Near Infrared Spectroscopy, Prediction, Chemical Composition, Sample Preparation, Particle Size


The near infrared reflectance spectroscopy (NIRS) technique is a rapid and non-destructive technique used to evaluate the chemical composition of complete feed and ingredients. The accuracy of its prediction relies upon calibration standards to account for variations in material composition and particle shape and size. The purpose of this study was to determine the effect of alternative ingredient inclusion and corn particle size along with sample preparation method on the accuracy of the NIRS technique using standard calibrations provided with the instrument. Treatments were arranged as a 4 × 3 × 3 factorial with diet type (soybean meal (SBM) + DDGS (SD); SBM + fish meal + DDGS (SFD); SBM + fish meal + wheat bran (SFB); and SBM + wheat bran (SB)); corn particle size (400, 600, and 800 μm); and method of analysis (laboratory, NIRS-ground, and NIRS-unground). All samples were evaluated for crude protein (CP) content. Laboratory values from wet chemistry analyses were obtained using the Dumas Combustion method for comparison to results from the NIRS. Ground and unground samples for NIRS were scanned on a Foss NIRS D2500 machine with a wavelength range of 400 to 2,500 nm at a reflectance of log (1/R) at 2 nm intervals for each sample. There was no diet × particle size × method interaction on CP; however, there was an interaction (P ≤ 0.05) between diet and method of analysis. When analyzing diets using laboratory methods there were no differences in CP, but when using the NIRS, grinding samples prior to NIRS analysis improved the results compared to not grinding, though they were still lower than laboratory analysis. There was also an interaction (P ≤ 0.05) between corn particle size and method of analysis. The CP content of NIRS-ground and laboratory samples were similar within the methods used, and values obtained for the different particle sizes were closer to the expected CP (20%) as compared to the NIRS-unground samples. Results from NIRS-unground samples of diets were significantly different and lower than results from laboratory analysis. However, results from the NIRS-ground samples were intermediate between NIRS-unground and laboratory analysis. Results of this trial indicate the necessity for proper calibration biasing to improve the prediction accuracy of NIRS, especially when diets contain alternative ingredients. Grinding the sample prior to scanning with the NIRS will improve accuracy, though values may still differ from laboratory methods when using standard equipment calibrations, further emphasizing the importance of calibration biasing.

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This work is licensed under a Creative Commons Attribution 4.0 License.