Abstract:
Ecofeed is an animal feed made from food, beverage and agricultural by-products and that is
expected to reduce feed processing cost, increase feed self-sufficiency in Japan and urge effective
utilization of unused resources. However, ecofeed has some disadvantages like unstable
chemical composition because of high water contents in the feed, variable supply of the
materials, and so on. Therefore, rapid determination of the nutritive compositions is needed for
managing the feed and materials quality, and calculating feed formulation. Therefore, near
infrared spectroscopy (NIRS) is focused as a rapid analysis method of nutritive value of
feedstuffs. In this study, NIRS calibration models for predicting feed nutritive compositions,
crude protein (CP), ether extract (EE), crude ash (CA), crude fiber (CF) and nitrogen free extract
(NFE) in ecofeed and the materials collected in Okinawa were developed. In this study, 121
samples (ecofeed and the materials) were collected from industrial waste collector in Okinawa.
Then, 100 samples were used for creating calibration models (calibration sets) and 21 samples
(validation sets) were for evaluate the models. Collected samples were oven dried at 65˚Cfor 48h
and powdered finely. The feed nutritive compositions in CP, EE, CA CF and NFE were analyzed
by using general methods. NIRS scanning was done by NIRS instrument (InfraXact, Foss AB,
Hillerød) and the absorbance was measured in 2nm increments from 570 nm to 1848 nm. After
scanning, the spectrum data were differentiated twice and the calibration models developed by
multiple linear regression (MLR) and partial least squares regression (PLSR). Moreover, created
models were evaluated by coefficients of determination of calibration (R2C), standard deviation
of calibration (SEC), coefficients of determination of validation(R2V), standard deviation of
prediction (SEP) and ratio of performance to deviation (RPD; Standard deviation (SD) of
analyzed value in validation sets.SEP-1). All PLSR calibration models were better than MLR
models. R2C, SEC, R2V, SEP and RPD values of PLSR model were 0.94, 2.52, 0.98, 2.02 and 6.83
for CP, 0.93, 2.29, 0.98, 1.14 and 7.82 for EE, 0.59, 2.61, 0.32, 1.63 and 0.98 for CA, 0.81, 1.82,
0.75, 1.79 and 1.90for CF, and 0.95, 4.07, 0.94, 3.98 and 4.16 for NFE, respectively. Williams
(2001) suggested that calibration model with RPD > 3.1 is suitable for screening and model with
RPD > 6.5 is suitable for process control. Considering the above result, CP, EE and NFE
calibration models could be developed with high accuracy by PLSR.
