Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir

IF 1.6 4区 化学 Q3 CHEMISTRY, APPLIED
A. Oluk, Hatice Yucel, Feyza D Bilgin, U. Serbester
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引用次数: 0

Abstract

Dallisgrass (Paspalum dilatatum Poir.) is an economically important and widely cultivated forage crop for livestock feeding in the tropical, subtropical, and warm temperate regions because of good adaptation to unsuitable pasture conditions. In this study, 216 dallisgrass samples were used to develop near infrared reflectance calibrations to estimate five forage quality parameters: dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF) and ash. Second derivative pretreatment was applied for calibration of DM, CP and NDF while a first derivative pretreatment was used for ADF and ash. The coefficients of determination in the internal validation set (r 2 ) were 0.78 for DM, 0.80 for CP, 0.95 for NDF 0.75 for ADF, and 0.71 for ash. The relative predictive determinant ratios for calibration indicate that the NDF equations were acceptable for quantitative prediction of dallisgrass quality, whereas the DM, CP, ADF, and ash equations were useful for screening purposes. The near infrared prediction models developed in this study can be used for screening in the forage breeding researches to be carried out for five quality parameters in the future.
用近红外反射光谱法评价大尾草、雀稗、茯苓等牧草品质
牧草(Paspalum dilatatum Poir.)是热带、亚热带和暖温带地区广泛种植的重要经济饲料作物,对不适宜的牧草条件具有良好的适应性。本研究以216个大尾草样品为研究对象,建立了近红外反射校准方法,对干物质(DM)、粗蛋白质(CP)、中性洗涤纤维(NDF)、酸性洗涤纤维(ADF)和灰分5个牧草品质参数进行了估算。DM、CP和NDF采用二阶导数预处理,ADF和灰分采用一阶导数预处理。内验证集(r 2)的决定系数分别为DM 0.78、CP 0.80、NDF 0.95、ADF 0.75和灰分0.71。校准的相对预测决定比表明,NDF方程可用于定量预测草质量,而DM、CP、ADF和灰分方程可用于筛选目的。本研究建立的近红外预测模型可用于今后饲草育种研究中对5个品质参数的筛选。
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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
35
审稿时长
6 months
期刊介绍: JNIRS — Journal of Near Infrared Spectroscopy is a peer reviewed journal, publishing original research papers, short communications, review articles and letters concerned with near infrared spectroscopy and technology, its application, new instrumentation and the use of chemometric and data handling techniques within NIR.
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