Abstract
To study the effect of calibration set on quantitatively determining test weight of maize by near-infrared spectroscopy, 584 maize samples were collected and scanned for near-infrared spectral data. Test weight was measured following the standard GB 1353-2009, resulting the sample test weight of 693–732 g•L−1. Two calibration models were respectively built using partial least squares regression, based on two different calibration sets. Test weight of two calibration sets distribute differently, with normal and homogeneous distributions. Both quantitative models were selected by root mean square error of cross validation (RMSECV), and evaluated by validation set. Results show the RMSECV of the model based on normal distribution calibration set is 4.28 g•L−1, the RMSECV of the model based on homogeneous distribution calibration set is 2.99 g•L−1, the predication of two models have significant difference for the samples with high or low test weight.
Highlights
Maize is a major energy ingredient for livestock feed in China, with more than 50% of maize used for feed [1]
Test weight is defined as a measurement of bulk density or the weight of a unit volume of grain
The objective of this study is to compare the difference of NIR prediction models, which are built on two distributions of maize test weight of calibration set respectively
Summary
Maize is a major energy ingredient for livestock feed in China, with more than 50% of maize used for feed [1]. To evaluate the quality of maize for feed, many standards adopt test weight as a certification and ranking indicator [2]. Test weight is defined as a measurement of bulk density or the weight of a unit volume of grain (gL-1). Maize of low-level test weight is of lower feeding value than that of normal test weight [3]. The authoritative approach of determining test weight of maize involves measuring the weight of corn cereal per standard volume. Measurements normally be taken around 5 minutes with an accuracy of ±9 gL-1 [4]. The maize utilized in Chinese feed industry is from local province, and transported from other provinces and countries. The laboratory-based method would not be feasible for achieving this uncertain variation of different country maize to safeguard product quality stabilization at high frequency and a large volume
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