000			03206nab a22004457a 4500
001			64463
003			MX-TxCIM
005			20211101203829.0
008			190214s2021 gw |||p|op||| 00| 0 eng d
022			_20040-5752
022			_21432-2242 (Online)
024	8		_ahttps://doi.org/10.1007/s00122-021-03926-8
040			_aMX-TxCIM
041			_aeng
100	1		_924476 _aBurns, M.J.
245	1	0	_aPredicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy
260			_aBerlin (Germany) : _bSpringer, _c2021.
500			_aPeer review
520			_aLack of high-throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high-throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman’s rank correlation coefficient of 0.852 between wet laboratory and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR spectra data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.
546			_aText in English
650		7	_91173 _aMaize _2AGROVOC
650		7	_91218 _aProcessing _2AGROVOC
650		7	_912723 _aMoisture content _2AGROVOC
650		7	_96179 _aInfrared spectrophotometry _2AGROVOC
650		7	_911127 _aMachine learning _2AGROVOC
700	1		_924477 _aRenk, J.S.
700	1		_924478 _aEickholt, D.P.
700	1		_924479 _aGilbert, A.M.
700	1		_924480 _aHattery, T.J.
700	1		_924481 _aHolmes, M.
700	1		_924482 _aAnderson, N.
700	1		_924483 _aWaters, A.J.
700	1		_924484 _aKalambur, S.
700	1		_916954 _aFlint-Garcia, S.A.
700	1		_924485 _aYandeau-Nelson, M.D.
700	1		_924486 _aAnnor, G.A.
700	1		_915365 _aHirsch, C.N.
773	0		_dBerlin (Germany) : Springer, 2021. _gv. 134, no. 11, p. 3743-3757 _tTheoretical and Applied Genetics _wu444762 _x0040-5752
942			_2ddc _cJA _n0
999			_c64463 _d64455