Relevant bibliographies by topics / Corn – Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Corn – Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Corn – Analysis"

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Tsuji, Shojiro. "Analysis on pasting properties of corn and waxy corn starches." NIPPON SHOKUHIN KOGYO GAKKAISHI 35, no. 11 (1988): 748–54. http://dx.doi.org/10.3136/nskkk1962.35.11_748.

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Bucchini, Luca, and Lynn R. Goldman. "Starlink corn: a risk analysis." Environmental Health Perspectives 110, no. 1 (January 2002): 5–13. http://dx.doi.org/10.1289/ehp.021105.

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Tong, Jin, Bai Gong Zeng, Dong Hui Chen, Long Zhe Quan, and Shu Jun Zhang. "Finite Element Modal Analysis of the Frame of Corn Stubble-Collector." Advanced Materials Research 430-432 (January 2012): 1072–75. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1072.

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In this paper, one kind of agriculture machines with picking and separating soil was designed to collect corn stubble. The core component of the machine is the frame of corn stubble-collector. The 3-D solid model of the frame of corn stubble-collector was designed using Pro/Engineer software. The first 10 natural frequencies and vibration modes of the frame were obtained by means of the finite element modal analysis in the ANSYS-Workbench software, and analyze the dynamic characteristics of the frame, which can be used to provide some theory basis for design and optimization of the frame.
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Drennan, N., and J. Munroe. "Corn Syrup Analysis Check Service (CSACS)." Journal of the American Society of Brewing Chemists 44, no. 3 (June 1986): 145–46. http://dx.doi.org/10.1094/asbcj-44-0145b.

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Hodgson, John. "Doubts linger over Mexican corn analysis." Nature Biotechnology 20, no. 1 (January 2002): 3–4. http://dx.doi.org/10.1038/nbt0102-3.

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Pengelly, Jasper J. L., Scott Kwasny, Soumi Bala, John R. Evans, Elena V. Voznesenskaya, Nuria K. Koteyeva, Gerald E. Edwards, Robert T. Furbank, and Susanne von Caemmerer. "Functional Analysis of Corn Husk Photosynthesis." Plant Physiology 156, no. 2 (April 21, 2011): 503–13. http://dx.doi.org/10.1104/pp.111.176495.

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Johri, M. M., and E. H. Coe. "Clonal analysis of corn plant development." Genetica 97, no. 3 (May 1996): 291–303. http://dx.doi.org/10.1007/bf00055315.

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Zhang, Beili. "Corn Products International (CPI) Financial analysis." E3S Web of Conferences 275 (2021): 01021. http://dx.doi.org/10.1051/e3sconf/202127501021.

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Corporation financial analysis is crucial for accounting research, including background and environment, revenue and profit, different kinds of financial ratios. This paper will analyze Corn Products International, a corn refining and ingredient companies.
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Ma, Chengye, Yuyan Fan, Shuhua Wu, Zhehao Zhang, and Dongliang Zhang. "Analysis of the Complex Index of Extruded Corn Starch and Degermed Corn." Journal of Food Research 6, no. 6 (October 29, 2017): 56. http://dx.doi.org/10.5539/jfr.v6n6p56.

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Commercial corn starch or degermed corn contains lipids and protein, and starch-lipid (or protein) complexes were formed during extrusion. The formation of starch and lipid (or protein) complexes was investigated using the complex index (CI) and differential scanning calorimetry (DSC) analysis. The CI of extrudates of a commercial corn starch/germ mixture (or gluten meal) showed that starch was complexed with lipid or protein, thus decreasing the iodine-binding capacity of amylose. The CI increased as the content of germ or gluten meal blending starch increased. Blends containing degermed corn and thermostable or mesophilic α-amylase were extruded. The CI of extrudates was higher than 55%; however, the starch-lipid complex was not stable and could be separated. The DSC analysis of the blending starch extrudate and palmitic acid showed that the enthalpy of the starch-palmitic acid complex was increased with increasing fatty acid content. Increased complex formation required more DSC heating, resulting in an enthalpy change of the endothermic peak rise, with the peak temperature higher than 100℃.
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Ou, Longwen, Tristan R. Brown, Rajeeva Thilakaratne, Guiping Hu, and Robert C. Brown. "Techno-economic analysis of co-located corn grain and corn stover ethanol plants." Biofuels, Bioproducts and Biorefining 8, no. 3 (February 24, 2014): 412–22. http://dx.doi.org/10.1002/bbb.1475.

