NIR Calibrations Research Articles

Predicting Chemical Composition and Apparent Total Tract Digestibility on Freeze-Dried Not Ground Faeces Using Near-Infrared Spectroscopy in Pigs

The present study aimed to compare NIRS results using freeze-dried ground or not ground (FDG or FDNG) faeces to predict faecal chemical composition and apparent total tract digestibility (ATTD) coefficients. Two different batches of pigs were used (n = 20 mixed sex pens/batch; 11 pigs/pen; Duroc × (Large White × Landrace)). The first batch of pigs (B1; 50.1 ± 3.44 kg body weight (BW)) was used at 13 wks of age and the second batch (B2; 87.0 ± 4.10 kg BW) was used at 18 wks of age. For both B1 and B2, pens were assigned to five diets formulated to obtain a control [10.03 MJ of net energy (NE), 160.0 g of crude protein (CP), and 9.5 g of standardized ileal digestive (SID) lysine (Lys) per kg of feed], low protein (132.0 g CP and 7.5 g SID Lys), high protein (188.0 g CP and 11.5 g SID Lys), low energy (9.61 MJ NE/kg), and high energy (10.45 MJ NE/kg) diets. After a 10-day adaptation period, one faecal sample was collected daily from each pen floor during 6 days in both B1 and B2 (n = 120/batch). Faecal samples were freeze-dried and analysed via NIRS as FDNG and FDG faeces. Dry matter (DM), organic matter (OM), CP, gross energy (GE), fat, and ATTD coefficients were analysed/calculated. The NIRS calibrations were evaluated by cross-validation, splitting the data in four random groups, or using the leave-one-out method. For both FDNG and FDG faeces, coefficients of determination for calibration (R2cv) and residual predictive deviation (RPD) values were: close to 0.9 and 3 for DM and CP, 0.7–0.8 and ≥2 for OM and GE, 0.6 and <2 for fat, and 0.54–0.75 and ≤2 for ATTD coefficients, respectively. CP was better predicted using FDG faeces (p < 0.05), while DM and OM ATTD were better predicted using FDNG faeces (p < 0.05). In conclusion, NIRS successfully predicts faeces’ chemical components and ATTD coefficients of nutrients using FDNG or FDG faeces.

‘Specific Normalization’ method for NIRS calibrations

In this study, we present a chemometric procedure called ‘Specific Normalization’. It upgrades the calibration for use in a specific set of new samples by normalizing together the spectra of both the calibration set and a new measured sample set. The proposed procedure is shown for near infra-red spectroscopy measurement of olive oil acidity, as an example. This procedure would need its integration as software application for routine analysis.

Estimation of the sensory properties of black tea samples using non-destructive near-infrared spectroscopy sensors

The quality characteristics of black tea are routinely assessed before it is purchased, blended and marketed to ensure its quality and value. Although some of these quality characteristics can be measured analytically, others need to be determined as sensory scores following cupping tests conducted by tea experts. However, most of these analyses (especially the sensory ones) require high training and expertise, are time-consuming and prone to human error. Therefore, in this study, non-destructive spectral sensors were combined with chemometric methods to rapidly measure the results of the cupping test (appearance, body, colour and overall quality) and some other important sensory quality attributes (bulk density, cellulose, water extract and moisture) of black tea samples. A total of 54 black tea samples from Türkiye were analysed in three different NIRS (near-infrared spectroscopy) devices (MicroNIR™ 1700, Matrix-F FT-NIR and NIRS DS 2500). Partial Least Squares Regression (PLSR) and Principal Components Regression (PCR) with stepwise variable elimination were used as regression algorithms to develop the NIR calibrations. As a result, PLSR provided slightly superior estimates with Rcv2 between 0.83 and 0.97 and RPDcv between 2.47 and 5.79 for sensory traits. For analytical traits, model statistics for PLSR ranged between 0.66-0.89 and 1.72–3.08 for Rcv2 and RPDcv, respectively. These results suggest that PLSR combined with FT-NIR technology may be promising for rapid and economical evaluation of sensory (cupping test) scores and related properties for its use in the tea industry.

