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.

Mathematical Model Coupled to Neural Networks Calculates the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic Matrix.

Unknown extraction recovery from solid matrix samples leads to meaningless chemical analysis results. It cannot always be determined, and it depends on the complexity of the matrix and properties of the extracted substances. This paper combines a mathematical model with the machine learning method-neural networks that predict liquid extraction recovery from solid matrices. The prediction of the three-stage extraction recovery of polycyclic aromatic hydrocarbons from a wooden railway sleeper matrix is demonstrated. Calculation of the extraction recovery requires the extract's volume to be measured and the polycyclic aromatic hydrocarbons' concentration to be determined for each stage. These data are used to calculate the input values for a neural network model. Lowest mean-squared error (0.014) and smallest retraining relative standard deviation (20.7%) were achieved with the neural network setup 6:5:5:4:1 (six inputs, three hidden layers with five, five, and four neurons in a layer, and one output). To train such a neural network, it took less than 8000 steps-less than a second--using an average-performance laptop. The relative standard deviation of the extraction recovery predictions ranged between 1.13 and 5.15%. The three-stage recovery of the extracted dry sample showed 104% of three different polycyclic aromatic hydrocarbons. The extracted wet sample recovery was 71, 98, and 55% for phenanthrene, anthracene, and pyrene, respectively. This method is applicable in the environmental, food processing, pharmaceutical, biochemical, biotechnology, and space research areas where extraction should be performed autonomously without human interference.