01826naa a2200277 a 450000100080000000500110000800800410001902400540006010000200011424501540013426000090028830000120029752009830030965300220129265300210131465300150133570000150135070000200136570000180138570000180140370000180142170000240143970000170146370000130148077300550149321370332022-11-24       2021    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1021/acsagscitech.1c000672DOI1 aOLIVEIRA, I. C.  aFast and accurate discrimination of Brachiaria brizantha (A.Rich.) stapf seeds by molecular spectroscopy and machine learning.h[electronic resource]  c2021  a443?448  a: Brachiaria brizantha is the most common forage plant used in cattle pastures. The field plant population is the main parameter for cattle nutrition; it is mainly determined by the genotype and physiological quality of the seed, that is, seed vigor. Seed vigor standard tests are considered time-consuming and laborious. Fourier transformed infrared (FTIR) spectroscopy was used to classify the seed cultivar and vigor from two different genotypes of B. brizantha, namely, Marandu and Paiaguas cultivars. Two ́ batches from each group were used for classification by applying FTIR and machine learning algorithms. The algorithms with a higher overall accuracy in the leave-one-out cross-validation were also validated by an external validation using a dedicated set of samples exclusively separated for this purpose. The results indicate that molecular spectroscopy combined with machine learning analysis presents great potential for the classification of B. brizantha seeds.  aFTIR spectroscopy  aMachine learning  aSeed vigor1 aFRANCA, T.1 aNICOLODELLI, G.1 aMORAIS, C. P.1 aMARANGONI, B.1 aBACCHETTA, G.1 aMILORI, D. M. B. P.1 aALVES, C. Z.1 aCENA, C.  tACS Agricultural Science & Technologygv. 1, 2021.