372 Selecting Goats for Juniper Consumption Increases Pentobarbital-Induced Sleep Time

Abstract A sodium pentobarbital-induced sleep time (ST) study was conducted on intact males, Boer x Spanish goats (n = 30), selected for high (J+) or low juniper (J-) consumption with the objective to compare the Phase I detoxification system of the genetic divergent lines (EBV of 13.1 ± 0.2 and -14.2 ± 0.3, respectively). The ST study was conducted following a minimum period of 21 days on three different diets: grazing juniper infested rangeland (JIR), forage diet with no monoterpenes (M0), and forage diet with 8 g/kg added camphor, sabinene, and α-pinene monoterpenes in a ratio of 5:4:1 (M+). Fecal samples from goats on the JIR and M+ diets were analyzed for camphor and sabinene, and, when on the JIR diet, analyzed for percentage juniper in the diet using previously developed NIRS calibrations. Data were submitted to ANOVA analysis with repeated measures, assuming as significant when alpha was less than 10% (P < 0.10) by the F test. Percentage of juniper in the diet of J+ goats on JIP (38.6 ± 3.4%) was greater (P < 0.001) than J- goats (28.0 ± 3.8%). The J+ goats slept 15.1 (P = 0.02), 14.3 (P = 0.06), and 15.8 (P = 0.06) minutes longer than J- goats on JIR, M0, and M+ diets, respectively. There was no difference in ST between diets, and there was no interaction line × diet. Goats from the J+ line had higher concentrations of camphor in their feces on the JIR (P = 0.07) and M+ (P = 0.09) diets, while sabinene concentration was similar. Goats selected for juniper consumption had longer ST than J- goats which indicates a less efficient Phase I detoxification system, probably because there were less monoterpenes in the blood to be metabolized in the liver by the cytochromes P450 enzymes.

Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric cereal feed ingredients

Carbohydrates are the main components of cereal grain ingredients and are comprised predominantly of starch which varies in the rate and extent of digestibility, and non-starch polysaccharides (NSP) from plant cell walls. These components are traditionally measured by chemical methods but there is interest in developing more rapid, cost effective predictions by use of near infrared reflectance spectroscopy (NIRS) technology. In this study, around 400 samples of wheat, corn, barley, wheat bran, oats, rye, corn germ, and sorghum were collected over several years to develop global NIR calibrations. Chemical analysis was performed for NSP, including details on solubility and individual constituent sugars, as well as separate determinations of cellulose and lignin. Total starch content and starch digestibility were characterized by the method of Englyst as rapidly (RDS) and slowly (SDS) digestible starch, and resistant starch (RS) determined as total starch content minus RDS and SDS. The same samples were scanned (400 to 2500 nm, every 0.5 nm) by two NIRS machines: a Foss model DS2500 and a Bruker Tango FT-NIR (Fourier transform near-infrared spectroscopy). The NIRS calibrations were developed by using the combined set of different cereal grains. The modified Partial Least Squares Regression method was employed to establish correlations between the reference chemical analysis and the collected NIRS spectra. The correlation between the NIRS and reference data for total and insoluble NSP and the main component sugars (arabinose, xylose, and glucose) were robust (R2 = >0.97) with a 1-VR > 0.96 and RPD values > 5.3. Similarly, the NIRS predicted lignin (R2 = 0.94; 1-VR = 0.93) and to a lesser extent cellulose (R2 = 0.70; 1-VR = 0.98) but still highly correlated with the reference data. The predicted total starch content was excellent (R2 = 0.99; 1-VR = 0.99). The NIRS prediction for RDS and SDS were lower in linearity (R2 = 0.81 and R2 = 0.97, respectively) but more than sufficient to provide an estimate of the starch digestion profile for many of the raw materials evaluated. In addition, the robust NIRS predictions for total starch content, RDS and SDS could then provide information to calculate the RS fraction of the ingredients. In conclusion, compared with the reference chemical data for this large set of cereal grains the global NIR calibrations developed provide a good prediction of NSP, monosaccharide sugars, lignin, cellulose and total starch content in cereal grains, and for the first time an NIR relationship with starch digestibility fractions in the grains has been shown.