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Dissertations / Theses on the topic "Corn – Analysis"

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Glover, Max A. "Diallel analysis of Chinese and U.S. maize germplasm /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418022.

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Richbourg, Henry L. "QTL analysis for genes conferring tolerance to drought stress and damage from UV-B radiation." View electronic thesis, 2008. http://dl.uncw.edu/etd/2008-1/r1/richbourgh/henryrichbourg.pdf.

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Flint-Garcia, Sherry A. "Genetic analysis of stalk strength in maize /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3036825.

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Cooper, Jennifer L. "Gene expression analysis of Sucrose synthase1 and Shrunken1 in euploid and aneuploid maize /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025614.

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Buckler, Edward St Clair. "Maize domestication and molecular evolution /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9842511.

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Thelen, Jay J. "Purification, characterization and molecular analysis of the mitochondrial pyruvate dehydrogenase complex from maize /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9901296.

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Mitchell, Brook D. "Turning corn silage analysis into economic value for the beef industry." Thesis, Kansas State University, 2016. http://hdl.handle.net/2097/35243.

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Master of Agribusiness
Department of Agricultural Economics
Elizabeth Yeager
The corn silage market is typically focused heavily on the dairy market and results for corn silage plots are dedicated to the dairy industry specifically to get to the overall answer of $ Milk/Acre. The beef cattle industry is more interested in pounds of beef that can be gained from corn silage. There are roughly 6.2 million acres of corn silage in the US. Within that 6 million acres, roughly half is fed to beef cattle, but the corn silage industry has not focused on this aspect of the market. Within Mycogen Seeds there has been an ever increasing demand to find a way to deliver the corn silage plot data in a useable format for beef producers. By delivering corn silage plot data to beef producers, Mycogen has aspirations of increasing it’s market share of the corn silage market in Kansas as well as across the US, while at the same time increasing the awareness of how quality of a corn silage hybrid can affect a producers operation. This thesis examines corn silage plot info sample analysis and specific feed analysis calculations. With these specific calculations, the corn silage plot will illustrate information in a form that beef producers will understand, $ Beef Produced/Acre. At the same time, this thesis will examine the industry wide concept that tonnage is the only component that is important when selecting a corn silage hybrid. Finally this thesis will examine at what point ($/bushel) in the corn grain market does it make sense to start looking at utilizing corn silage over dry rolled corn by comparing price per MegaCalorie of energy by utilizing 25 year historical corn grain prices and using the Purdue Method of determining the cost of corn silage on a per ton basis.
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Guelpa, 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.

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Thesis (PhD (Food Sc))--Stellenbosch University, 2015.
ENGLISH 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.
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Zhao, Zuo-Yu Weber David F. "Analysis of nondisjunction induced by the r-X1deficiency and the effect of nullisomy during microsporogenesis in Zea mays." Normal, Ill. Illinois State University, 1988. http://wwwlib.umi.com/cr/ilstu/fullcit?p8818723.

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Thesis (Ph. D.)--Illinois State University, 1988.
Title from title page screen, viewed September 14, 2005. Dissertation Committee: David F. Weber (chair), Herman E. Brockman, Robert W. Briggs, Alan J. Katz, Glen E. Collier. Includes bibliographical references (leaves 104-111) and abstract. Also available in print.
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Badarch, Bayarbat. "Corn Yield Frontier and Technical Efficiency Measures in the Northern United States Corn Belt: Application of Stochastic Frontier Analysis and Data Envelopment Analysis." Diss., North Dakota State University, 2020. https://hdl.handle.net/10365/31867.

