Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction

Research related to Biology often utilizes machine learning models that are ultimately uninterpretable by the researcher. It would be helpful if researchers could leverage the same computing power but instead gain specific insight into decision-making to gain a deeper understanding of their domain knowledge. This paper seeks to select features and derive rules from a machine learning classification problem in biochemistry. The specific point of interest is five species of Glycyrrhiza, or Licorice, and the ability to classify them using High-Performance Thin Layer Chromatography (HPTLC) images. These images were taken using HPTLC methods under varying conditions to provide eight unique views of each species. Each view contains 24 samples with varying counts of the individual species. There are a few techniques applied for feature selection and rule extraction. The first two are based on methods recently pioneered and presented as “Binary Encoding of Random Forests” and “Rule Extraction using Sparse Encoding” (Liu 2012). In addition, an independently developed technique called “Interval Extraction and Consolidation” was applied, which was conceptualized due to the particular nature of the dataset. Altogether, these techniques used in consort with standard machine learning models could narrow a feature space from around one-thousand candidates to only ten. These ten most critical features were then used to derive a set of rules for the classification of the five species of licorice. Regarding feature selection, compared to standard model parameter optimization, the Binary Encoding of Random Forests performed similarly, if not much better, in reducing the feature space in almost all cases. Additionally, the application of Interval Extraction and Consolidation excelled in further simplifying the reduced feature space, often by another factor of five to ten. The selected features were then used for relatively simple rule extraction using decision trees, allowing for a more interpretable model

ANALYSIS OF FOOD COLORANTS USING RAMAN SPECTROSCOPY

Color is one of the most important quality attributes that affect consumers\u27 selection of food. The increasing demand of consumers for natural colorants over artificial ones has placed challenges in both product development and regulatory practices. However, current analytical solutions for food colorants are mostly limited to a sophisticated laboratory setting with tedious sample preparation procedures. Herein, this research focuses on the analytical developments toward cost-effective determination of colorant adulteration and stability analysis. The main technique explored is Raman spectroscopy, which measures the inelastic light scattering and allows one to obtain unique molecular fingerprints for specific molecules. Compared with chromatographic methods, e.g., high-performance liquid chromatography (HPLC) or gas chromatography (GC), Raman spectroscopic methods show great potential in the analysis of food colorants due to their fingerprinting capability non-destructive nature, good portability, and easy preparation protocols. In addition, the integration of gold or silver metallic nano substrate to Raman spectroscopy, known as surface-enhanced Raman spectroscopy (SERS), improves detection sensitivity tremendously. The first study was aimed to develop a fast screening and quantification method combining thin-layer chromatography (TLC) and Raman spectroscopy for saffron powder analysis, which is one of the most expensive agricultural products in the world. Sample solutions of pure saffron and spiked saffron were prepared and dropped on TLC chips for the identification of pure saffron quality as well as discrimination of spiked saffron. The result indicated that Raman spectroscopy was capable of quantifying the major coloring compound, crocin, on dried TLC sample patterns. The method achieved excellent prediction capability for crocin concentration (expressed via absorbance value at 440nm, according to ISO3632) in the range of 0 to 400 with an RMECV of 13 and an R2 of 0.99 using partial least squares (PLS) regression model. The TLC patterns between adulterants (safflower, turmeric, red 40, and yellow 5) were distinct under bright and UV lights. Their variation was demonstrated using principal components analysis (PCA). The lowest adulteration degrees that can be detected were 2.59% for red 40, 4.15% for yellow 5, 31.01% for safflower, and 41.98% for turmeric respectively, using the PLS analysis. The next study was aimed to utilize data fusion strategies to improve the classification and quantification accuracies of saffron adulteration. In the data processing protocol, the imaging data and featured Raman data were concatenated into one data matrix. The model performance of the fused data was compared against each data block. The result indicated that the classification accuracy for saffron adulterants were greatly improved with a classification accuracy of 99% among 4 different adulterants ranging from 2% to 100% (w/w) using the fused data block as compared to the imaging data (84% accuracy) or Raman data (86% accuracy) alone. The quantification performances of the developed PLS model were improved slightly using the fused data block, achieving higher R2 values with lower errors. To improve the detection capability of TLC-Raman for adulterant analysis, a mirror “stamping” TLC-SERS approach was developed. The aim of this study was to build a simple approach for sample separation (TLC) and signal enhancement (SERS) using easily available materials. Homemade silver nanoparticles were fabricated, formed to a mirror, and stamped on developed TLC patterns for Raman signal amplification. Pure saffron (1000 ppm) and spiked saffron (200 ppm red 40) solutions were used as a model system. The result indicated that both saffron colorant(crocin) and adulterated colorant (red 40) exhibited a strong characteristic peak using the mirror stamp approach, whereas there was no or little signal without the mirror application. An arising challenge was reported when food manufacturers were trying to develop products that contain natural colorants with iron fortification. In detail, the natural colorant, anthocyanins, developed unwanted blue color shifts due to iron-anthocyanin co-pigmentation. Thus, the last study was aimed to establish a quantitative model for the prediction of anthocyanin color stability in the presence of dissolved iron. Nine anthocyanin extracts obtained from various sources were purified and measured using SERS. The PCA model successfully discriminated anthocyanins from different plant sources. The stabilization index of each anthocyanin extract was measured using UV-vis spectroscopy under pH 3 and 6 with and without iron fortification (ferric sulfate) and used as input for PLS model. The PLS model demonstrated high accuracy of predicting the stability index with an RMSECV of 2.16 nm (bathochromic shift) and an R2 of 0.98 for external validations. The results from these studies demonstrated the capability of Raman or SERS conjoined with TLC techniques and multivariant analyses for natural colorant adulteration and stability analysis. The Raman/SERS spectral data obtained in the present studies provide useful references for the food colorant research. The established methods could provide useful methodologies for industry applications in regard to fast raw ingredients screening for food companies as well as quality control for natural colorant manufacturers

