticide Sorption in Soil

We used near infrared reflectance spectroscopy (NIR) combined with chemometric methods to predict the sorption of lindane and linuron to 27 agricultural soils from Sweden. The NIR predictions were calibrated by principal component analysis (PCA) of the NIR spectra and sorption data obtained using the batch equilibrium technique. Partial least square regression PLSR of the NIR data could predict 85% of the soil sorption (K (d)) for lindane and 84% for linuron, respectively. The results indicate that NIR can be used as a rapid and simple method to predict pesticide sorption to and leaching from soils.

Effective rumen degradation of dry matter, crude protein and neutral detergent fibre in forage determined by near infrared reflectance spectroscopy

The objective of the present study was to examine if near infrared reflectance spectroscopy (NIRS) could be used to predict degradation parameters and effective degradation from scans of original forage samples. Degradability of dry matter (DM), crude protein (CP) and neutral detergent fibre (NDF) of 61 samples of perennial ryegrass (Lolium perenne L.) and orchardgrass (Dactylis glomerata L.) was tested by using the in situ technique. The grass samples were harvested at three different stages, early vegetative growth, early reproductive growth and late reproductive growth. Degradability was described in terms of immediately rumen soluble fraction (a fraction, for DM and CP only as NDF does not contain a soluble fraction), the degradable but not soluble faction (b fraction) and the rate of degradation of the b fraction (c value). Overall effective degradability of DM, CP and NDF was also calculated. Near infrared reflectance spectroscopy was examined for its ability to predict degradation parameters and to make a direct prediction of effective degradation from scans of the original samples of perennial ryegrass and orchardgrass. Prediction of effective degradation of the different feed fractions showed different accuracy. The coefficients of determination (R(2)) from regressions of predicted vs. measured effective degradation, using a cross-validation method, were 0.92 for DM, 0.78 for CP and 0.61 for NDF. The attempt to predict the degradation parameters (a, b and c) by NIRS was less successful as the coefficients of determination for the degradation parameters were low. Concentrations of CP and NDF in the original samples were predicted by using NIRS and the validated R(2) value was 0.98 for CP and 0.92 for NDF. It is concluded that using NIRS predictions from scans of original samples is a promising method to obtain values for the effective degradation of DM, CP and NDF in ruminant feeds, but that larger calibration sets are necessary for obtaining improved accuracy.

Effects of breed, sex, and age on the variation and ability of fecal near-infrared reflectance spectra to predict the composition of goat diets1,2

The effects of breed, sex, and age of goats on fecal near-infrared reflectance spectroscopy-predicted percentage juniper in the diet were investigated, as were spectral differences in feces from goats differing in estimated genetic merit for juniper consumption. Eleven goats from each breed, sex, and age combination, representing 2 breeds (Angora and meat-type), 3 sex classifications (female, intact male, and castrated male), and 2 age categories [adult and kid (less than 12 mo of age)] were fed complete, pelleted rations containing 0 or 14% juniper. After 7 d on the same diet, fecal samples were collected for 3 d, and the spectra from the 3 replicate samples were averaged. Fecal samples were assigned to calibration or validation data sets. In a second experiment, Angora and meat goats with high or low estimated genetic merit for juniper consumption were fed the same diet to determine the effect of consumer group on fecal spectra. Feces were scanned in the 1,100- to 2,500-nm range with a scanning reflectance monochromator. Fecal spectra were analyzed for the difference in spectral characteristics and for differences in predicted juniper in the diet using internal and independent calibration equations. Internal calibration had a high precision (R(2) = 0.94), but the precision of independent validations (r(2) = 0.56) was low. Spectral differences were affected by diet, sex, breed, and age (P < 0.04). However, diet was the largest source of variation in spectral differences. Predicted percentage of juniper in the diet also showed that diet was the largest source of variation, accounting for 95% of the variation in predictions from internal calibrations and 51% of the variation in independent validations. Predictions from independent calibrations readily detected differences (P < 0.001) in the percentage of juniper in the 2 diets, and the predicted differences were similar to the actual differences. Predicted juniper in the diet was also affected by sex. Feces from goats from different juniper consumer groups fed a common diet were spectrally different, and the difference may have resulted from a greater intake by high- compared with low-juniper-consuming goats. Fecal near-infrared reflectance spectroscopy predictions of botanical composition of diets should be considered an interval scale of measurement.

