Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents

Abstract

Tigabu M., Felton A.M. (2018). Multivariate calibration of near infrared spectra for predicting nutrient concentrations of solid moose rumen contents. Silva Fennica vol. 52 no. 1 article id 7822. 14 p. https://doi.org/10.14214/sf.7822 Highlights• Multivariate calibrations were established for predicting nutrient concentrations of solid moose rumen contents by near infrared spectroscopy (NIRS).• Crude protein, available protein and ash contents were accurately predicted.• Prediction of microbial nitrogen, ash, acid-detergent fiber, neutral detergent fiber and lignin were satisfactory.• The results demonstrate that NIRS offers quick and inexpensive procedure to quantify nutrient concentrations of solid rumen contents. AbstractThis study aimed at establishing calibrations to predict nutrient concentrations of solid moose (Alces alces L.) rumen content using near infrared spectroscopy (NIRS), as an alternative to expensive chemical analyses. NIR reflectance spectra of 148 dry pulverized samples were recorded. The scanned samples were then analyzed for crude protein, available protein, microbial nitrogen (N), ash, acid-detergent fiber (ADF), neutral detergent fiber (NDF) and lignin contents following standard chemical analysis procedures. The calibration models were derived by Orthogonal Projection to Latent Structure (OPLS) and validated using external prediction sets. The calibration models accurately predicted crude protein, available protein and ash contents (R 2 = 0.99, 0.96, and 0.92, prediction error = 0.39, 0.72 and 0.53% dry matter, respectively) while NDF (R 2 = 0.92; prediction error = 2.23% dry matter) and ADF (R 2 = 0.89; prediction error = 1.94% dry matter) were predicted with sufficient accuracy and that of microbial-N (R 2 = 0.81; prediction error = 1.25 mg yeast-RNA g -1 dry matter) and lignin (R 2 = 0.84; prediction error = 1.05% dry matter) were acceptable. The ratio of performance to deviation values were > 3.0 for crude protein and available protein, between 3.0 and 2.5 for ADF, NDF and lignin, and 2.32 for microbial-N; attesting the robustness of the calibration models. It can be concluded that NIR spectroscopy offers a quick and inexpensive procedure for prediction of nutrient concentrations of solid rumen contents in wild herbivores.

Macronutrient balancing in free‐ranging populations of moose

“…NIRS reflectance spectra were acquired with XDS Rapid Content Analyzer (FOSS NIRSystems, Inc.) from 780 to 2,498 nm at an interval of 0.5 nm. After scanning a total of 481 samples, the most representative samples were selected for chemical analyses based on scores of principal component analysis (PCA) (see Tigabu & Felton, 2018 ). In brief, samples were selected as representative (i.e., spanning the entire range of variation in concentrations of nutritional constituents) depending on the distance from the center of the data in all three principal components.…”

“…The 333 samples in question had been excluded during calibration calculations. The other constituents were predicted with slightly higher prediction error but acceptable accuracy (see Tigabu & Felton, 2018): NDF (R 2 = .92; prediction error = 2.2% dm), ADF (R 2 = .89; 1.9% dm), lignin (R 2 = .84;…”

The multidimensional nutritional niche of fungus-cultivar provisioning in free-ranging colonies of a neotropical leafcutter ant

“…Prediction models were cross-validated with seven segments and permutation tests with 500 re-calculations were used to analyze models. Root mean square error of the estimation (RMSEE) for observations in the workset, root mean square error computed from the selected cross validation round (RMSECV), and R 2 indicating the relationship between the measured and predicted samples were used to evaluate model performance ((80); Table S11).…”

Nutritional niche separation between native roe deer and the nonnative fallow deer—a test of interspecific competition

“…For the sample preparation, all rumen samples were dried at 65°C. We used Near-infrared Spectroscopy (NIRS), as per Tigabu and Felton (2018), to predict the concentrations of different nutritional components, as this technique is rapid, nondestructive and accurate (Foley et al 1998). Each pulverized rumen sample was thoroughly mixed before drawing ca.…”