000119759 001__ 119759
000119759 005__ 20240319081027.0
000119759 0247_ $$2doi$$a10.12706/itea.2022.001
000119759 0248_ $$2sideral$$a128851
000119759 037__ $$aART-2022-128851
000119759 041__ $$aspa
000119759 100__ $$aVigo, Alba
000119759 245__ $$aTransmission and reflectance near-infrared spectroscopy for predicting the chemical composition of ground and grain cereals
000119759 260__ $$c2022
000119759 5060_ $$aAccess copy available to the general public$$fUnrestricted
000119759 5203_ $$aAnalyzing 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.
000119759 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000119759 590__ $$a0.4$$b2022
000119759 592__ $$a0.179$$b2022
000119759 591__ $$aAGRONOMY$$b83 / 88 = 0.943$$c2022$$dQ4$$eT3
000119759 593__ $$aEconomics, Econometrics and Finance (miscellaneous)$$c2022$$dQ3
000119759 591__ $$aAGRICULTURE, DAIRY & ANIMAL SCIENCE$$b57 / 62 = 0.919$$c2022$$dQ4$$eT3
000119759 593__ $$aVeterinary (miscellaneous)$$c2022$$dQ3
000119759 593__ $$aHorticulture$$c2022$$dQ4
000119759 593__ $$aAgronomy and Crop Science$$c2022$$dQ4
000119759 594__ $$a0.9$$b2022
000119759 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000119759 700__ $$0(orcid)0000-0002-3005-2675$$aLatorre, Maria Angeles$$uUniversidad de Zaragoza
000119759 700__ $$aRipoll, Guillermo
000119759 7102_ $$12008$$2700$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Producción Animal
000119759 773__ $$g118, 4 (2022), 565-579$$pInf. téc. econ. agrar.$$tITEA Informacion Tecnica Economica Agraria$$x1699-6887
000119759 8564_ $$s677671$$uhttps://zaguan.unizar.es/record/119759/files/texto_completo.pdf$$yVersión publicada
000119759 8564_ $$s2175406$$uhttps://zaguan.unizar.es/record/119759/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000119759 909CO $$ooai:zaguan.unizar.es:119759$$particulos$$pdriver
000119759 951__ $$a2024-03-18-16:48:36
000119759 980__ $$aARTICLE