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About 75% of human food in the 21st century consists of just 12 crops, though specific crops vary among nations. Modern technology has allowed development of innovative food and non-food uses for these commodities. For instance, corn (maize (Zea mays L.)) is produced for many purposes, including food, livestock feed, biofuels, fiber for clothing, etcetera. Scientists project the human population will reach 9.2 billion in next 20 years—an 18% increase from the 2020 population of 7.8 billion—resulting in increased demand for corn and other crops. Hence, farmers must increase total crop production to meet demand; however, local agricultural resource endowments such as climate, land and water availability, and soil attributes constrain production. Perhaps the quickest yield and efficiency improvements will result from farm management practices that tailor input applications to match accurate seasonal weather forecasts. Regional seasonal weather forecasts would enable farmers to optimize yields by reducing yield risk from extreme weather events, as well as from less extreme inter-annual weather variability. Improved productive efficiency is also critical to reducing environmental harms, e.g. contaminated runoff from excessive agricultural input use. The objective of this dissertation is to estimate the corn yield frontier and efficiency measures based on agricultural input management and weather. This research contributes to an enhanced understanding of how the corn yield frontier responds to inter-annual weather variations, and how it may shift with climate change. The first chapter summarizes three main topics—farm technology, climate change and weather variability, and methods for evaluating production efficiency. The second presents estimated corn yield frontiers and efficiency measures based on stochastic frontier and data envelopment analyses for nine North Dakota Agricultural Statistics Districts from 1994 to 2018. The third presents corn yield efficiency measures for five states: Minnesota, North Dakota, Nebraska, South Dakota, and Wisconsin from 1994 to 2018. The results reveal the major causes of inter-annual yield variation are variability of rainfall and temperature. Development of accurate growing-season weather forecasts is likely to result in high value-added for farmers and downstream agribusinesses. Federal, state, and private research funding in seasonal weather forecasting would probably be well invested.
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Books on the topic "Corn – Analysis"

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Dlamini, Samuel M. Economic analysis of maize production on Swazi nation land. [Mbabane?]: Ministry of Agriculture and Cooperatives, Agricultural Research Division, 1990.

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Koo, Won W. An economic analysis of corn-based ethanol production. Fargo, N.D: North Dakota State University, Center for Agricultural Policy and Trade Studies, 2008.

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Kirimi, Lilian. A farm gate-to-consumer value chain analysis of Kenya's maize marketing system. Nairobi, Kenya: Tegemeo Institute of Agricultural Policy and Development, 2011.

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Campbell, James R. MULPOC Maize Demonstration Program: Analysis of adoption of new maize production technology. Maseru, Lesotho: Agricultural Information Services, Ministry of Agriculture, Cooperatives, and Marketing, 1991.

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Shitundu, Joseph L. M. Analysis of the fertilizers' demand factors for cotton and maize production in Tanzania's big four regions, 1973-1990. [Dar es Salaam]: University of Dar es Salaam, Economic Research Bureau, 1996.

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Wehelie, Yassin Jeyte. Maize price seasonality: An analysis of monthly retail maize prices in Mogadishu from January 1979 to December 1986 (with 1987 monthly maize forecast prices). [Mogadishu]: Ministry of Agriculture, Planning Directorate, 1987.

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Nijhoff, J. J. Recent developments and future potential of Zambia's international maize trade: A brief analysis of the potential role for Zambia on the international grain market. Lusaka: Marketing Management Assistance Project, 1995.

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Haley, Stephen L. Targeting of U.S. agricultural export subsidies: A theoretical analysis. [Washington, DC]: U.S. Dept. of Agriculture, Economic Research Service, Agriculture and Trade Analysis Division, 1988.

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Haley, Stephen L. Targeting of U.S. agricultural export subsidies: A theoretical analysis. [Washington, DC]: U.S. Dept. of Agriculture, Economic Research Service, Agriculture and Trade Analysis Division, 1988.

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Haley, Stephen L. Targeting of U.S. agricultural export subsidies: A theoretical analysis. [Washington, DC]: U.S. Dept. of Agriculture, Economic Research Service, Agriculture and Trade Analysis Division, 1988.

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Book chapters on the topic "Corn – Analysis"

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Dunkel, Z., and M. Hunkár. "Dynamic Simulation of Corn Growth." In Systems Analysis and Simulation II, 229–32. New York, NY: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-8936-1_47.

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Bullock, D., and K. Moore. "Protein and Fat Determination in Corn." In Modern Methods of Plant Analysis, 181–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-01639-8_9.

