The impact of different drying methods on the antioxidant compounds in torch ginger flowers as revealed by ATR-FTIR fingerprint and multivariate data analysis

This study determined the antibacterial activity of 12 minor Zingiberaceae spices and to identify the active compounds in the most active spices using chromatographic fingerprinting and multivariate data analysis. Curcuma purpurascens had the highest antibacterial activity against Staphylococcus aureus and Escherichia coli. HPLC chromatogram and antibacterial data of hexane, chloroform, ethyl acetate, methanol, and water extracts of C. purpurascens were linked using Orthogonal Projection to Latent Structure. Retention time of 16.5–18.0 min correlated with antibacterial activity and is abundantly found in methanol fraction. UHPLC-HRMS analysis of methanol fraction showed that the peak was attributed to demethoxycurcumin.

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

Inorganic elements are important components of medicinal herbs, and provide valuable experimental evidence for the quality evaluation and control of traditional Chinese medicine (TCM). In this study, to investigate the relationship between the inorganic elemental fingerprint and geographical origin identification of cultivated Polygala tenuifolia, 41 elemental fingerprints of P. tenuifolia from four major polygala-producing regions (Shanxi, Hebei, Henan, and Shaanxi) were evaluated to determine the importance of inorganic elements to cultivated P. tenuifolia. A total of 15 elemental (B, Ca, Cl, Cu, Fe, K, Mg, Mn, Na, N, Mo, S, Sr, P, and Zn) concentrations of cultivated P. tenuifolia were measured using inductively coupled plasma mass spectroscopy (ICP-MS). The element composition samples were classified by radar plot, elemental fingerprint, and multivariate data analyses, such as hierarchical cluster analysis (HCA), principle component analysis (PCA), and discriminant analysis (DA). This study shows that radar plots and multivariate data analysis can satisfactorily distinguish the geographical origin of cultivated P. tenuifolia. Furthermore, PCA results revealed that N, Cu, K, Mo, Sr, Ca, and Zn are the characteristic elements of cultivated P. tenuifolia. Therefore, multi-element fingerprinting coupled with multivariate statistical techniques can be considered an effective tool to discriminate geographical origin of cultivated P. tenuifolia.

When developing fingerprints of herbal samples, three steps should be considered, that is, the development, the validation, and the extraction of information from the multivariate fingerprint data. After an introduction concerning the tendency to shifting toward using fingerprints to evaluate herbal samples in various contexts and a brief description of the three steps when developing fingerprints, the major part of this chapter focuses on the multivariate data analysis of herbal fingerprints. This chapter also deals with the preprocessing of the multivariate data, and unsupervised and supervised data analysis techniques are described in short Finally, some case studies are presented, in which multivariate data analysis methods were applied to extract information on herbal samples from various types of fingerprints, such as spectroscopic, mass spectrometric, chromatographic fingerprints, and fingerprints developed using electrodriven techniques. Depending on the goal of the study, different multivariate data analysis methods were used.

The complex chemical composition of honey presents significant challenges for its analysis with variations influenced by factors such as botanical source, geographical location, bee species, harvest time, and storage conditions. This study aimed to employ high-performance thin-layer chromatography (HPTLC) fingerprinting, coupled with multivariate data analysis, to characterise the chemical profiles of Australian stingless bee honey samples from two distinct bee species, Tetragonula carbonaria and Tetragonula hockingsi. Using a mobile phase composed of toluene:ethyl acetate:formic acid (6:5:1) and two derivatisation reagents, vanillin–sulfuric acid and natural product reagent/PEG, HPTLC fingerprints were developed to reveal characteristic patterns within the samples. Multivariate data analysis was employed to explore the similarities in the fingerprints and identify underlying patterns. The results demonstrated that the chemical profiles were more closely related to harvest time rather than bee species, as samples collected within the same month clustered together. The quality of the clustering results was assessed using silhouette scores. The study highlights the value of combining HPTLC fingerprinting with multivariate data analysis to produce valuable data that can aid in blending strategies and the creation of reference standards for future quality control analyses.