A NIRS-Aided Methodology to Elucidate the Nutrition of the Endangered Mountain Gazelle (Gazella gazella) Using Samples of Rumen Contents from Roadkills

The populations of the endangered mountain gazelle (Gazella gazella), which inhabit large parts of Israel, across various ecosystems and climatic conditions, shrunk drastically over the last decades. To date, data on gazelle nutrition, how these relate with individual characteristics and respond to seasonal and environmental changes, have not been available. We analyzed 110 samples from gazelle rumen contents collected throughout the country from occasional fatalities, mainly roadkills, and tested the feasibility of using them for near-infrared spectrometry (NIRS) calibrations. Although NIR calibrations for crude protein, in vitro dry matter digestibility, and ash were reasonable, we found that using calibrations based on local forage and feed plant species performed better, and used these to estimate several nutritional constituents in gazelle rumens, using NIRS. We tested how constituents relate to the sex, age-class, and weight of the individual gazelle, and to season and ecosystem type, and found that season plays a major role in gazelle nutrition. Winter is the most propitious season, when crude protein, ash and digestibility are highest, and acid detergent fiber (ADF), neutral detergent fiber (NDF), and the carbon to nitrogen (C:N) ratio are lowest. Autumn, being the harshest season, mirrors winter conditions, and summer and spring show intermediate levels. Yet the relative changes between seasons were mild: about 30%, for crude protein, digestibility, and ash, and 14–22% for ADF, NDF, and C:N ratio. Ecosystem type affected several constituents, and nutrition was slightly better in Mediterranean than in dry ecosystems. Gazelle sex, weight, and age-class had minor effects on nutrition. Overall, it seems that the adaptation of gazelles to their environment is germane to keeping relatively steady nutrition throughout the year. Our results, which do not show a dramatic decline in the quality of gazelle nutrition during any season or among the climatic regions that were studied, suggest that nutrition is not a major driver of the survival of gazelles in the populations surveyed.

PSV-15 Using near infrared reflectance spectroscopy to predict lab scoured yield in Rambouillet sheep

Abstract Lab scoured yield (LSY) is a major indicator of wool quality. LSY is used for the valuation of wool in commercial settings and can be used by growers as selection criteria for breeding stock. Current laboratory methods for LSY are costly and labor intensive. Evaluation of fleece core samples using Near-Infrared Reflectance Spectroscopy (NIR) may present an efficient, cost-effective alternative to predict LSY. Lamb and yearling fleece core samples from flocks originating from Texas were scanned on a FOSS 6500 spectrometer. Constituent data were obtained from the Bill Sims Wool and Mohair Laboratory using ASTM methodology. LSY ranged from 48–68%. Spectral data were pretreated with a 14 nm moving average and Savitsky-Golay 2nd derivative. Eight outlier spectra were removed. Samples were parsed from the center of the distribution to minimize the Dunn effect creating calibration (n = 108) and test (n = 41) sets. Calibrations were executed using a partial least squares regression on spectra from 1100 to 2492 nm. Test set calibration statistics for LSY were: r2=0.64, RMSE=3.39, and slope=0.91. Independent validation statistics for LSY using spectra for different years were: r2=0.33, RMSE=3.69, and slope=0.29. RMSE for independent validation and lab methods on side samples are similar. Between flock independent validations were less promising. Accuracy of laboratory methods for estimating yield is 2 percentage units. NIRS calibrations can be improved by developing calibration sets with a uniform distribution, which can be difficult within flocks because of the small number of fleeces in the tails of the distribution. These data demonstrate that when calibration and test sets are developed such that test samples are drawn from the calibration population, NIR is a reliable predictor of LSY. However, when test samples are drawn from populations dissimilar to the calibration set, reliability of NIR predictions are greatly reduced.

Determination of Sex in Ungulate Herbivores via near Infrared Spectroscopy of Hair: Growing Cattle as a Surrogate Model

Abstract Near infrared spectroscopy (NIRS) has been used in a variety of medical and veterinary science applications. In particular, NIRS calibrations have been developed in livestock for steroid content in cattle hair, and wound age or stage of healing in hot iron cattle brands. These NIRS applications also have potential utility in forensic science. Portable NIRS instruments facilitate measurements on live animals and or animal samples in the field. The objective of this study was to evaluate the efficacy of determining sex in growing cattle via NIRS of hair utilizing a portable spectrometer. In two consecutive years, log 1/R spectra (350–2500 nm) were collected using an ASD Field Spec fitted with a contact probe. Experimental subjects were Bos taurus cross calves (n = 12, yr 1; n = 14, yr 2) born to cows grazing central Arizona rangeland. Calf age was approximately 60, 90 and 210 d at branding, estrus synchronization, and weaning, respectively. As cattle were gathered for these routine working events, a total of 7 M and 19 F calves were scanned 3 times each over the left ribcage. A linear discriminant function was applied to spectral data in order to determine sample membership in M or F groups at each collection date. Chi-square procedures were used to determine differences (P < 0.05) in proportion of correct identifications per group and collection date. Overall, 86% of F and 72% of M were correctly (P < 0.05) identified. Corresponding values were 82% for F and 71% for M at branding, 100% for F and 89% for M at estrus synchronization, and 86% for F and 64% for M at weaning. Calf sex was successfully determined using portable NIRS in this proof of concept study. Efficacy of this method should be evaluated for different ungulate herbivores and under additional collection scenarios.

