Transmission and reflectance near-infrared spectroscopy for predicting the chemical composition of ground and grain cereals
Resumen: Analyzing the nutritional value of the raw materials is essential for a correct feedstuff formulation. This analysis has to be as accurate as possible, but at the same time, it has to be fast, inexpensive and sus- tainable. The aim of this trial was to study the aptitude of near infrared spectroscopy, by comparing transmittance with reflectance mode, to estimate the chemical composition of wheat, maize and barley in two forms of presentation (ground and whole grain samples). For this, a total of 45 samples (15 of each raw material) from different feed factories in the northeast of Spain were used. Firstly, wet processing analysis was carried out in the laboratory to determine the chemical composition of the cereals. Once the NIR spectra were collected, they were modified with several mathematical pretreatments: derivatives, smooths, standard normal variate and multiplicative scatter correction. A partial least square regression with The Unscrambler X was made with spectra and reference data, choosing the best prediction model. In general terms, the use of reflectance together the whole grain is preferable to grinded grain avoiding work and being faster and it was more indicated to prediction of fibers. Reflectance with grinded grain provided good calibrations for crude protein, and adequate calibrations for dry matter, ashes and organic matter. Both grinded and whole grain with reflectance provided accurate predictions for dry matter but reflectance with whole grain provided best calibrations for fibers and starch while transmittance was better to predict fat and organic matter. These were preliminary results but provide an insight to the possibilities and limitations of NIRS.
Idioma: Español
DOI: 10.12706/itea.2022.001
Año: 2022
Publicado en: ITEA Informacion Tecnica Economica Agraria 118, 4 (2022), 565-579
ISSN: 1699-6887

Factor impacto JCR: 0.4 (2022)
Categ. JCR: AGRONOMY rank: 83 / 88 = 0.943 (2022) - Q4 - T3
Categ. JCR: AGRICULTURE, DAIRY & ANIMAL SCIENCE rank: 57 / 62 = 0.919 (2022) - Q4 - T3

Factor impacto CITESCORE: 0.9 - Agricultural and Biological Sciences (Q4) - Veterinary (Q3) - Economics, Econometrics and Finance (Q3)

Factor impacto SCIMAGO: 0.179 - Economics, Econometrics and Finance (miscellaneous) (Q3) - Veterinary (miscellaneous) (Q3) - Horticulture (Q4) - Agronomy and Crop Science (Q4)

Tipo y forma: Article (Published version)
Área (Departamento): Área Producción Animal (Dpto. Produc.Animal Cienc.Ali.)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material.


Exportado de SIDERAL (2024-03-18-16:48:36)


Visitas y descargas

Este artículo se encuentra en las siguientes colecciones:
Articles



 Record created 2022-11-15, last modified 2024-03-19


Versión publicada:
 PDF
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)