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Trucksess, Mary W., and Mohamed M. Abouzied. "Evaluation and Application of Immunochemical Methods for Fumonisin B1in Corn." In Immunoassays for Residue Analysis, 358–67. Washington, DC: American Chemical Society, 1996. http://dx.doi.org/10.1021/bk-1996-0621.ch028.

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Maragos, Chris M., Glenn A. Bennett, and John L. Richard. "Analysis of Fumonisin B1 in Corn by Capillary Electrophoresis." In Fumonisins in Food, 105–12. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-1379-1_9.

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Mosheim, Roberto, and David Schimmelpfennig. "Nutrient Use and Precision Agriculture in Corn Production in the USA." In Advances in Efficiency and Productivity Analysis, 349–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47106-4_15.

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Yang, Yantao, Zhongli Zhou, and Fu Qin. "Analysis and Forecast of World Corn Market Trade and Policy." In Proceedings of Selected Articles of 2013 World Agricultural Outlook Conference, 97–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54356-2_9.

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Graeff, Simone, Wilhelm Claupein, Diedrich Steffens, and Sven Schubert. "Digital Image Analysis of Corn Leaves affected by Nutrient Deficiencies." In ASA Special Publications, 53–62. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/asaspecpub66.c5.

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Jones, J. Benton, Harold V. Eck, and Regis Voss. "Plant Analysis as an Aid in Fertilizing Corn and Grain Sorghum." In SSSA Book Series, 521–47. Madison, WI, USA: Soil Science Society of America, 2018. http://dx.doi.org/10.2136/sssabookser3.3ed.c20.

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de Souza, Aguinaldo Eduardo, João José Giardulli Junior, João Gilberto Mendes dos Reis, Ataide Pereira Cardoso Junior, Paula Ferreira da Cruz Correia, Ricardo Zandonadi Schimidt, José Benedito Sacomano, and Márcia Terra da Silva. "An Evaluation of Brazilian Ports for Corn Export Using Multicriteria Analysis." In IFIP Advances in Information and Communication Technology, 129–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30000-5_17.

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Huang, Yan, Manquan Zhao, and Hantao Liu. "Analysis of Airflow Field of Toss Device of Yellow Corn Forage Harvester." In Computer and Computing Technologies in Agriculture VII, 486–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54341-8_51.

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Conference papers on the topic "Corn – Analysis"

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Li-Jun Chen, Wen-Tao Ren, and Yong-Kui Li. "Fast location of corn images based on position features." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476114.

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Kurt A. Rosentrater. "Analysis of Factors Affecting Corn Masa Byproduct Generation." In 2004, Ottawa, Canada August 1 - 4, 2004. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2004. http://dx.doi.org/10.13031/2013.16985.

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Chunli, Wan, Shi Wen, and Chen Luyi. "Cost-benefit Analysis of Corn Production in China." In Proceedings of the 4th International Conference on Economics, Management, Law and Education (EMLE 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/emle-18.2018.33.

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Guo, Qinghua, and John C. Crittenden. "An energy analysis of polylactic acid (PLA) produced from corn grain and corn stover integrated system." In 2011 IEEE International Symposium on Sustainable Systems and Technology (ISSST). IEEE, 2011. http://dx.doi.org/10.1109/issst.2011.5936897.

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Brizgis, Lawrence J. "Image Analysis System for Measuring Mechanical Damage of Corn." In 2nd Annual Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1986. http://dx.doi.org/10.4271/861456.

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Lei, Hanwu, Shoujie Ren, James Julson, Lu Wang, Quan Bu, and Roger Ruan. "Microwave Torrefaction of Corn Stover and Tech-Economic Analysis." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50230.

