Browsing by Author "Banerjee, Protibha Nath"

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    Modeling of sugarcane bagasse conversion to levulinic acid using response surface methodology (RSM), artificial neural networks (ANN), and fuzzy inference system (FIS): A comparative evaluation
    (Elsevier, 2022) Ogedjo, Marcelina; Kapoor, Ashish; Kumar, P. Senthil; Rangasamy, Gayathri; Ponnuchamy, Muthamilselvi; Rajagopal, Manjula; Banerjee, Protibha Nath
    Levulinic acid is recognized as a prominent value-added chemical that can be obtained from bioresources and has versatile industrial applications. This work reports bioprocess modeling of levulinic acid synthesis from sugarcane bagasse employing response surface methodology and artificial intelligence techniques, including artificial neural network and fuzzy inference system approaches. The influence of process parameters, namely reaction temperature, concentration of levulinic acid and reaction time on the production of levulinic acid was investigated. The levulinic acid production values predicted by empirical models were compared with experimentally acquired data. The predictive capability of various models was appraised by computing statistical indices. The artificial neural network model was determined to be the best predictive model with the highest coefficient of determination value of 0.96 and the lowest error values (root mean square error = 0.272, mean absolute error = 0.072 and mean absolute percentage deviation = 2 %). The feedforward back propagation network with 3–10-1 architecture was employed to model the production of levulinic acid. The process optimization was performed using the desirability function approach. The bagasse, on treatment under optimum conditions, with 1 M sulphuric acid at 190 °C for 15 min yielded 5.40 mg/mL levulinic acid accounting for 77.1 % process efficiency. The utilization of waste biomass sugarcane bagasse would offer a sustainable approach for the production of platform chemical levulinic acid.
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    A review of the newly identified impurity profiles in methamphetamine seizures
    (Elsevier, 2020) Onoka, Isaac; Banyika, Andrew Toyi; Banerjee, Protibha Nath; Makangara, John J.; Dujourdy, Laurence
    Forensic intelligence of synthetic illicit drugs suffers a problem of continuous introduction of new synthetic methods, modification of the existing routes of manufacture, and adulterations practiced by criminal networks. Impurity profiling has been indispensable in methamphetamine intelligence based on precursors, synthetic routes, and chemical modifications during trafficking. Law enforcement authorities maintain the credibility and integrity of intelligence information through constant monitoring of the chemical signatures in the illicit drug market. Changes in the synthetic pattern result in new impurity profiles that are important in keeping valuable intelligence information on clandestine laboratories, new synthetic routes, trafficking patterns, and geographical sources of illicit Methamphetamine. This review presents a critical analysis of the methamphetamine impurity profiles and more specifically, profiling based on impurity profiles from Leuckart, Reductive amination, Moscow, Emde, Nagai, Birch, Moscow route; a recent nitrostyrene route and stable isotope signatures. It also highlights the discrimination of ephedrine from pseudoephedrine sources and the emerging methamphetamine profiling based on stable isotopes.