Prediction of chemical composition and neutral detergent fibre of Italian ryegrass (Lolium multiflorum lam) by near infrared spectroscopy (NIRS)

被引：8

作者：

Sandra B.Q. ^{[1
]}

Teresa A.F. ^{[1
]}

Fernando C.C. ^{[1
]}

Felipe S.M.H. ^{[1
]}

Christian L.L. ^{[2
]}

Jean R.E. ^{[3
]}

Virginia R. ^{[1
]}

Oscar E.F. ^{[1
]}

Jorge G.V. ^{[4
]}

Víctor V.M. ^{[5
]}

机构：

[1] Laboratorio de Bioquímica, Nutrición y Alimentación Animal, Facultad de Medicina Veterinaria, Universidad Nacional Mayor de San Marcos, P-Lima

[2] Reactivos para Análisis Perú SA, P-Lima

[3] Estación Experimental del Centro de Investigación IVITA-El Mantaro, Universidad Nacional Mayor de San Marcos, Huancayo

[4] Facultad de Química E Ingeniería Química, EAP Ingeniería Agroindustrial, Universidad Nacional Mayor de San Marcos, P-Lima

[5] Estación Experimental del Centro de Investigación IVITA-Maranganí, Universidad Nacional Mayor de San Marcos, Cusco

来源：

Revista de Investigaciones Veterinarias del Peru | 2017年 / 28卷 / 03期

关键词：

Calibration; Forage evaluation; Italian rye grass; Lolium multiflorum lam; Near infrared spectroscopy; Nirs; Proximate analysis;

D O I：

10.15381/rivep.v28i3.13357

中图分类号：

学科分类号：

摘要：

The aim of this study was to generate calibration equations to predict the nutritional chemical composition of the Italian rye grass (RG) (Lolium multiflorum Lam) by near infrared spectroscopy (NIRS). A total of 75 samples of RG of different harvesting weeks were collected from the IVITA Research Center in Huancayo (Peru). Spectrum capture was performed using NIRS and the chemical analysis was done for reference of the following components: crude protein (CP), ether extract (EE), total ash (CZ), crude fibre (CF) and neutral detergent fibre (NDF). A calibration and validation model by partial least squares (PLS) was developed and the correlation coefficient (R), coefficient of determination (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), ratio range with error (RER) and residual predictive deviation (RPD) were used as statistics of accuracy and precision. Proximate analysis means were: PC = 19.02%, EE = 4.53%, CZ = 12.79%, FC = 16.50% and NDF 60.98%. High values of R2 and low values of RMSEC and RMSEP were obtained for PC (0.96, 1.02, 1.19), EE (0.94, 0.29, 1.05), CZ (0.90, 0.57, 0.92) and NDF (0.90, 1.01, 1.25, respectively). The largest RER (22.34) and RPD (4.90) were obtained for EE. It is concluded that the calibration and validation equations obtained by NIRS enable optimal quantitative prediction of PC, EE, CZ and NDF in Italian rye grass (Lolium multiflorum Lam).

引用

页码：538 / 548

页数：10

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