Chromatographic Data Segmentation Method: A Hybrid Analytical Approach for the Investigation of Antiviral Substances in Medicinal Plant Extracts.

The methodology described in this article will significantly reduce the time required for understanding the relations between chromatographic data and bioactivity assays. The methodology is a hybrid of hypothesis-based and data-driven scientific approaches. In this work, a novel chromatographic data segmentation method is proposed, which demonstrates the capability of finding what volatile substances are responsible for antiviral and cytotoxic effects in the medicinal plant extracts. Up until now, the full potential of the separation methods has not been exploited in the life sciences. This was due to the lack of data ordering methods capable of adequately preparing the chromatographic information. Furthermore, the data analysis methods suffer from multidimensionality, requiring a large number of investigated data points. A new method is described for processing any chromatographic information into a vector. The obtained vectors of highly complex and different origin samples can be compared mathematically. The proposed method, efficient with relatively small sized data sets, does not suffer from multidimensionality. In this novel analytical approach, the samples did not need fractionation and purification, which is typically used in hypothesis-based scientific research. All investigations were performed using crude extracts possessing hundreds of phyto-substances. The antiviral properties of medicinal plant extracts were investigated using gas chromatography-mass spectrometry, antiviral tests, and proposed data analysis methods. The findings suggested that (i) ß- cis-caryophyllene, linalool, and eucalyptol possess antiviral activity, while (ii) thujones do not, and (iii) α-thujone, ß-thujone, cis- p-menthan-3-one, and estragole show cytotoxic effects.

Compensation of the baseline temperature fluctuations for autonomous CE-C4D instrument working in harsh environments.

One of the main problems of the remote complex sample analysis instrumentation is that such systems are susceptible to temperature fluctuations. Temperature regulation is energetically ineffective, and it is not used in most of the field portable analytical systems. Separations performed in a changing temperature environment provide electropherograms with considerable baseline fluctuations, resulting in significant errors in detection and integration of the peaks. This paper describes electropherogram baseline compensation that is suitable for the capillary electrophoresis-contactless conductivity detection analytical method. The baseline compensation utilizes linear or polynomial data processing methods, and can be programmed in-line using simple microcontroller, or on-line and off-line in data acquisition software. This method is targeted for field portable and autonomous analytical systems that are utilized in a fluctuating environment.

Capillary Electrophoresis Sensitivity Enhancement Based on Adaptive Moving Average Method.

In the present work, we demonstrate a novel approach to improve the sensitivity of the "out of lab" portable capillary electrophoretic measurements. Nowadays, many signal enhancement methods are (i) underused (nonoptimal), (ii) overused (distorts the data), or (iii) inapplicable in field-portable instrumentation because of a lack of computational power. The described innovative migration velocity-adaptive moving average method uses an optimal averaging window size and can be easily implemented with a microcontroller. The contactless conductivity detection was used as a model for the development of a signal processing method and the demonstration of its impact on the sensitivity. The frequency characteristics of the recorded electropherograms and peaks were clarified. Higher electrophoretic mobility analytes exhibit higher-frequency peaks, whereas lower electrophoretic mobility analytes exhibit lower-frequency peaks. On the basis of the obtained data, a migration velocity-adaptive moving average algorithm was created, adapted, and programmed into capillary electrophoresis data-processing software. Employing the developed algorithm, each data point is processed depending on a certain migration time of the analyte. Because of the implemented migration velocity-adaptive moving average method, the signal-to-noise ratio improved up to 11 times for sampling frequency of 4.6 Hz and up to 22 times for sampling frequency of 25 Hz. This paper could potentially be used as a methodological guideline for the development of new smoothing algorithms that require adaptive conditions in capillary electrophoresis and other separation methods.