Gas chromatography on self assembled single walled carbon nanotubes

Carbon nanotubes (CNTs) are nano-sized carbon-based sorbents, which have high surface area, large aspect ratio, can be self-assembled and are known to be stable at high temperatures. It is therefore conceivable that separation techniques, such as, gas chromatography (GC) can benefit from their unique properties and nano-scale interactions. Self-assembly, in-contrast to packing these materials in a tube, prevents them from agglomeration and thus facilitates in retaining their nano-characteristics. In this research, novel substrates, such as, steel tubings, on a scaled-up level have been explored for the self-assembly process of CNTs, for applications such as gas chromatography, where the CNTs served as stationary phases. In the first part of this research, the self-assembly of multi-walled carbon nanotubes (MWCNTs) on the inside wall of long stainless steel tubings was studied. The CNTs were deposited by the chemical vapor deposition (CVD) using ethylene as the carbon source and the iron nanostructures in the stainless steel as the catalyst. Variation in uniformity in terms of the thickness and morphology of the deposited film and surface coverage were studied along the length of a tube by scanning electron microscopy (SEM). The effects of process conditions, such as flow rate and deposition time on the coating thickness, were studied. The catalytic effect of the iron nanostructures depended on surface conditioning of the tubing. It was found that the pretreatment temperature influenced the quality of the nanotube coating. The morphology of the CNT deposit supported the base-growth scheme and VLS (vapor—liquid—solid) growth mechanisms of CNTs. This study served as the basis for the development of CNTs in the larger scale application. Scaled up self-assembly of single-walled carbon nanotubes (SWCNTs) was studied in long tubes and finally they were used as GC columns. The strategy for selective SWCNT growth required the prevention of iron in the bulk steel from participating in the catalytic CVD process, as the presence of iron always led to MWCNT formation. Consequently, silica lined stainless steel tubings, such as, SilcosteelTM and SulfinertTM were selected. A SWCNT film with an average thickness of 300 nm was self-assembled by a unique single-step, catalytic CVD process consisting of dissolved cobalt and molybdenum salts in ethanol, where ethanol served as the precursor and cobalt and molybdenum as catalysts. Such large-scale assembly required process and catalyst optimization. A variety of organic compounds with varying polarity were separated at high resolution and the column efficiency demonstrated around 1000 theoretical plates/in, comparable to commercial GC columns. Evaluation of Van’t Hoff and Van deemter plots suggested that the CNTs followed classical chromatography behavior. Comparison of capacity factors (k’) and isosteric heats of adsorption (ΔHS) with a packed column containing a commercial sorbent (Carbopack CTM) showed comparable results. This demonstrated high capacity and strong sorbate-sorbent interactions on the SWCNT phase. Evaluation of the McReynolds constants suggested that the SWCNT was a non-polar phase. The high surface area of the SWCNT media allowed separations of gases, and at the same time, its high thermal stability (\u3e425°C) permitted separations of higher molecular weights at higher temperatures, thus extending the range of conventional chromatography on the same column

Kromatografsko profiliranje primarnih metabolita kao alat za kemotaksonomsku klasifikaciju uzoraka sjemenki bobičastog voća

