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Browsing by Author "Reid, John F."

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    Aflatoxin in corn drying
    Reid, John F. (Virginia Polytechnic Institute and State University, 1982)
    Relationships defining conditions conducive for aflatoxin production by Aspergillus f!avus were coupled with a non-equilibrium corn drying model and used to determine constant drying conditions indicating a potential for aflatoxin development. Airflow rates of 0.8 to 16 m³/min/m³ in 18, 20, 22% initial wet-basis moisture content corn were simulated in a drying model at temperatures from 12.7 to 40.6°C and relative humidities from 5 to 95%. The potential for aflatoxin development was expressed in terms of the critical relative humidities at a given temperature and airflow rate. All relative humidities simulated above the critical relative humidity also indicated a potential for aflatoxin development. Typical drying conditions for high airflow, low temperature drying systems found in Virginia were simulated to identify potential aflatoxin problems. A sensitivity analysis evaluated the importance of temperature, time, and relative humidity on the drying conditions indicating a potential for aflatoxin development. The critical relative humidities for aflatoxin development reached high levels when drying was simulated at high airflow rates and/or low initial corn moisture contents. The results of the sensitivity analysis performed indicated that relatively small changes in the criteria for potential aflatoxin development significantly affected the critical relative humidities. The time criterion for initial aflatoxin development used in the model exhibited the greatest sensitivity. Drying data from drying tests performed in November, 1981 were used to validate the corn drying model.
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    Computer simulation of hourly dry-bulb temperatures
    Kline, D. Earl; Reid, John F.; Woeste, Frank E. (Virginia Agricultural Experiment Station, 1982)
    A computer model of hourly dry-bulb temperatures was developed for Blacksburg, Virgi nia, from a 9-year sample of hourly dry-bulb temperature data. The periodic variations over the course of a year were estimated by least-square approximation. A first order Markov chain model was used to simulate the stochastic nature of temperature. These two models were combined to simulate years of hourly dry-bulb temperatures.