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All Journal Livestock and Animal Research
M. Adhyatma
Department of Animal Science, Politeknik Negeri Jember, Jember, 68101, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Veterinary

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Near infrared spectroscopy for the quality control of rice bran Suci Wulandari; M. Adhyatma; Dadik Pantaya; Anuraga Jayanegara; Rizki Amalia Nurfitriani
Livestock and Animal Research Vol 20, No 2 (2022): Livestock and Animal Research
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (632.583 KB) | DOI: 10.20961/lar.v20i2.54353

Abstract

Objective: The objective of this study was to examine the accuracy of estimating the nutrient content in rice bran as a feed ingredient by using Near Infrared (NIR) technology compared to proximate analysis method.Methods: Rice bran was used as feed sample to compare the results of data analysis using these two methods. In this study the Fourier Transform Near-infrared used infrared with wavelength of 12500-4000 cm-1. The OPUS 7.8 software was integrated with NIRS. The model was made by using Partial Least Square regression analysis to correlate the result of the spectrum and the result of proximate analysis method.Results: In this study, the nutrient content in rice bran available in markets, which commonly are Crude Protein (CP), ether extract (EE), and Crude Fiber (CF), are quite varied and wide in range, so that The curve prediction/true showed up wider and did not cover the entire surface of the line. In consequence two outliers were removed so that the accuracy value could be increased in measuring the quality of animal feed as indicated by the improvement of the coefficient of determination or R2, Root Mean Square Error for Cross Validation (RMSECV), and Standard Error (SE).Conclusions. NIR is a useful tool to estimate nutrient composition of rice bran available in the market especially for CP, EE, and CF by removing two outliers. The Prediction / True curve does not widen after removing two outliers, and can improve R2, RMSECV, and SE values.
Co-Authors Anuraga Jayanegara DADIK PANTAYA Rizki Amalia Nurfitriani Suci Wulandari