Compositional Analyses Reveal Relationships among Components of Blue Maize Grains.

One aim of this experiment was to develop NIR calibrations for 20-grain components in 143 pigmented maize samples evaluated in four locations across New Mexico during 2013 and 2014. Based on reference analysis, prediction models were developed using principal component regression (PCR) and partial least squares (PLS). The predictive ability of calibrations was generally low, with the calibrations for methionine and glycine performing best by PCR and PLS. The second aim was to explore the relationships among grain constituents. In PCA, the first three PCs explained 49.62, 22.20, and 6.92% of the total variance and tend to align with nitrogen-containing compounds (amino acids), carbon-rich compounds (starch, anthocyanin, fiber, and fat), and sulfur-containing compounds (cysteine and methionine), respectively. Correlations among traits were identified, and these relationships were illustrated by a correlation network. Some relationships among components were driven by common synthetic origins, for example, among amino acids derived from pyruvate. Similarly, anthocyanins, crude fat, and fatty acids all share malonyl CoA in their biosynthetic pathways and were correlated. In contrast, crude fiber and starch have similar biosynthetic origins but were negatively correlated, and this may have been due to their different functional roles in structure and energy storage, respectively.

Broad near-infrared spectroscopy calibrations can predict the nutritional value of >100 forage species within the Australian feedbase

Context Near-infrared reflectance spectroscopy (NIRS) is a tool that permits rapid and inexpensive prediction of the nutritional characteristics of forages consumed by ruminants. Aim Our aim was to investigate the feasibility of developing a NIRS calibration to predict the nutritional value of the majority of grasses, legumes and forbs that are utilised for sheep and cattle production in southern Australia. Methods More than 100 annual and perennial forage species were grown in replicated plots at two locations over a period of 3 years. Biomass was sampled every 3–6 weeks, dried, ground and scanned with a desktop NIRS machine (n = 4385). One-quarter of these samples were subjected to laboratory analysis for calibration development or validation. Key results Despite the large variation in the taxonomy and maturity of the plants when sampled, we successfully developed broad calibrations that predicted key nutritional traits. We achieved excellent predictions for crude protein, with a ratio of standard error of performance:standard deviation (RPD) of 5.3, and standard error of cross validation (SECV) of 1.06%. Predictions of neutral detergent fibre were also excellent (RPD 4.3, SECV. 3.5%). For pepsin–cellulase DM digestibility and acid detergent fibre, predictions were very good (RPD 3.7, SECV 2.6% and RPD 3.9, SECV 2.1%). Predictions for organic matter were less reliable (RPD 2.2). We achieved very promising predictions of methane production during batch culture fermentation (RPD 3.1, SECV 3.5 mL/gDM). Predictions of ammonia and total volatile fatty acid concentrations in the post-fermentation substrate were poor. Conclusions We found that the broad calibrations predicted the nutritional traits of annual grasses, annual legumes and forb species with greater accuracy than perennial grasses or legumes. This could be associated with the accuracy of the wet chemistry methods. As a general rule, separating taxonomically similar species into groups before the development of calibrations, did not lead to more accurate predictions. Implications If more spatial and temporal diversity can be built in without a large reduction in accuracy, these broad NIRS calibrations represent a valuable tool for Australian researchers, feed testing agents and livestock producers, as they encompass nearly all of the species that appear in monocultures or mixed swards.

