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Academic literature on the topic 'Endosperm morphology'
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Journal articles on the topic "Endosperm morphology"
DeBono, Allan G., and John S. Greenwood. "Characterization of programmed cell death in the endosperm cells of tomato seed: two distinct death programs." Canadian Journal of Botany 84, no. 5 (May 2006): 791–804. http://dx.doi.org/10.1139/b06-034.
Full textShapter, F. M., R. J. Henry, and L. S. Lee. "Endosperm and starch granule morphology in wild cereal relatives." Plant Genetic Resources: Characterization and Utilization 6, no. 02 (May 14, 2008): 85–97. http://dx.doi.org/10.1017/s1479262108986512.
Full textSaccomanno, Benedetta, Alan H. Chambers, Alec Hayes, Ian Mackay, Simon C. McWilliam, and Kay Trafford. "Starch granule morphology in oat endosperm." Journal of Cereal Science 73 (January 2017): 46–54. http://dx.doi.org/10.1016/j.jcs.2016.10.011.
Full textOliveira, Jonathas Henrique Georg, and Adelita Aparecida Sartori Paoli. "ANÁLISES ONTOGENÉTICAS EM SEMENTES DE EUPHORBIACEAE." FLORESTA 44, no. 2 (November 1, 2013): 165. http://dx.doi.org/10.5380/rf.v44i2.32472.
Full textSilva, Vanessa Neumann, Silvio Moure Cicero, and Mark Bennett. "Relationship between eggplant seed morphology and germination." Revista Brasileira de Sementes 34, no. 4 (2012): 597–604. http://dx.doi.org/10.1590/s0101-31222012000400010.
Full textLi, Chun-Yan, Wei-Hua Li, Byron Lee, André Laroche, Lian-Pu Cao, and Zhen-Xiang Lu. "Morphological characterization of triticale starch granules during endosperm development and seed germination." Canadian Journal of Plant Science 91, no. 1 (January 2011): 57–67. http://dx.doi.org/10.4141/cjps10039.
Full textYates, I. E., and Darrell Sparks. "Morphology of Postpollination Fruit Abortion in Pecan." Journal of the American Society for Horticultural Science 120, no. 3 (May 1995): 446–53. http://dx.doi.org/10.21273/jashs.120.3.446.
Full textG, Binderya, and Tumenjargal D. "The seed morphology and anatomy of the allium anisopodium on the seed genebank." Mongolian Journal of Agricultural Sciences 22, no. 03 (May 9, 2018): 69–71. http://dx.doi.org/10.5564/mjas.v22i03.956.
Full textBosnes, M., E. Harris, L. Aigeltinger, and O. A. Olsen. "Morphology and ultrastructure of 11 barley shrunken endosperm mutants." Theoretical and Applied Genetics 74, no. 2 (June 1987): 177–87. http://dx.doi.org/10.1007/bf00289966.
Full textZhao, Can, Wenrong Xu, Lingchao Meng, Sheng Qiang, Weimin Dai, Zheng Zhang, and Xiaoling Song. "Rapid endosperm development promotes early maturity in weedy rice (Oryza sativa f. spontanea)." Weed Science 68, no. 2 (March 2020): 168–78. http://dx.doi.org/10.1017/wsc.2020.5.
Full textDissertations / Theses on the topic "Endosperm morphology"
Verhoeven, Tamara M. O. "Determination of the morphology of starch granules in cereal endosperm." Thesis, University of East Anglia, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268549.
Full textRahman, Sayedur. "Canopy architecture, carbon gain and grain properties of native Australian rices: effects of elevated atmospheric carbon dioxide." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27809.
Full textGuelpa, Anina. "Maize endosperm texture characterisation using the rapid visco analyser (RVA), X-ray micro-computed tomography (μCT) and micro-near infrared (microNIR) spectroscopy." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96967.
Full textENGLISH ABSTRACT: Maize kernels consists of two types of endosperm, a harder vitreous endosperm and a softer floury endosperm, and the ratio of the vitreous and floury endosperm present mainly determines the hardness of the kernel. Maize (Zea mays L.) is a staple food in many countries, including South Africa, and is industrially processed into maize meal using dry-milling. For optimal yield and higher quality products, hard kernels are favoured by the milling industry. Despite many maize hardness methods available, a standardised method is still lacking, furthermore, no dedicated maize milling quality method exists. Using an industrial guideline (chop percentage), a sample set of different maize hybrids was ranked based on milling performance. Unsupervised inspection (using principal component analysis (PCA) and Spearman’s rank correlation coefficients) identified seven conventional methods (hectoliter mass (HLM), hundred kernel mass (HKM), protein content, particle size index (PSI c/f), percentage vitreous endosperm (%VE) as determined using near infrared (NIR) hyperspectral imaging (HSI) and NIR absorbance at 2230 nm (NIR @ 2230 nm)) as being important descriptors of maize milling quality. Additionally, Rapid Visco Analyser (RVA) viscograms were used for building prediction models, using locally weighted partial least squares (LW-PLS). Hardness properties were predicted in the same order or better than the laboratory error of the reference method, irrespective of RVA profile being used. Classification of hard and soft maize hybrids was achieved, based on density measurements as determined using an X-ray micro-computed tomography (µCT) density calibration constructed from polymers with known densities. Receiver operating classification (ROC) curve threshold values of 1.48 g.cm-3 , 1.67 g.cm-3 and 1.30 g.cm-3 were determined for the entire kernel (EKD), vitreous (VED) and floury endosperm densities (FED), respectively at a maximum of 100% sensitivity and specificity. Classification based on milling quality of maize hybrids, using X-ray µCT derived density and volume measurements obtained from low resolution (80 µm) µCT scans, were achieved with good classification accuracies. For EKD and vitreous-to-floury endosperm ratio (V:F) measurements, 93% and 92% accurate classifications were respectively obtained, using ROC curve. Furthermore, it was established that milling quality could not be described without the inclusion of density measurements (using PCA and Spearman’s rank correlation coefficients). X-ray µCT derived density measurements (EKD) were used as reference values to build NIR spectroscopy prediction models. NIR spectra were acquired using a miniature NIR spectrophotometer, i.e. a microNIR with a wavelength range of 908 – 1680 nm. Prediction statistics for EKD for the larger sample set (where each kernel was scanned both germ-up and germ-down) was: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 and for the smaller sample set (where each kernel was scanned only germ-down): R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. The results from the larger sample set indicated that reasonable predictions can be made at the fast NIR scan rate that would be suitable for breeders as a rough screening method.
