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The Unified Grain Moisture Algorithm is capable of improved accuracy and allows the combination of many grain types into a single “unified calibration”. The purposes of this research were to establish processes for determining unifying parameters from the chemical and physical properties of grains. The data used in this research were obtained as part of the United States Department of Agriculture-Grain Inspection, Packers and Stockyards Administration's Annual Moisture Calibration Study. More than 5,000 grain samples were tested with a Hewlett-Packard 4291A Material/Impedance Analyzer. Temperature tests were done with a Very High Frequency prototype system at Corvinus University of Budapest. Typical chemical and physical parameters for each of the major grain types were obtained from the literature. Data were analyzed by multivariate chemometric methods. One of the most important unifying parameters (Slope) and the temperature correction coefficient were successfully modeled. The Offset and Translation unifying parameters were not modeled successfully, but these parameters can be estimated relatively easily through limited grain tests.

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Profile Analysis (TPA) test by TA-XTplus (Stable Micro System, Surrey, UK) Texture Analyzer with P/25 type stainless steel cylinder at room temperature. Test setting were as follows: compression with pre-test speed 2 mm/s, test speed 1 mm/s, post

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temperature test. Fan et al. [ 10 ] performed laboratory tests on eight square sections to study the resistance of stainless-steel box columns under high temperature. Neural network is a new method that has been applied for elaborating heat data, but it never

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