Abstract

Molybdenum, a heavy metal, is toxic to ruminants and can inhibit animal spermatogenesis. A previously identified Mo-reducing bacterium known as Serratia sp. strain MIE2 was optimized using an Artificial neural network (ANN) to predict points that can give optimum molybdenum blue production to combat molybdenum pollution in agricultural soils. ANN predicted the best optimum points occurring at pH, temperature, sucrose, ammonium sulfate, phosphate, and molybdate concentrations of 6.5 to 7.0, between 27 to 35°C, 30 to 40 g/L, 10 g/L, between 4 and 6 mM and between 10 and 20 mM, respectively, with a Mo-blue production of 14 absorbance unit as measured at 865 nm. The effect of various xenobiotics such as carbofuran, diazinon, methomyl, malathion, trichlorfon, bendiocarb, carbaryl, hexane, and butanol showed minimal inhibition to molybdenum blue production. The results indicate ANN's utility in predicting optimum production of Mo blue from this bacterium.

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