Transmission and reflectance near-infrared spectroscopy for predicting the chemical composition of ground and grain cereals

Vigo, Alba (Universidad de Zaragoza) ; Latorre, Maria Angeles (Universidad de Zaragoza) ; Ripoll, Guillermo

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: Artículo (Versión definitiva)

Área (Departamento): Area Ingeniería Agroforestal (Dpto. CC.Agrar.y Medio Natural)
Área (Departamento): Área Producción Animal (Dpto. Produc.Animal Cienc.Ali.)