For most environmental systems, specifically wastewater treatment plants and aquifers, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify temporal operational performance. The objective of this research study was to provide an effective means for comprehensive temporal evaluation of environmental systems. The proposed methodology used several multivariate data analyses and statistical techniques to present an assessment framework for the water quality monitoring programs as well as optimization of treatment plants and aquifer systems. The developed procedure considered the combination of statistical and data analysis algorithms including correlation techniques, factor analysis and principal component analysis, and multivariate stepwise regression analysis. Those methodologies were used to develop a series of independent indexes to quantify the composition of wastewater and groundwater. Also, by developing a stepwise data analysis approach, a baseline was introduced to discover the key operational parameters which significantly affect the performance of environmental systems. Moreover, a comprehensive approach was introduced to develop numerical models for forecasting key operational and quality parameters which can be used for future simulation and scenario analysis practices. The developed methodology and frameworks were successfully applied to four case studies which include three wastewater treatment plants and an aquifer system. In the first case study, the aforementioned approach was applied to the Floyds Fork water quality treatment center in Louisville, KY. The objective of this case study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. The study presented a multivariate statistical and data analyses of the wastewater physicochemical parameters to provide a baseline for temporal assessment of the treatment plant. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9%

Application of direct analysis in real time-orbitrap mass spectrometry combined with multivariate data analysis for rapid quality assessment of Yuanhu Zhitong Tablet

Two-dimensional difference gel electrophoresis (DIGE) in combination with univariate (Student's t-test) and multivariate data analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to study the anti-inflammatory effects of the beta(2)-adrenergic receptor (beta(2)-AR) agonist zilpaterol. U937 macrophages were exposed to the endotoxin lipopolysaccharide (LPS) to induce an inflammatory reaction, which was inhibited by the addition of zilpaterol (LZ). This inhibition was counteracted by addition of the beta(2)-AR antagonist propranolol (LZP). The extracellular proteome of the U937 cells induced by the three treatments were examined by DIGE. PCA was used as an explorative tool to investigate the clustering of the proteome dataset. Using this tool, the dataset obtained from cells treated with LPS and LZP were separated from those obtained from LZ treated cells. PLS-DA, a multivariate data analysis tool that also takes correlations between protein spots and class assignment into account, correctly classified the different extracellular proteomes and showed that many proteins were differentially expressed between the proteome of inflamed cells (LPS and LZP) and cells in which the inflammatory response was inhibited (LZ). The Student's t-test revealed 8 potential protein biomarkers, each of which was expressed at a similar level in the LPS and LZP treated cells, but differently expressed in the LZ treated cells. Two of the identified proteins, macrophage inflammatory protein-1beta (MIP-1beta) and macrophage inflammatory protein-1alpha (MIP-1alpha) are known secreted proteins. The inhibition of MIP-1beta by zilpaterol and the involvement of the beta(2)-AR and cAMP were confirmed using a specific immunoassay.

A new approach to cortical perfusion imaging is demonstrated using high-sensitivity thermography in conjunction with multivariate statistical data analysis. Local temperature changes caused by a cold bolus are imaged and transferred to a false color image. A cold bolus of 10 ml saline at ice temperature is injected systemically via a central venous access. During the injection, a sequence of 735 thermographic images are recorded within 2 min. The recorded data cube is subjected to a principal component analysis (PCA) to select slight changes of the cortical temperature caused by the cold bolus. PCA reveals that 11 s after injection the temperature of blood vessels is shortly decreased followed by an increase to the temperature before the cold bolus is injected. We demonstrate the potential of intraoperative thermography in combination with multivariate data analysis to image cortical cerebral perfusion without any markers. We provide the first in vivo application of multivariate thermographic imaging.

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

Fruit-based snacks produced via drying methods have gained commercial relevance in the global food industry. Different drying technique distinctly influences food quality attributes. Color is one of the most fundamental quality parameters of a food, and undesirable color changes in dried foods can lead to reduced marketability and final quality. This study was investigated that the effects of microwave power level (200 and 600 W) (MD) and slice thickness (3 and 5 mm) on color retention in pineapples. Colorimetric parameters (L*a*b*Ch), Browning Index (BI), Whiteness Index (WI), and total color difference (ΔE) were evaluated. Additionally, Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) analyses were conducted using color models. The findings demonstrated that MD power intensity and pineapple slice thickness had a pronounced effect on color properties. Increased MD power intensity and slice thickness resulted in significant reductions in L*, b*, C, and WI values, while a*, ΔE, and BI values exhibited marked increases, indicating intensified pigment degradation and non-enzymatic browning reactions. The most favorable visual quality was achieved at 200 W combined with 3 mm slice thickness, minimizing thermal damage and preserving yellow hues. PCA, explained for 97% of the total variance and effectively differentiated the samples according to drying conditions. Furthermore, the SIMCA analysis revealed that pineapple slices obtained through different drying methods could be classified based on their color data with 85% accuracy. The study demonstrates that maintaining the chromatic integrity and visual quality of microwave-dried pineapples requires careful optimization of both power density and slice dimensions.