Using Chemometrics and Near-Infrared Reflectance Spectroscopy for Discrimination of Poultry Meat and Prediction of Cholesterol Content

Four calibrations were made for cholesterol content in poultry meat (breasts and legs from chickens, cockerels, capons, and breasts and legs from geese). Standard uncertainties expressed as SECV (%, relative) for chickens, cockerels and capons were 9.2 for breasts and 7.8 for legs. These values for geese were 8.4 and 9.0, respectively. The discriminant method with the highest predictive ability, based on residuals RMSX Residents, was used to classify the samples. Classification accuracy values were good and ranged, on average, from 96.8% to 98%. The NIRS calibrations on cholesterol content in the breast and leg meat of chickens, capons, cockerels, as well as in the breast and leg meat of geese, are suitable for rapid routine analyses to use in practice.

Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy

The selection of samples for modelling of visible and near infrared (vis-NIR) spectra for prediction of soil organic carbon (OC) is a crucial step for improving model prediction performance. This paper aims at comparing three soil sample selection methods coupled with spiking technique for improving on-line prediction performance of OC. Sample selection methods included random selection (RS), Kennard-Stone (KS) algorithm and similarity analysis (SA). Soil vis-NIR spectra was measured with an on-line fibre-type vis-NIR spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a spectral range of 305–2200 nm. A multiple field sample set (268 samples) was merged with samples (148 samples) collected from one target field, and the resulted sample set was subjected to the three sample selection methods. After dividing spectra into calibration and prediction sets, partial least squares regression (PLSR) was run on the calibration set to develop calibration models for OC, and resulted models were validated using samples of the prediction set. Results show that SA performed generally better than its competitors, especially when there were 58 spiked samples used in the calibration set (54% of total spiked samples of 106), with the best residual prediction deviation (RPD) and root mean squares error of prediction (RMSEP) of 2.14–2.54 and 0.16–0.15% for laboratory and on-line prediction. KS and RS performed similarly, but depending on the size of the calibration set, KS produced slightly better models. This indicates that the proposed SA coupled with spiking holds great potential in the optimization of a calibration set size and may serve as a novel and efficient tool for balancing the cost and quality of vis–NIR calibrations for estimating OC.

Prediction of browse nutritive attributes in a Pinus radiata D. Don silvopastoral system based on visible-near infrared spectroscopy

Browse is an important source of food for rustic livestock, particularly when herbaceous forage is scarce. The Atlantic Basque Country (Northern Spain) forest landscape is dominated by Pinus radiata D. Don plantations where Rubus sp. and Ulex gallii are understorey dominant species. Knowledge of the nutritive value of these species is needed in the context of silvopastoralism, primarily because do not always meet livestock requirements. The objective of this study was to evaluate the potential of Visible-Near Infrared Spectroscopy to determine the quality attributes of Rubus sp. and U. gallii, using a Sample Turn Table probe to acquire spectra on non-dried samples. VIS–NIRS calibrations were developed for dry matter (DM), crude protein (CP), crude fibre (CF), neutral detergent fibre (NDF), acid detergent fibre (ADF) and ashes. Spectra were pre-treated and Partial Least Squares Regression models were constructed. Calibration models were accurate for most of the considered variables, both for Rubus sp. (DM: Rc2 = 0.95, RPD = 2.53; CP: Rc2 = 0.90, RPD = 2.39; CF: Rc2 = 0.86, RPD = 2.30; NDF: Rc2 = 0.93, RPD = 2.80; ADF: Rc2 = 0.95, RPD = 3.12; Ashes: Rc2 = 0.91, RPD = 2.15) and for U. gallii (DM: Rc2 = 0.98, RPD = 3.67; CP: Rc2 = 0.94, RPD = 1.84; CF: Rc2 = 0.98, RPD = 4.74; NDF: Rc2 = 0.94, RPD = 3.91; ADF: Rc2 = 0.98, RPD = 3.62; Ashes: Rc2 = 0.82, RPD = 1.65). In general, ADF and DM were the most accurately predictable variables and ash content, the least predictable one. The results showed VIS–NIRS potential for the rapid and accurate prediction of quality attributes in non-dried samples and proved as a useful tool for making decisions in silvopastoral systems.

