Abstract
A triphenylmethane reductase derived from Citrobacter sp. KCTC 18061P was coupled with a glucose 1-dehydrogenase from Bacillus sp. ZJ to construct a cofactor self-sufficient bienzyme biocatalytic system for dye decolorization. Fed-batch experiments showed that the system is robust to maintain its activity after 15 cycles without the addition of any expensive exogenous NADH. Subsequently, three different machine learning approaches, including multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN), were employed to explore the response of decolorization efficiency to the variables of the bienzyme system. Statistical parameters of these models suggested that a three-layered ANN model with six hidden neurons was capable of predicting the dye decolorization efficiency with the best accuracy, compared with the models constructed by MLR and RF. Weights analysis of the ANN model showed that the ratio between two enzymes appeared to be the most influential factor, with a relative importance of 54.99% during the decolorization process. The modeling results confirmed that the neural networks could effectively reproduce experimental data and predict the behavior of the decolorization process, especially for complex systems containing multienzymes.
Highlights
Triphenylmethane dyes such as malachite green and crystal violet are extensively applied in the textile industry for dyeing [1] and in the aquaculture industry as antifungal agents [2]
CsTMR/BzGDH displayed the highest initial and average decolorization rate; either increase or decrease in the proportion of CsTMR caused a decrease in decolorization efficiency, indicating that CsTMR should be in proper ratio with BzGDH in the system to achieve a high dye degradation efficiency
The performance of the decolorization process was modeled by employing multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) algorithms
Summary
Triphenylmethane dyes such as malachite green and crystal violet are extensively applied in the textile industry for dyeing [1] and in the aquaculture industry as antifungal agents [2]. A variety of biotreatment methods for triphenylmethane dye removal have been developed based on microbes or enzymes [4,5,6]. KCTC 18061P (CsTMR), and is capable of catalyzing the decolorization of triphenylmethane dyes to their leuco-derivatives using NAD(P)H as cofactors [7]. On account of its high activity and considerable stability, CsTMR provides a promising alternative for the biological removal of triphenylmethane dyes. As one of the enzymes widely used for cofactor regeneration, glucose 1-dehydrogenase (GDH), which catalyzes the oxidation of β-D-glucose to produce D-glucono-1,5-lactone, converting NAD(P) to NAD(P)H concomitantly, has the advantages oIfntd. GDH can be coupled with TMR to construct a self-sufficient system for the decolorization of trriepgheenneyrlamtioentheannzeydmyeess.[10,11]. GDH can be coupled with TMR to construct a self-sufficient systAemlthforutghhe dneucmoleorroizuastiomnuoltfiternipzhyemneylmsyesttheamnse dhyaevse.
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