Research background. Considering the importance of consumption of berry fruits with proven health-beneficial properties and difficulties in quality control of products of specific botanical and geographic origin, a fingerprint method was developed, based on advanced data analysis (pattern recognition, classification), in order to relate the variability of nutrients in the selected cultivars to primary metabolite profile. Experimental approach. Forty-five samples of genuine berry fruit cultivars (strawberry, raspberry, blackberry, black currant, blueberry, gooseberry, chokeberry, cape gooseberry and goji berry) were characterized according to chromatographic profiles of primary metabolites (sugars, lipids and fatty acids) obtained by three chromatographic techniques (high-performance thin-layer chromatography, gas chromatography coupled to mass spectrometry, and high-performance anion-exchange chromatography with pulsed amperometric detection). Results and conclusions. Comprehensive analysis allowed monitoring and identification of metabolites belonging to polar lipids, mono-, di- and triacylglycerols, free fatty acids, free sterols, sterol esters, mono- to heptasaccharides and sugar alcohols. Chemical fingerprint of berry seeds showed the uniformity of primary metabolites within each fruit species, but revealed differences depending on the botanical origin. All three chromatographic methods provided a discriminative, informative and predictive metabolomics methodology, which proved to be useful for chemotaxonomic classification. Novelty and scientific contribution. A novel methodology for the identification of bioactive compounds from primary metabolites of natural products was described. The proposed untargeted metabolite profiling approach could be used in the future as a routine method for tracing of novel bioactive compounds. The knowledge of metabolite composition obtained in this study can provide a better assessment of genotypic and phenotypic differences between berry fruit species and varieties, and could contribute to the development of new breeding programs.Pozadina istraživanja. Imajući u vidu značaj konzumiranja bobičastog voća dokazanog blagotvornog učinka na organizam, ali i teškoće u kontroli kvalitete proizvoda specifičnog botaničkog i geografskog podrijetla, u radu je predložena metodologija zasnovana na kemijskom profiliranju i naprednoj analizi podataka (prepoznavanje obrazaca i klasifikacija), koja bi se mogla koristiti za procjenu autentičnosti određenih vrsta na osnovu njihovog profila primarnih metabolita. Eksperimentalni pristup. Ukupno je okarakterizirano 45 uzoraka različitih sorata bobičastog voća (jagoda, malina, kupina, crni ribiz, borovnica, ogrozd, aronija, peruanska jagoda i goji) na osnovu kemijskih profila primarnih metabolita (šećera, lipida i masnih kiselina) dobivenih pomoću triju kromatografskih tehnika (tankoslojnom kromatografijom velike učinkovitosti, plinskom kromatografijom spregnutom s masenom spektrometrijom i ionskom kromatografijom s pulsnom amperometrijskom detekcijom). Rezultati i zaključci. Sveobuhvatnom kemijskom analizom identificirane su različite klase metabolita: polarni lipidi, mono-, di- i triacilgliceroli, slobodne masne kiseline, slobodni steroli, sterolni esteri, mono- do heptasaharidi i šećerni alkoholi. Rezultati pokazuju da uzorci koji pripadaju istoj biljnoj vrsti imaju sličan kemijski profil, a različite vrste imaju različit sastav primarnih metabolita. Sve tri kromatografske metode pružaju diskriminativnu, informativnu i prediktivnu metabolomičku metodologiju primjenjivu u kemotaksonomskoj klasifikaciji. Novina i znanstveni doprinos. Opisana je nova metodologija identifikacije bioaktivnih spojeva iz primarnih metabolita prirodnih proizvoda. Predloženi pristup neciljanog profiliranja metabolita mogao bi se koristiti kao rutinska metoda pronalaska novih bioaktivnih spojeva. Poznavanje sastava metabolita omogućuje bolju procjenu genotipskih i fenotipskih razlika između sorata bobičastog voća, što može pridonijeti razvoju novih programa oplemenjivanja

Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

<p>Abstract</p> <p>Background</p> <p>Determination of lipophilicity as a tool for predicting pharmacokinetic molecular behavior is limited by the predictive power of available experimental models of the biomembrane. There is current interest, therefore, in models that accurately simulate the biomembrane structure and function. A novel bio-device; a lipid thin film, was engineered as an alternative approach to the previous use of hydrocarbon thin films in biomembrane modeling.</p> <p>Results</p> <p>Retention behavior of four structurally diverse model compounds; 4-amino-3,5-dinitrobenzoic acid (ADBA), naproxen (NPX), nabumetone (NBT) and halofantrine (HF), representing 4 broad classes of varying molecular polarities and aqueous solubility behavior, was investigated on the lipid film, liquid paraffin, and octadecylsilane layers. Computational, thermodynamic and image analysis confirms the peculiar amphiphilic configuration of the lipid film. Effect of solute-type, layer-type and variables interactions on retention behavior was delineated by 2-way analysis of variance (ANOVA) and quantitative structure property relationships (QSPR). Validation of the lipid film was implemented by statistical correlation of a unique chromatographic metric with Log P (octanol/water) and several calculated molecular descriptors of bulk and solubility properties.</p> <p>Conclusion</p> <p>The lipid film signifies a biomimetic artificial biological interface capable of both hydrophobic and specific electrostatic interactions. It captures the hydrophilic-lipophilic balance (HLB) in the determination of lipophilicity of molecules unlike the pure hydrocarbon film of the prior art. The potentials and performance of the bio-device gives the promise of its utility as a predictive analytic tool for early-stage drug discovery science.</p

Chemometric Classification Of Herb � Orthosiphon Stamineus According To Its Geographical Origin Using Virtual Chemical Sensor Based Upon Fast GC

An analytical method using Electronic Nose (E-nose) instrument for analysis of volatile organic compound from Orthosiphon stamineus raw samples have been developed

Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire