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Erschienen in:
Study on the Theory and Practice of Data Visualization Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Effect of Calibration Set Selection on Quantitatively Determining Test Weight of Maize by Near-Infrared Spectroscopy

verfasst von : Lianping Jia, Peng Jiao, Junning Zhang, Zhen Zeng, Xunpeng Jiang

Erschienen in: Computer and Computing Technologies in Agriculture X

Verlag: Springer International Publishing

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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.

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Literatur
1.
China Feed Industry Association: China Feed Industry Yearbook (2015). (in Chinese)
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)
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)
4.
Zhang, X.: Discussion on measuring result error of corn bulk density. Grain Distrib. Technol. 2, 27–28 (2005). (in Chinese)
5.
Perez-Marin, D., Fearn, T., Guerrero, J.E., Garrido-Varo, A.: A methodology based on NIR-microscopy for the detection of animal protein by-products. Talanta 80(1), 48–53 (2009)CrossRef
6.
Chen, L.J., Yang, Z.L., Han, L.J.: A review on the use of near-infrared spectroscopy for analyzing feed protein materials. Appl. Spectrosc. Rev. 48(7), 509–522 (2013)CrossRef
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)
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)
9.
Siska, J., Hurburgh, C.T.: Corn density measurement by near-infrared transmittance. Trans. ASAE 38(6), 1821–1824 (1995)CrossRef
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)
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)
12.
Kenard, R.W., Stone, L.: Computer aided design of experiments. Technometrics 11, 137–148 (1969)CrossRef
13.
Pérez-Marı́n, D.C., Garrido-Varo, A., Guerrero-Ginel, J.E., Gómez-Cabrera, A.: Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feeding stuffs: chemical composition and open-declaration. Anim. Feed Sci. Technol. 116(3–4), 333-349 (2004)CrossRef
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)
15.
Yang, Z.L., Han, L.J., Li, Q.F., Fan, X.: Use of discriminant analysis on NIRS to detect meat-and-bone meal content in ruminant concentrates. J. Anim. Feed. Sci. 16, 442–447 (2007)CrossRef
16.
Malley, D.F., Mcclure, C., Martin, P.D.: Compositional analysis of cattle manure during composting using a field-portable near-infrared spectrometer. Commun. Soil Sci. Plant Anal. 36(4), 455–475 (2005)CrossRef
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)
Metadaten
Titel
Effect of Calibration Set Selection on Quantitatively Determining Test Weight of Maize by Near-Infrared Spectroscopy
verfasst von
Lianping Jia
Peng Jiao
Junning Zhang
Zhen Zeng
Xunpeng Jiang
Copyright-Jahr
2019
Buch
Computer and Computing Technologies in Agriculture X

Print ISBN: 978-3-030-06154-8

Electronic ISBN: 978-3-030-06155-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-06155-5_49