Oxidation of Some α-Amino Acids by Pyridinium Bromochromate in an Aquo-Acetic Acid Medium—A Kinetic and Mechanistic Study

Oxidation of -amino acids by pyridinium bromochromate (PBC) was studied in acetic acid–water mixture containing perchloric acid. The reaction rate is first order in [PBC] and inverse first order in [H⁺] and has aldehyde as a product. The results are contrary to that of Karim and Mahanti, who observed first order with [H⁺] and cyanide as the product in the oxidation of amino acids by quinolinium dichromate. Michaelis–Menten type kinetics has been observed with respect to -amino acids. The rate of reaction increases with a decrease in the polarity of solvent indicating an ion–dipole interaction in the slow step. The reactions exhibit no primary kinetic isotope effect. The activation parameters have been evaluated. The reaction mechanism involving the formation of chromate-ester between unprotonated PBC and unprotonated amino acid followed by C–C bond fission in the slow step has been suggested. The value of the Michaelis constant (substrate–oxidant complex formation constant) increases as the number of carbon atoms increases in the amino acid.     

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier–Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier–Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.