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Microwave torrefaction of corn stover with particle size of 4 mm was investigated and the effects of reaction temperature and time on the yields of volatile, bio-oil and torrefied biomass were determined. The response surface analysis of the central composite design (CCD) showed that the yields of volatile, bio-oil and torrefied biomass were significantly affected by the reaction temperature and time. Three linear models were developed to predict the yields of conversion products as a function of temperature and time. A first order reaction kinetics was also developed to model the corn stover torrefaction. Ph values of torrefaction bio-oils ranged from 2.3 to 2.76 which were similar to those of bio-oils from biomass pyrolysis. GC/MS analysis for torrefaction bio-oils showed that the organic acid was about 2.16% to 12.00%. The torrefaction bio-oils also contain valuable chemical compounds such as phenols, furan derivatives and aliphatic hydrocarbons determined by a GC/MS. There are no aromatic compounds and polycyclic aromatic hydrocarbons (PAHs) detected in the torrefaction bio-oils. The torrefaction biogas was mainly consisted of ch4, c2h6, c3h8, which was about 56 wt% of the total bio-gas. The biogas can be used for chemical synthesis or electricity generation. The heating values of torrefied biomass were from 18.64–22.22 MJ/kg depending on the process conditions. The heating values of torrefied biomass were significantly greater than those of raw biomass and similar to those of coals. The energy yields of torrefied biomass from 87.03–97.87% implied that most energy was retained in the torrefied biomass. Economic analysis indicated that the biomass microwave torrefaction plant located in a farm is profitable.
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Sencaki, Dionysius B., F. L. Tiara Grace, Laju Gandharum, and Ilvi F. Cahyaningtyas. "Discrimination Analysis of Corn Species Using Field Hyperspectral Data." In 2019 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). IEEE, 2019. http://dx.doi.org/10.1109/agers48446.2019.9034340.

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"Cost Analysis of Corn Stover Harvested in the Spring." In 2014 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, 2014. http://dx.doi.org/10.13031/aim.20141895805.

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Gallagher, Paul, Guenter Schamel, and Hosein Shapouri. "Some Price and Cost Analysis for the Corn Processing Sector." In Proceedings of the 13th Annual Integrated Crop Management Conference. Iowa State University, Digital Press, 2000. http://dx.doi.org/10.31274/icm-180809-737.

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Chung, Jack C. H., M. Litt, and G. Leininger. "A computer vision system for automated corn seed purity analysis." In the third international conference. New York, New York, USA: ACM Press, 1990. http://dx.doi.org/10.1145/98784.98822.

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Reports on the topic "Corn – Analysis"

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Darr, Matthew J., Stuart J. Birrell, Ajay Shah, Keith E. Webster, and Curtis Peder Thoreson. Analysis of Corn Stover Harvesting Equipment and Corn Stover Storage Methods. Ames: Iowa State University, Digital Repository, 2010. http://dx.doi.org/10.31274/farmprogressreports-180814-1850.

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Euken, Russ. Corn Silage and Earlage Nutrient Analysis. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/ans_air-180814-554.

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Darr, Matthew J. Multi-pass Corn Stover Harvest Analysis and Storage Trials. Ames: Iowa State University, Digital Repository, 2013. http://dx.doi.org/10.31274/farmprogressreports-180814-1792.

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Arora, S., M. Wu, and M. Wang. Update of distillers grains displacement ratios for corn ethanol life-cycle analysis. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1004867.

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Dunn, J., M. Johnson, Z. Wang, M. Wang, K. Cafferty, J. Jacobson, E. Searcy, et al. Supply Chain Sustainability Analysis of Three Biofuel Pathways. Biochemical Conversion of Corn Stover to Ethanol Indirect Gasification of Southern Pine to Ethanol Pyrolysis of Hybrid Poplar to Hydrocarbon Fuels. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1149252.

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Carew, J. F., A. Aronson, D. M. Cokinos, A. Prince, and E. C. Selcow. PIUS core performance analysis. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/239293.

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Brian D. Hawkes and Richard Schultz. HTTF Core Stress Analysis. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1056030.

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Richard W. Johnson, Hiroyuki Sato, and Richard R. Schultz. CFD Analysis of Core Bypass Phenomena. Office of Scientific and Technical Information (OSTI), November 2009. http://dx.doi.org/10.2172/974775.

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Richard W. Johnson, Hiroyuki Sato, and Richard R. Schultz. CFD Analysis of Core Bypass Phenomena. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/978363.

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MARTINO, CHRISTOPHERJ. Tank 10H Saltcake Core Sample Analysis. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/825576.

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