Prediction of in situ degradation characteristics of neutral detergent fibre (aNDF) in temperate grasses and red clover using near-infrared reflectance spectroscopy (NIRS)

Near-infrared reflectance spectroscopy (NIRS) was used to estimate in situ neutral detergent fibre (aNDF) degradation characteristics. Dried or ensiled samples of perennial grasses and red clover ( n = 382) were used to develop calibration equations. In situ residues of aNDF after 144 h of ruminal incubation were assumed to represent the total indigestible aNDF fraction (aINDF). The fractional degradation rate of aNDF (kdNDF) was calculated assuming first order degradation kinetics with an initial lag time. The aNDF content ranged from 266 to 703 g/kg DM. The aINDF values ranged from 14 to 360 g/kg DM, and the kdNDF varied from 0.020 to 0.137/h. Spectral data was transformed to their second order derivates and a standard normal variate and detrending procedure was used. NIRS prediction were made by modified partial least square technique and prediction accuracy was tested after separating the samples into a validation (close to 70 samples) and calibration dataset. Establishment of new NIRS calibrations and successive predictions of new validation samples were repeated four times. Accuracy of NIRS predictions was evaluated by the standard error of prediction, corrected for bias (SEPC), and by the standard coefficient of determination in prediction ( R P 2 ). Dispersion in sample reference values (square root of variance) divided by SEPC (RPD) was also calculated and a value >2.5 was used as indicator of acceptable prediction. For aINDF, SEPC was 17.7 g/kg DM, R p 2 was 0.92 and RPD 3.4. For kdNDF, SEPC was 0.012/h, R p 2 was 0.51, and the RPD value was 1.7. For the initial lag time, SEPC was 1.27 h, R p 2 was 0.02 and RPD was 1.0. From this study it is concluded that the developed calibration equations had an acceptable prediction of aINDF, and can be used for predicting aINDF for use in practical feed evaluation. The NIRS prediction of kdNDF was less accurate because of weaknesses with the reference method. The initial lag time could not be predicted by NIRS.

Predicting Oleic and Linoleic Acid Content of Single Peanut Seeds using Near‐Infrared Reflectance Spectroscopy

One objective of peanut breeders is to develop cultivars with elevated oleic acid content (&gt;740 g kg−1). Testing single seeds for oleic acid content is possible using gas chromatography (GC), but it is time‐consuming and requires cutting a portion of the seed which could reduce germination. Using single, intact peanut seeds, we developed a near‐infrared reflectance spectroscopy (NIR) calibration equation relating oleic acid measured with GC to oleic acid predicted by NIR. The slope of the regression line of oleic acid measured with GC on oleic acid predicted by NIR was 1.01 g kg−1 (P &gt; t &lt; .0001) and the intercept was not different from zero. An independent set of 95 peanut seeds was used to validate the NIR calibration. The slope of the regression line was 1.01 g kg−1 (P &gt; t &lt; .0001) and the intercept was not different from zero. By selecting seeds with at least 700 g kg−1 NIR predicted oleic acid content, only four of the 43 seeds (validation set) with elevated oleic acid content were misclassified by NIR, and none with normal oleic acid content were misidentified. Results were similar for linoleic acid content. This research shows that NIR prediction of oleic acid and linoleic acid using intact peanut seeds is accurate and rapid and should be especially useful for early generation screening.

Utility of Near-Infrared Reflectance Spectroscopy to Predict Nutrient Composition and In Vitro Digestibility of Total Mixed Rations

Total mixed ration (TMR) samples (n = 110) were analyzed for dry matter (DM), crude protein (CP), soluble CP, neutral detergent fiber (NDF), NDF CP, starch, ash, fat, total ethanol-soluble carbohydrate, and nonfiber carbohydrate (NFC). Rapidly and slowly degraded and undegraded in situ CP fractions and in vitro DM, organic matter, and NDF digestibility were determined on each TMR. The TMR were scanned using near-infrared reflectance spectroscopy (NIRS); spectra were retained with NIRS calibration and cross-validation statistics were determined using partial least squares regression methods. The CP, NDF, starch, in vitro DM, and in vitro indigestible NDF contents of TMR were predicted by NIRS with good degrees (R2 >0.85) of accuracy with proportionally low standard errors of prediction. Moderate utility of NIRS to predict the NFC (R2 = 0.83) and fat content (R2 = 0.81) of TMR was observed. Rapidly, slowly, and undegraded in situ CP fractions in TMR were not well predicted by NIRS. Similarly, soluble CP, NDF CP, total ethanol-soluble carbohydrate, and in vitro NDF digestibility (% of NDF) were not well predicted by NIRS. Ratios of nutrient range to reference laboratory method error were calculated and found to be positively related (R2 = 0.84) to NIRS predictability of a given TMR nutrient, suggesting some laboratory procedures were not precise enough to yield suitable NIRS predictions. Data suggest that NIRS has utility to predict basic nutrients such as CP, NDF, starch, NFC, and fat in TMR. However, difficulty was observed using NIRS in predicting key biological nutrients in TMR such as in situ CP fractions and in vitro NDF digestibility. Difficulty of NIRS in predicting these nutrients is related to the level of reference method error in relationship to the range of nutrient values in TMR, but other sources of prediction error may exist.

Quantifying species composition in root mixtures using two methods: near‐infrared reflectance spectroscopy and plant wax markers

Understanding of plant interactions is greatly limited by our ability to identify and quantify roots belonging to different species. We proposed and compared two methods for estimating the root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial root mixtures composed of two or three herbaceous species were prepared. The proportion of root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and actual root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.

