Analysis and Prediction of Whole-Plant Corn Raw Material in Beijing of Cornell Net Carbohydrate and Protein System Components by Near Infrared Reflectance Spectroscopy

LIU Na, TU Yan, DIAO Qiyu, GUO Jiangpeng, QI Zhiguo, SI Bingwen, WANG Jun, WU Wancheng, CHEN Guoshun

doi:10.3969/j.issn.1006-267x.2019.05.035

2019 , Vol. 31 >Issue 5: 2287 - 2295

DOI: https://doi.org/10.3969/j.issn.1006-267x.2019.05.035

Molecular Nutrition

Analysis and Prediction of Whole-Plant Corn Raw Material in Beijing of Cornell Net Carbohydrate and Protein System Components by Near Infrared Reflectance Spectroscopy

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1. Gansu Agricultural University, Lanzhou 730070, China;
2. Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Feed Research Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China;
3. Beijing Municipal Animal Husbandry Station, Beijing 100107, China

Received date: 2018-10-15

Online published: 2019-05-15

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Abstract

This study aimed to establish a database of nutrient components of whole-plant corn raw material in Beijing based on the Cornell net carbohydrate and protein system (CNCPS), and found nutrient value prediction models using the method of near infrared reflectance spectroscopy (NIRS). A total of 89 whole-plant corn raw material samples were collected from 18 dairy farms in Beijing, and the nutrient components were determined, then the carbohydrate (CHO) and protein components were calculated by CNCPS 6.5. The calibration set and verification set were based on a 4:1 ratio, and NIRS models were evaluated using 71 and 18 samples of whole-plant corn raw material as calibration and validation database, respectively. The results showed as follows: 1) the conventional nutrient components of whole-plant corn raw material, protein composition and CHO composition in CNCPS system could be quite accurately estimated by NIRS technology. 2) The cross validation determinant coefficients (1-VR) were >0.8, and the verification decision coefficients (RSQv) were ≥ 0.84 for the model parameters of dry matter (DM), crude ash (Ash), crude protein (CP), ether extract (EE), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), starch (Starch), neutral detergent insoluble protein (NDIP), acid detergent insoluble protein (ADIP), soluble protein (SP), CHO, non fiber carbohydrates (NFC), soluble fiber (CB2), digestible fiber (CB3), indigestible fiber (CC), soluble true protein (PA2), insoluble true protein (PB1), fiber conjugated protein (PB2) and undegradable protein (PC), which suggested that these models could be used for rapidly actual analysis. Model parameters of DM, Ash, EE, NDF, ADF, ADL, Starch, NDIP, CHO, NFC, CB2, CB3, PC and PB1 were processed by second derivative, and CP, SP, ADIP, CC, PA2 and PB2 were processed by standard normal variate+second derivative. In conclusion, the study provide chemical analysis data of hole-plant corn raw material, and establish models for prediction of main nutrient components by NIRS technology. It is beneficial to the evaluation of the quality of whole-plant corn raw material before silage in farms.

Key words： whole-plant corn raw material; CNCPS; NIRS; nutrient value

Cite this article

LIU Na, TU Yan, DIAO Qiyu, GUO Jiangpeng, QI Zhiguo, SI Bingwen, WANG Jun, WU Wancheng, CHEN Guoshun . Analysis and Prediction of Whole-Plant Corn Raw Material in Beijing of Cornell Net Carbohydrate and Protein System Components by Near Infrared Reflectance Spectroscopy[J]. Chinese Journal of Animal Nutrition, 2019 , 31(5) : 2287 -2295 . DOI: 10.3969/j.issn.1006-267x.2019.05.035

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