AFRIKAANSE OPSOMMING: Mieliepitte bestaan uit twee tipes endosperm, ‘n harder glasagtige endosperm en ‘n sagter melerige endosperm, en die verhouding waarin die twee tipes endosperm aangetref word, bepaal hoofsaaklik die hardheid van die pit. Mielies (Zea mays L.) is ‘n stapelvoedsel in baie lande, insluitende Suid-Afrika, en word industrieël geprosesseer na mieliemeel deur van droë-vermaling gebruik te maak. Vir optimale produksie en beter kwaliteit produkte, word harde pitte deur die meule verkies. Ongeag die beskikbaarheid van verskeie mielie hardheid metodes, ontbreek ‘n gestandardiseerde metode nog, en verder bestaan ‘n metode om mielies se maalprestasie te bepaal ook nie. ‘n Monsterstel, bestaande uit verskillende mieliebasters, is op grond van maalprestasie ingedeel deur van ‘n industriële riglyn (chop persentasie) gebruik te maak. Inspeksie sonder toesig (deur gebruik te maak van hoofkomponentanalise (HKA) en Spearman’s rangkorrelasiekoëffisiënte) het sewe onkonvensionele metodes (hektoliter massa, honderd pit massa, protein inhoud, partikel grootte indeks, persentasie glasagtige endosperm soos bepaal deur gebruik te maak van naby-infrarooi (NIR) hiperspektrale beelding en NIR absorbansie by 2230 nm) identifiseer as belangrike beskrywers van maalprestasie. Daarbenewens, is Rapid Visco Analyser (RVA) viskogramme gebruik om voorspellingsmodelle te bou deur gebruik te maak van plaaslik geweegte gedeeltelike kleinstekwadrate (PG-GKK) wat hardheidseienskappe kon voorspel met laer, of in dieselfde orde, laboratorium foute van die verwysingsmetodes, ongeag die gebruik van verskillende RVA profiele. Klassifikasie tussen harde en sagte mieliebasters was moontlik, gebasseer op digtheidsmetings soos bepaal met ‘n X-staal mikro-berekende tomografie (µBT) digtheids kalibrasie gebou vanaf polimere met bekende digthede. Ontvanger bedryf kenmerkende (OBK) kurwe drempelwaardes van 1.48 g.cm-3 , 1.67 g.cm-3 en 1.30 g.cm-3 is bepaal vir hele pit, glasagtige en melerige endosperm digthede, onderskeidelik, teen ‘n maksimum van 100% sensitiwiteit en spesifisiteit. Klassifikasie van die mieliebasters, gebasseer op maalprestasie en deur gebruik te maak van X-straal µBT afgeleide digtheid en volume metings soos verkry teen lae resolusie (80 µm) skanderings, was moontlik met goeie klassifikasie akkuraatheid. Vir heel pit digtheid en glasagtigtot-melerige endosperm verhouding metings is 93% en 92% akkurate klassifikasies verkry wanneer OBK kurwes gebruik is. Verder is dit vasgestel (deur gebruik te maak van HKA en Spearman’s rangkorrelasiekoëffisiënte) dat digtheidsmetings ingesluit moet word vir ‘n volledige beskrywing van maalprestasie. X-straal µBT afgeleide digtheid metings is gebruik as verwysings waardes om NIR spektroskopie voorspellings modelle te bou. NIR spektra is verkry deur van ‘n miniatuur NIR spektrofotometer, naamlik ‘n microNIR, bebruik te maak vanaf 908 – 1680 nm. Voorspellings statestiek vir die groter monsterstel (waar elke pit beide kiem-bo en kiem-onder geskandeer is) was vir HPD: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 en vir die kleiner monsterstel (waar elke pit was slegs kiem-onder geskandeer is) vir HPD: R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. Die resultate van die groter monsterstel het aangedui dat redelike voorspellings moontlik is, teen die vinnige NIR skaderings tempo wat as rowwe vertoningsmetode geskik sal wees vir telers.