A study of vegetable oil modified QCM sensor to detect ß-pinene in Indian cardamom.

A quartz crystal microbalance (QCM) sensor was developed in this study with the vegetable oil from olive (OLV-QCM) to detect an important volatile organic compound, ß-pinene in Indian cardamom. Hydrophobic vegetable oil from olive, which contains oleic acid and omega-9, a monounsaturated fatty acid was found to be suitable for binding ß-pinene through non-covalent bonds. The fabricated QCM sensor coating was examined with the field emission scanning electron microscope (FESEM) and Fourier-transform infrared spectroscopy (FTIR) to determine its surface morphology and chemical compositions. The sensitivity, reproducibility, repeatability, and reusability were studied for the developed sensor. Notably, the sensor was observed to be highly selective towards ß-pinene as compared to the other volatile components present in cardamom. The limit of detection (LOD) and limit of quantitation (LOQ) parameters were determined as 5.57 mg L-1 and 18.57 mg L-1, respectively. Moreover, the adsorption isotherm models of the sensor were studied to validate the physical adsorption affinity towards ß-pinene applying Langmuir, Freundlich, and Langmuir-Freundlich isotherm models. The sensor showed a correlation factor of 0.99 with the peak area percentage of gas chromatography-mass spectrometry (GC-MS) analysis for ß-pinene in cardamom samples. The sensor was prepared with natural vegetable oil, unlike health hazard chemicals. In addition to this, the low-cost, easy fabrication process ensured the suitability of the sensor for practical deployment.

Substrate uptake, phosphorus repression, and effect of seed culture on glycopeptide antibiotic production: process model development and experimental validation.

Actinomycetes, the soil borne bacteria which exhibit filamentous growth, are known for their ability to produce a variety of secondary metabolites including antibiotics. Industrial scale production of such antibiotics is typically carried out in a multi-substrate medium where the product formation may experience catabolite repression by one or more of the substrates. Availability of reliable process models is a key bottleneck in optimization of such processes. Here we present a structured kinetic model to describe the growth, substrate uptake and product formation for the glycopeptide antibiotic producer strain Amycolatopsis balhimycina DSM5908. The model is based on the premise that the organism is an optimal strategist and that the various metabolic pathways are regulated via key rate limiting enzymes. Further, the model accounts for substrate inhibition and catabolite repression. The model is also able to predict key phenomena such as simultaneous uptake of glucose and glycerol but with different specific uptake rates, and inhibition of glycopeptide production by high intracellular phosphate levels. The model is successfully applied to both production and seed medium with varying compositions and hence has good predictive ability over a variety of operating conditions. The model parameters are estimated via a well-designed experimental plan. Adequacy of the proposed model was established via checking the model sensitivity to its parameters and confidence interval calculations. The model may have applications in optimizing seed transfer, medium composition, and feeding strategy for maximizing production.