Faecal near infrared reflectance spectroscopy to predict diet quality for sheep

Faecal near infrared reflectance spectroscopy (NIRS) is a non-invasive method to determine diet quality in herbivores, but has not been reported for sheep ( Ovis aries) in US. A diet reference chemistry:faecal near infrared spectra calibration ( n = 78) was developed to determine if faecal NIRS can predict diet quality of forage-fed sheep. In 2002 ( n = 15) and 2003 ( n = 20) mature ewes (55 ± 2.4 kg) were fed individual diets for 7 days. Diets ranged from 4.3 to 23.5% crude protein (CP) and 52.4 to 75.8% digestible organic matter (DOM) and were composed of various grass, forb and browse components. Daily intake was recorded. Faecal samples were collected on days 6 and 7. CP was determined by micro-Kjeldahl and DOM by an in vivo corrected in sacco technique. Partial least squares (PLS) and stepwise regression (SWR) techniques were used to develop predictive equations. Calibration results for percent dietary CP were: SWR, R 2 = 0.93, SE calibration (SEC) = 1.27 and PLS, R 2 = 0.95, SEC = 1.08. Calibration results for DOM were: SWR, R 2 = 0.78, SEC = 1.58 and PLS, R 2 = 0.80, SEC = 1.51. Equation validation was accomplished by cross validation, predicting an independent validation set, and by predicting day 7 samples with a day 6 derived equation within this study. Validation results indicate acceptable predictive ability. To determine the effect of individual animal variation on faecal NIRS predictions, five ewes were fed different forages in two, 7-day trials. Predicted percent CP and DOM from both trials indicate minimal effect on NIR predicted diet quality due to individual animal variation. Diet quality of forage-fed sheep can be accomplished by faecal NIRS.

Uso de la Espectroscopía de Reflectancia en el Infrarrojo Cercano (nirs) para la cuantificación rápida y precisa de triptófano en maíces colombianos

En este estudio se desarrolló una ecuación de calibración mediante Espectroscopía de Reflectancia en el Infrarrojo Cercano (Near Infrared Reflectance Spectroscopy, NIRS) para predecir el contenido del aminoácido triptófano en maíces colombianos. El método de cuantificación directa de la concentración de triptófano en el grano de maíz fue por HPLC y la detección UV a 280 nm. Se escanearon 60 matrices de maíz colombiano mediante NIRS, de las cuales 40 espectros se destinaron para calibración y 20 para validación, generándose una ecuación de predicción con un Error Estándar de Calibración (SEC) de 0,001 y un Coeficiente de Correlación (RSQ) de 0,979. Los datos de concentración de triptófano en las matrices de maíz obtenidos por el méto­do directo (HPLC) usados para la calibración se encontraron en el rango de 0,033 a 0,083% y las muestras de validación en el rango de 0,044 a 0,062%. El RSQval (fracción de la varianza explicada de muestras independientes de validación o coeficiente de correlación al r cuadrado) obtenido, fue bajo debido a la poca variabilidad de las muestras y al número de muestras. Sin embargo, de acuerdo con la prueba del Ji-cuadrado, los valores de predicción por NIRS se ajustaron a los valores obtenidos por HPLC con un nivel de confianza del 95%.

Energy evaluation of extruded compound foods for dogs by near‐infrared spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was used to predict the chemical composition, apparent digestibility and digestible nutrients and energy content of commercial extruded compound foods for dogs. Fifty-six foods of known chemical composition and in vivo apparent digestibility were analysed overall and 51 foods were used to predict gross energy digestibility and digestible energy content. Modified partial least square calibration models were developed for organic matter (OM), crude protein (CP), ether extract (EE), crude fibre (CF), nitrogen free extracts (NFE) and gross energy (GE) content, the apparent digestibility (OMD, CPD, EED, NFED and GED) and the digestible nutrient and energy content (DOM, DCP, DEE, DNFE and DE) of foods. The calibration equations obtained were evaluated by the standard error and the determination coefficient of cross-validation. The cross-validation coefficients of determination (R) were 0.61, 0.99, 0.91, 0.96, 0.94 and 0.92 for OM, CP, EE, CF, NFE and GE, the corresponding standard error of cross-validation (SECV) being 5.80, 3.51, 13.35, 3.64 and 16.95 g/kg dry matter (DM) and 0.29 MJ/kg DM respectively. The prediction of apparent digestibility was slightly less accurate, but NIRS prediction of digestible nutrient (g/kg DM) and DE (MJ/kg DM) gave satisfactory results, with high R (0.93, 0.97, 0.93, 0.83 and 0.93 for DOM, DCP, DEE, DNFE and DE respectively) and relatively low SECV (11.55, 6.85, 12.14 and 22.98 g/kg DM and 0.47 MJ/kg DM). It is concluded that the precision of NIRS in predicting the energy value of compound extruded foods for dogs is similar or better than by proximate analysis, as well as being faster and more accurate.