Effect of ripening and variety on the physiochemical quality and flavor of fermented Chinese chili pepper (Paojiao)

Hydroelectric generation is comprised of complex systems specifically designed to meet the dynamic load. Forced outages and unscheduled maintenance activities severely limit the generation output and oftentimes create undesired environmental effects within the immediate Dam/Reservoir area as well as the downstream surroundings. The introduction of multivariate descriptive data analysis and multi-criteria decision making in the maintenance sphere of hydroelectric generation are designed to eliminate reactive preservation methodologies while economically dispatching the unit. Moreover, the implementation of a correlation matrix for powertrain and auxiliary electrical systems produce a general method to localize the outage cause to a component level. Statistical regression techniques were used to evaluate the differences of inconsistencies between maintenance practices and theoretical systematic preservation methods experienced in the hydroelectric generation industry. The regression forecasting model minimizes the risks typically encountered in systems maintenance and prioritizes capital-intense projects within the power production envelope. Additionally, the application will assist the decision-makers with systematic and orderly ranking of projects competing for scarce resources (labor, material, and funding) over a multi-year period in a constrained power production environment. The identification of inadequate performing assets and its cost effectiveness throughout the electrical footprint is an important tenet of the program.

High quality extra virgin olive oils represent an optimal source of nutraceuticals. The European Union (EU) is the world’s leading olive oil producer, with the Mediterranean region as the main contributor. This makes the EU the greatest exporter and consumer of olive oil in the world. However, small olive oil producers also contribute to olive oil production. Beneficial effects on human health of extra virgin olive oil are well known, and these can be correlated to the presence of vitamin E and phenols. Together with the origin of the olives, extraction technology can influence the chemical composition of extra virgin olive oil. The aim of this study was to investigate the concentration of potentially bioactive compounds in Italian extra virgin olive oils from various sources. For this purpose, vitamin E and phenolic fractions were characterized using high-performance liquid chromatography (HPLC) coupled with fluorescence, photodiode array and mass spectrometry detection in fifty samples of oil pressed at industrial plants and sixty-six samples of oil produced in low-scale mills. Multivariate statistical data analysis was used to determine the applicability of selected phenolic compounds as potential quality indicators of extra virgin olive oils.

To investigate the effects of growth conditions related to marine habitat on antibiotic production in sponge-derived Salinispora actinobacteria. Media with varying salt concentration were used to investigate the effects of salinity in relation to Salinispora growth and rifamycin production. The chemotypic profiles of the model strain Salinispora arenicola M413 was then assessed using metabolomic fingerprints from high-pressure liquid chromatography with diode array detection (HPLC-DAD) and multivariate data analysis, before extending this approach to two other strains of S. arenicola. Fingerprint data were generated from extracts of S. arenicola broth cultures grown in media of varying salt (NaCl) concentrations. These fingerprints were then compared using multivariate analysis methods such as principal components analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). From the analysis, a low-sodium growth condition (1% NaCl) was found to delay the onset of growth of the model S. arenicola M413 strain when compared to growth in media with either 3% artificial sea salt or 3% NaCl. However, low-sodium growth conditions also increased cell mass yield and contributed to at least a significant twofold increase in rifamycin yield when compared to growth in 3% artificial sea salt and 3% NaCl. The integration of HPLC-DAD and multivariate analysis proved to be an effective method of assessing chemotypic variations in Salinispora grown in different salt conditions, with clear differences between strain-related chemotypes apparent due to varying salt concentrations. The observed variation in S. arenicola chemotypic profiles further suggests diversity in secondary metabolites in this actinomycete in response to changes in the salinity of its environment.