New strategies for a reliable assessment of baking quality of wheat – Rethinking the current indicator protein content

New strategies for a reliable assessment of baking quality of wheat are required since the protein content is a widely discussed indicator in this respect. In this study, the indicator value of the protein content for baking quality was evaluated using almost 600 wheat samples representing different quality and varieties. NIRS calibrations were developed to estimate bread volume at different levels of processing. Bread volume increased with increasing protein content in a wide curvilinear relationship with an R2 of 0.59. At protein contents above 12% representing the relevant range for trading, the R2 dropped to 0.15. Different wheat varieties showed differential responses (i.e. linear and non-linear) in bread volume to increasing protein content. The performance of NIRS calibrations for bread volume were best at the processing level of dough (R2 0.81) and good at the level of whole grains (R2 0.70). Hence, the protein content is not a reliable indicator for baking quality on its own. Bread volume was predicted better by NIRS calibrations than by protein content. A strategy is presented paying attention to the variety specific interplay between baking quality and protein content with implications for the variety registration system, trading, breeding and nitrogen fertilisation.

Near-infrared calibration transfer for undried whole maize plant between laboratory and on-site spectrometers

The analysis of the maize plant immediately after harvest is essential in order to check the composition and maturity of the plant to optimise the quality of silage. NIRS calibrations were carried out on chopped maize using three spectrophotometers: a laboratory instrument (FOSS NIRSystems 5000 scanning monochromator, FOSS, Silver Spring, MD) and two versions of new-generation portable instruments (poliSPECNIR, PL1 and PL2). The aim was to verify the quality of the transfer of the calibration curves between FOSS, PL1 and PL2 and between PL1 and PL2, obtained by three methods of spectra processing: pre-processing, piecewise direct standardisation (PDS) and direct standardisation (DS). Seventy-six samples of chopped whole maize plant were scanned with the three instruments and were analysed by wet chemistry for dry matter (DM), ash, crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), starch and total sugars, to develop calibration equations. Two more datasets of 15 samples each were used for the standardisation of equations and validation. The calibration transfer obtained, according to the values of R2, standard error of prediction and bias, can be considered satisfactory (0.72 > R2 <0.97) for DM, ash and NDF for both poliSPECNIR, while CP and ADF have shown a good accuracy of prediction (0.78 > R2 <0.82) with PL2. Using FOSS as a master instrument, the choice of method of standardisation varies depending on the slave instrument even though the best results are obtained using PDS with PL2. The most accurate predictions are reached using PDS even when PL1 is the master.

Effect of measurement units on NIR calibrations

Effect of measurement units on NIR calibrations

Comparison of CCA and PLS to explore and model NIR data

Partial Least Squares (PLS) regression is the most widely used technique for developing NIR calibrations. PLS uses several factors to reach the optimum models which can be helpful in a physical interpretation of the sources of correlation between x and y variables. However, it suffers from later factors not arising in the order of the explained variance. Canonical Correlation Analysis (CCA) overcomes this problem by selecting the latent variables as the directions of maximum x-y correlation. Calibration of moisture, crude protein, dry gluten and resistance of dough to deformation of wheat flour samples from NIR spectra is here studied using PLS-1, PLS-2, CCA-1 and CCA-2. The calibration set contains 429 samples while 215 extra independent samples are used for the validation set. It is shown that a 2-D CCA-2 calibration model gathers the highest explained variance between the models studied. When particular calibration models of each property are compared, CCA requires regularization to avoid instability of the regression coefficients. A regularization term that tends to reduce the regression coefficients and the Durbin-Watson test or the Test for Runs to select the regularization parameter have been used. Both statistical tests led to similar values of the regularization parameter and the resulting regression coefficients and RMSEP of the CCA-1 models became similar to those of the PLS-1 models.

Prediction error and repeatability of near infrared reflectance spectroscopy applied to faeces samples in order to predict voluntary intake and digestibility of forages by ruminants

The study examined the prediction error of near infrared reflectance spectroscopy of faeces (FNIRS) in estimations of in vivo organic matter digestibility (OMD) and dry and organic matter voluntary intake (DMVI and OMVI) of forages by ruminants in established calibration datasets from our laboratories. It also examined the repeatability of the FNIRS measurements of these parameters and the proportion of grass and clover in ingested forage. The prediction error of NIRS calibrations depends on the accuracy and precision of reference data. In this study, the variability in reference data for OMD, DMVI and OMVI was less than 10% of the mean. Correction for the error in the reference method almost halved the standard error of prediction (SEP) for OMD to 0.0155. For DMVI and OMVI, the corrected SEP was 8–9g/kg BW0.75, similar to the apparent SEP. These results suggested that the FNIRS calibrations were precise enough to predict OMD adequately, but probably not DMVI or OMVI.The repeatability of FNIRS spectra and predictions assessed by repeated measurements of the same sample was satisfactory for all tested parameters. For OMD, DMVI, OMVI and the grass and clover proportion of the ingested forage, prediction repeatability was lower than, or similar to, the standard error of cross-validation (SECV) of the FNIRS calibration. The study showed that FNIRS could be a reliable method for predicting the OMD of ruminants, but the prediction of voluntary intake with acceptable error was less satisfactory because of uncertainties in the FNIRS calibration models.

