Effect of Calibration Set Selection on Quantitatively Determining Test Weight of Maize by Near-Infrared Spectroscopy
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.
KeywordsTest weight Near-infrared Calibration set selection
This work was supported financially by the National key research and development program (Project No. 2016YFD0700204) and S&T Nova Program of Beijing (Project No. Z1511000003150116).
- 1.China Feed Industry Association: China Feed Industry Yearbook (2015). (in Chinese)Google Scholar
- 2.Wang, B.J., Yang, X.L., Qi, B.Z.: Comparison of quality safety standards of maize in China with other foreign country. Rev. China Agric. Sci. Technol. 4(5), 10–14 (2002). (in Chinese)Google Scholar
- 3.Johnston, L.J.: Use of low-test-weight corn in swine diets and the lysine/protein relationship in corn. J. Swine Health Prod. 3(4), 161–164 (1995)Google Scholar
- 4.Zhang, X.: Discussion on measuring result error of corn bulk density. Grain Distrib. Technol. 2, 27–28 (2005). (in Chinese)Google Scholar
- 7.Chu, X.L., Lu, W.Z.: Research and application progress of near infrared spectroscopy analytical technology in China in the past five years. Sepctrosc. Spectr. Anal. 34(10), 2595–2605 (2014). (in Chinese)Google Scholar
- 8.Pomeranz, Y., Hall, G.E., Czuchajowska, Z., Lai, F.S.: Test weight, hardness, and breakage susceptibility of yellow dent corn hybrids. Cereal Chem. 63(4), 349–351 (1986)Google Scholar
- 10.Li, J.T.: Study on the rapid evaluation of nutrient values of corn and wheat by near-infrared reflectance spectroscopy. China Agricultural University, Beijing (2014). (in Chinese)Google Scholar
- 11.Cai, H.Z., Bi, W.Q., Chu, J.Z., Li, H.H.: Study on the relationship between maize unit weight and the water content. J. Zhengzhou Inst. Technol. 22(3), 70–72 (2001). (in Chinese)Google Scholar
- 14.Roza-Delgado, B., Soldado, A., Garrido-Varo, A.M., Pérez-Marı́n, D., Haba, M.J., Guerrero-Ginel, J.E.: Application of near-infrared microscopy (NIRM) for the detection of meat and bone meals in animal feeds: a tool for food and feed safety. Food Chem. 105(3), 1164–1170 (2007)Google Scholar
- 17.Chu, X.L., Yuan, H.F., Lu, W.Z.: Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Prog. Chem. 4, 528–542 (2004). (in Chinese)Google Scholar