119759 20251204145008.0 doi 10.12706/itea.2022.001 sideral 128851 ART-2022-128851 spa Vigo, Alba Universidad de Zaragoza (orcid)0000-0002-5569-0219 Transmission and reflectance near-infrared spectroscopy for predicting the chemical composition of ground and grain cereals 2022 Access copy available to the general public Unrestricted 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. info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 0.4 2022 AGRONOMY 83 / 88 = 0.943 2022 Q4 T3 AGRICULTURE, DAIRY & ANIMAL SCIENCE 57 / 62 = 0.919 2022 Q4 T3 0.179 2022 Economics, Econometrics and Finance (miscellaneous) 2022 Q3 Veterinary (miscellaneous) 2022 Q3 Horticulture 2022 Q4 Agronomy and Crop Science 2022 Q4 0.9 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Latorre, Maria Angeles Universidad de Zaragoza (orcid)0000-0002-3005-2675 Ripoll, Guillermo 5011 500 Universidad de Zaragoza Dpto. CC.Agrar.y Medio Natural Area Ingeniería Agroforestal 2008 700 Universidad de Zaragoza Dpto. Produc.Animal Cienc.Ali. Área Producción Animal 118, 4 (2022), 565-579 Inf. téc. econ. agrar. ITEA Informacion Tecnica Economica Agraria 1699-6887 677671 http://zaguan.unizar.es/record/119759/files/texto_completo.pdf Versión publicada 2175406 http://zaguan.unizar.es/record/119759/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:119759 articulos driver 2025-12-04-14:45:27 ARTICLE