This work describes a small, low-cost electronic nose device which can detect harmful substances that can harm human health, such as flammable gas like acetone, ethanol, butane as well as methane, among others. An artificial olfactory instrument consists of a set of metal oxide semiconductor sensors as well as a computer-based communications channel for signal gathering, proceeding, and presentation. We used three sensors instead of six, and the results were plotted as a variance, score as well as loading plot with crossvalidation. For gas identification, we use artificial neural network (ANN) and compare them to parallel factor analysis. Electronic nose (e-nose) has provided numerous benefits in a variety of logical study disciplines. Our goal is to create a sensor exhibit framework that can discriminate the most exceedingly contaminated gases while also being extremely responsive, precise, and less power consuming. Thus, for gas detection, we employ an ANN as well as make a comparison of results with parallel factor analysis (PARAFAC).

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The electronic nose (e-nose) is demonstrated in this research for detecting and identifying several forms of hazardous gases. We describe an e-noses for detecting several gases, including butane, acetone, methane, and ethanol. For dimensionality reduction in 3D representation, data processing approaches are based on the partial least square (PLS) method. The suggested system can be utilised for sensor optimization since different sensors with varied operating temperatures can be tested in many devices to find the best array for a specific detection or application. The results reveal that, depending on the sensor array characteristics, varying success rates in classification can be attained when discriminating contaminants. The preceding criteria lead to a new search for a portable, dependable, low-cost, and most efficient gas sensor. The major purpose of this study is to create a gas sensor array that can detect and monitor toxic and poisonous gases in the environment, as well as warn against dangerous organic compounds. Our goal is to create a sensor system that can distinguish the most significant decontamination gases while also being highly responsive, precise, low-effort, and low-power demanding.

Background: A process capacity study is a study that uses capability indices to compare the output of an in-control process to specification constraints. Cp, Cpk, Ppk, and Pp are some of the statistics that can be used to assess process capability. The goal of the six-sigma study of levetiracetam tablets is to enhance and adjust the process to produce defect-free tablets and to maximize customer satisfaction. Process capability guarantees that procedures meet industry firm specifications while lowering process variation, which is essential for obtaining product quality characteristics. Before the batch is commercialized, this capacity research should be used in the industry. It is a cost-effective strategy that can shorten the inspection process. Materials and Methods: The control and capabilities of Antiepileptic tablets are described in this study. The research focuses on data collecting and analysis of process capability utilizing statistical software such as MINITAB 19.1. Results: The results of process capabilities for all the processes like hardness test, thickness test, disintegration test, loss on drying and friability tests were found to be above 1.33. Conclusion: The existing capability of the process is judged to be capable in this research.

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