Rabbit Feces as Feed for Ruminants and as an Energy Source.

Simple SummaryThis paper investigates the potential use of rabbit feces as a source of nutrition for ruminants and as an energy source. Feeding rabbit feces to ruminants or biogas production with rabbit manure may reduce the competition between human food and animal feed, provide a partial solution to some environment problems, and reduce treatment and disposal cost of manure. The in vitro rumen digestibility of rabbit feces was experimentally measured to assess its value as a feed for ruminants. In parallel, in order to extract the information about its potential nutritive and energetic value, the whole and partial relationships between the nutrient constituents of the rabbit feces and/or crop forages were investigated by chemometric analysis and also validated by near-infrared spectroscopy (NIRS). The results of this work indicated that rabbit feces has potential value as a ruminant feed and for biogas production.There are prospects for using novel feeds from various sources to provide ruminants with alternative sources of protein and energy such as by-products, and animal wastes. Rabbit feces are a concentrated source of fiber and could have commercial potential both as input biomass in anaerobic processes for biogas production, as well as a fibrous source for ruminal degradation. The aims of this work were to assess the potential as ruminant feeding and as biogas production of rabbit feces, in comparison with 12 crops. The chemical composition and the potential and experimental in vitro true digestibility (IVTD) and neutral detergent fiber digestibility (NDFD) of 148 feces samples were determined by using chemical methods, Daisy system digestibility and/or NIRS predictions. The average biomethane potential (BMP) was 286 ± 10 lCH4/kg SV with −4% vs. the crops average. Milk forage unit (milk FU), IVTD and NDFD of feces were 0.54 ± 0.06 milk FU/kg DM, 74% ± 3% and 50% ± 5%, respectively, with comparisons of −19%, −11% and −24% vs. the crops average. Reconstruction of the potential values based on the chemical constituents but using the crop partial least square model well agreed with the NIRS calibrations and cross-validation. In a global NIRS calibration of the feces and crops the relative predicted deviation for IVTD, NDFD and milk FU were 3.1, 2.9 and 2.6, respectively, and only 1.5 for BMP. Running the Daisy system for rabbit feces in rumen fluid gave some inconsistencies, weakened the functional relationships, and appeared not to be correlated with the potential values of IVTD and NDFD. Nevertheless, the energetic potential of feces appears to be similar to some conventional crops at different degrees of maturity. Thus we conclude that rabbit feces has potential value as a ruminant feed and for biogas production.

Deriving metallicities from calcium triplet spectroscopy in combination with near-infrared photometry

Context. [...] One of the most efficient methods is the measurement of the equivalent width (EW) of the Calcium II Triplet (CaT), corrected for the luminosity and temperature effects via magnitude differences from the horizontal branch (HB). Methods. We use the Ks magnitude difference between the star and the reddest part of the HB (RHB) to generate reduced equivalent widths (rEW) from the datasets presented in Saviane et al. (2012) and Rutledge et al. (1997). Subsequently we calibrated these rEW against three different metellicity (met) scales: the one presented in Carretta et al. (2009), the met values given in Harris (2010) and a version of the former corrected via high-resolution spectroscopic met. Results. We calculated the calibration relations for the two datasets and the three met scales and found that they are approximately equivalent, with differences almost negligible. We compared our nIR calibrations with the corresponding optical ones, and found them to be equivalent, establishing that the luminosity-corrected rEW using the Ks magnitude is compatible with the one obtained from the V magnitude. We then used the met obtained from the calibration to investigate the internal met distributions of the GCs. Conclusions. We have established that the ([Fe/H]:rEW) relation is independent from the mag used for the luminosity correction and find that the calibration relations only change slightly for different met scales. The CaT technique using NIR photometry is thus a powerful tool to derive met. In particular, it can be used to study the internal met spread of a GC. We confirm the presence of at least two met populations in NGC 6656 and find that several other GCs present peculiar met distributions.