EC Nutrition

Commercial livestock and poultry farming is one of the fastest growing and most promising industries in Bangladesh. Livestock industry depends solely on compound feeds which represents 65 - 70% of the total cost of production resulting needs proper attention to evaluate the nutritional quality of feeds quickly. The aim of this study was to develop local calibration procedures using NIRS with different wavelength ranges and validation of calibrations for the prediction of nutrients in animal feeds. Five NIRS machines having different wavelength ranges of 400 - 2500 nm (DS2500 monochromator, dispersive), 800 - 2500 nm (MPA, Matrix F and Antaris, fourier transform) and 920 - 1678 nm (VIAVI MicroNIR, portable type) were selected for the spectroscopic measurement of the animal feed samples. Multivariate analyses were performed to develop calibration equations of nutrients and data were centered using Partial Least Squares (PLS) algorithm and spectral outliers were identified from each calibration. The accuracy of the calibration models were validated by root mean square error cross validation (RMSECV), ratio of performance to deviation (RPD) and correlation coefficient (R²) between the Aunir reference and laboratory values vs predicted values of NIRS. The square error cross validation (SECV) for the evaluation of moisture (0.46 - 0.50%), protein (1.16 - 1.37%), fat (0.40 - 0.53%), fibre (0.69 - 0.75%) and ash (1.28 - 1.86%) in animal feeds by using 400 - 2500 and 800 - 2500 nm indicating higher potentiality of the models. Similarly, the RPD values (> 2.5) and R² (moisture > 0.88; protein > 0.98; fat > 0.86; fibre > 0.96 and ash > 0.85) were proved the accuracy of the model using 400 - 2500 and 800 - 2500 nm. In MicroNIR with wavelength range of 920 - 1678 nm, the SECV for the prediction of protein (2.18), fibre (1.13) and ash (2.32) were relatively higher than wavelength ranges of 400 - 2500 and 800 - 2500 nm. Besides, the RPD value of predicting fat contents by MicroNIR (920 - 1678 nm) was < 2 considered to not give a relevent prediction of the fat content in animal feeds. Therefore, the present study revealed that using NIRS with 400 - 2500 and 800 - 2500 nm wavelength could potentially used in predicting nutrient contents in animal feeds. However, the encouraging results obtained in this study by MicroNIR (920 - 1678 nm) suggest that by expanding number of samples in calibration and collection a higher reliability, quality and predictive capability of the models could be used for the prompt evaluation of animal feeds.

 Keywords: Nutrient Content; Nutrient Evaluation; Animal Feeds; NIRS; Wavelength Range

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