Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs

A 3D-printed high-hardness die steel microchip GC column: 3-meter long, low-cost, and exhibiting superior separation performance

In this work, a 3D-printed metal column engineered specifically for micro gas chromatography applications was developed, and an in-depth characterization of its performance and gas separation capabilities were conducted. A microchip gas chromatography column, with dimensions 7.0 × 7.0 × 0.2 cm, was fabricated via Direct Metal Laser Sintering (DMLS) technology using die steel powder. The column incorporates a 3-meter-long circular spiral channel, possessing an internal diameter of 500 μm, and employs OV-1 as the stationary phase. To enable efficient heating, a ceramic plate was affixed to one side of the column. The entire assembly weighs 118 g, facilitating the flexible adjustment of column length in a series configuration, thereby enhancing the analysis of complex mixtures. The column exhibited outstanding separation capabilities across mixtures encompassing ketones, aromatics, alkanes, and alcohols, demonstrating consistent repeatability. Notably, it enabled rapid temperature programming at an impressive rate of 120 °C/min within the boiling point spectrum spanning of C6 to C18, while maintaining its superior separation performance. This innovative design has achieved remarkable success in separating Benzene Toluene Ethylbenzene & Xylene (BTEX), volatile organic compounds (VOCs), and gasoline, thereby spotlighting its exceptional separation efficiency. Moreover, it offers a viable solution to the prevalent challenges commonly faced by microchip columns, including manufacturing complexity, low repeatability, and high production costs. Significantly, it stands as the longest chromatography column currently employing 3D printing technology for micro gas chromatography and provides significant insights into optimizing column length through the construction of large-curvature channels on a constrained planar substrate.

Digital Restoration of Separations Data

In experimental separations, the acquired signal can have four common but unwanted elements: (i) high-frequency noise, (ii) drift, (iii) occasional periodic and pink noise, and (iv) a mixture of components traveling with close velocities. Additionally, separation scientists observe partially resolved peaks in chromatographic or capillary electrophoresis detector output. A protocol for digitally recovering the "true data" is provided with clear-cut details and open-access codes for formulating denoising, baseline correction, and peak resolution problems as linear or nonlinear optimizations. The smoother of Bohlmann-Whittaker denoises and preserves the total area of the signal. Fourier transform methods to detect and remove periodic noise are also proposed for erratic periodic noise problems. A peak width preserving filter, called the bilateral filter, is also given. This procedure is followed by two reliable baseline correction algorithms based on asymmetric reweighted least squares or curvature-based weights to deal with low-frequency signal variations. Once low- and high-frequency noise components are removed, a convenient peak mode location method is introduced, relying upon numerically stable second derivative calculation from Savitzky-Golay (SG) filter. Peak locations, area, height, and higher statistical moments can be derived from iterative curve fitting. The protocol shows two peak models that can model left or right skewed peaks. The new peak models are (i) a numerically stable version of the bi-directional exponentially modified Gaussian (EMG), suitable for a wide variety of real chromatograms, and (ii) the twice generalized normal model with third- and fourth-moment parameters built into it. The nonlinear least squares regression approach uses the trust-region reflective optimization method. The algorithm converges to a solution even with multiple peaks and approximate guesses of peak parameters. The protocol can be generalized to fit more than 200 peak functions in separation sciences available in literature by minor adaptations of the provided codes in the MATLAB environment.

Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model

Background/Objectives: Epilepsy is a prevalent neurological disorder characterized by seizures that significantly impact individuals and their social environments. Given the unpredictable nature of epileptic seizures, developing automated epilepsy diagnosis systems is increasingly important. Epilepsy diagnosis traditionally relies on analyzing EEG signals, with recent deep learning methods gaining prominence due to their ability to bypass manual feature extraction. Methods: This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. The 35-channel EEG signals were transformed into 35-channel images using continuous wavelet transform. These images were then concatenated horizontally and vertically into a single image (seven rows by five columns) using Python's PIL library, which served as input for training the DCNN model. Results: The proposed model achieved impressive performance metrics on unseen test data: 95.99% accuracy, 94.27% sensitivity, 97.29% specificity, and 96.34% precision. Comparative analyses with previous studies and state-of-the-art models demonstrated the superior performance of the DCNN model and image concatenation technique. Conclusions: Unlike earlier works, this approach did not employ additional classifiers or feature selection algorithms. The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.

Control Software Design for a Multisensing Multicellular Microscale Gas Chromatography System

Microscale gas chromatography (μGC) systems are miniaturized instruments that typically incorporate one or several microfabricated fluidic elements; such systems are generally well suited for the automated sampling and analysis of gas-phase chemicals. Advanced μGC systems may incorporate more than 15 elements and operate these elements in different coordinated sequences to execute complex operations. In particular, the control software must manage the sampling and analysis operations of the μGC system in a time-sensitive manner; while operating multiple control loops, it must also manage error conditions, data acquisition, and user interactions when necessary. To address these challenges, this work describes the investigation of multithreaded control software and its evaluation with a representative μGC system. The μGC system is based on a progressive cellular architecture that uses multiple μGC cells to efficiently broaden the range of chemical analytes, with each cell incorporating multiple detectors. Implemented in Python language version 3.7.3 and executed by an embedded single-board computer, the control software enables the concurrent control of heaters, pumps, and valves while also gathering data from thermistors, pressure sensors, capacitive detectors, and photoionization detectors. A graphical user interface (UI) that operates on a laptop provides visualization of control parameters in real time. In experimental evaluations, the control software provided successful operation and readout for all the components, including eight sets of thermistors and heaters that form temperature feedback loops, two sets of pressure sensors and tunable gas pumps that form pressure head feedback loops, six capacitive detectors, three photoionization detectors, six valves, and an additional fixed-flow gas pump. A typical run analyzing 18 chemicals is presented. Although the operating system does not guarantee real-time operation, the relative standard deviations of the control loop timings were <0.5%. The control software successfully supported >1000 μGC runs that analyzed various chemical mixtures.