Optimization of Pressurized Liquid Extraction (PLE) Parameters for Extraction of Bioactive Compounds from Moringa oleifera Leaves and Bioactivity Assessment.

Moringa oleifera leaves are rich sources of bioactive compounds with potential health benefits, including antioxidants and anti-inflammatory agents. Pressurized liquid extraction (PLE) stands out as a promising technique for effectively extracting valuable compounds from natural sources. In this study, we aimed to optimize PLE parameters, such as temperature, extraction duration, and pressure, to maximize bioactive compound (polyphenols, flavonoids, and ascorbic acid) yield from M. oleifera leaves and evaluate their antioxidant and anti-inflammatory activities. According to the outcomes of this research, the maximum achieved total polyphenol content was 24.10 mg gallic acid equivalents (GAE)/g of dry weight (dw), and the total flavonoid content was increased up to 19.89 mg rutin equivalents (RtE)/g dw. Moreover, after HPLC-DAD analysis, neochlorogenic and chlorogenic acids, catechin and epicatechin, rutin, and narirutin were identified and quantified. As far as the optimum ascorbic acid content is concerned, it was found to be 4.77 mg/g dw. The antioxidant activity was evaluated by three different methods: ferric reducing antioxidant power (FRAP), the DPPH method, and the anti-hydrogen peroxide activity (AHPA) method, resulting in 124.29 µmol ascorbic acid equivalent (AAE)/g dw, 131.28 µmol AAE/g dw, and 229.38 µmol AAE/g dw values, respectively. Lastly, the albumin denaturation inhibition was found to be 37.54%. These findings underscore the potential of PLE as an efficient extraction method for preparing extracts from M. oleifera leaves with the maximum content of bioactive compounds.

Development of a method for evaluating skin dullness: A mathematical model explaining dullness by the color, optical properties, and microtopography of the skin.

BACKGROUND: Skin dullness has long been a major concern of Japanese women. It is usually evaluated and judged visually by experts. Although several factors are recognized to play a role, it is unclear to what extent such physiological characteristics contribute to skin dullness. The purpose of this study is to establish an objective method for evaluation, which will assist in developing cosmetics products targeting skin dullness. METHODS: We conducted a skin measurement study on 50 Japanese women in their 30-50s, where skin dullness was visually assessed by a group of experts to obtain an average dullness score, and several skin parameters were obtained. We then developed a regression model that explains the visual assessment score using these physiological parameters. RESULTS: The results of partial least squares analysis of the dullness perception and physiological characteristics showed that skin dullness can be defined by colorimetric, optical, and skin surface microtopography parameters. Additionally, the contribution of each parameter to the model was determined. Our results suggest that dullness perception is highly affected by the melanin content and yellowness of the skin, followed by skin reddishness, roughness, and translucency score, whereas glossiness has less effect. Strikingly, the contribution ratio of each parameter varied among age groups. Furthermore, we confirmed that the predicted value of skin dullness increases with age. CONCLUSION: Our results will help the design of cosmetics targeting factors specific to age groups in developing effective solutions for skin dullness.

Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis.

In metabolomics, identification of metabolic pathways altered by disease, genetics, or environmental perturbations is crucial to uncover the underlying biological mechanisms. A number of pathway analysis methods are currently available, which are generally based on equal-probability, topological-centrality, or model-separability methods. In brief, prior identification of significant metabolites is needed for the first two types of methods, while each pathway is modeled separately in the model-separability-based methods. In these methods, interactions between metabolic pathways are not taken into consideration. The current study aims to develop a novel metabolic pathway identification method based on multi-block partial least squares (MB-PLS) analysis by including all pathways into a global model to facilitate biological interpretation. The detected metabolites are first assigned to pathway blocks based on their roles in metabolism as defined by the KEGG pathway database. The metabolite intensity or concentration data matrix is then reconstructed as data blocks according to the metabolite subsets. Then, a MB-PLS model is built on these data blocks. A new metric, named the pathway importance in projection (PIP), is proposed for evaluation of the significance of each metabolic pathway for group separation. A simulated dataset was generated by imposing artificial perturbation on four pre-defined pathways of the healthy control group of a colorectal cancer study. Performance of the proposed method was evaluated and compared with seven other commonly used methods using both an actual metabolomics dataset and the simulated dataset. For the real metabolomics dataset, most of the significant pathways identified by the proposed method were found to be consistent with the published literature. For the simulated dataset, the significant pathways identified by the proposed method are highly consistent with the pre-defined pathways. The experimental results demonstrate that the proposed method is effective for identification of significant metabolic pathways, which may facilitate biological interpretation of metabolomics data.

Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI.

The impact of in-scanner motion on functional magnetic resonance imaging (fMRI) data has a notorious reputation in the neuroimaging community. State-of-the-art guidelines advise to scrub out excessively corrupted frames as assessed by a composite framewise displacement (FD) score, to regress out models of nuisance variables, and to include average FD as a covariate in group-level analyses. Here, we studied individual motion time courses at time points typically retained in fMRI analyses. We observed that even in this set of putatively clean time points, motion exhibited a very clear spatio-temporal structure, so that we could distinguish subjects into separate groups of movers with varying characteristics. Then, we showed that this spatio-temporal motion cartography tightly relates to a broad array of anthropometric and cognitive factors. Convergent results were obtained from two different analytical perspectives: univariate assessment of behavioural differences across mover subgroups unraveled defining markers, while subsequent multivariate analysis broadened the range of involved factors and clarified that multiple motion/behaviour modes of covariance overlap in the data. Our results demonstrate that even the smaller episodes of motion typically retained in fMRI analyses carry structured, behaviourally relevant information. They call for further examinations of possible biases in current regression-based motion correction strategies.

Bioactive Chemical Composition of Cannabis Extracts and Cannabinoid Receptors.

Cannabis is widely used as a therapeutic drug, especially by patients suffering from psychiatric and neurodegenerative diseases. However, the complex interplay between phytocannabinoids and their targets in the human receptome remains largely a mystery, and there have been few investigations into the relationship between the chemical composition of medical cannabis and the corresponding biological activity. In this study, we investigated 59 cannabis samples used by patients for medical reasons. The samples were subjected to extraction (microwave and supercritical carbon dioxide) and chemical analyses, and the resulting extracts were assayed in vitro using the CB1 and CB2 receptors. Using a partial least squares regression analysis, the chemical compositions of the extracts were then correlated to their corresponding cannabinoid receptor activities, thus generating predictive models that describe the receptor potency as a function of major phytocannabinoid content. Using the current dataset, meaningful models for CB1 and CB2 receptor agonism were obtained, and these reveal the insignificant relationships between the major phytocannabinoid content and receptor affinity for CB1 but good correlations between the two at CB2 receptors. These results also explain the anomalies between the receptor activities of pure phytocannabinoids and cannabis extracts. Furthermore, the models for CB1 and CB2 agonism in cannabis extracts predict the cannabinoid receptor activities of individual phytocannabinoids with reasonable accuracy. Here for the first time, we disclose a method to predict the relationship between the chemical composition, including phytocannabinoids, of cannabis extracts and cannabinoid receptor responses.

Does the performance of wet granulation and tablet hardness affect the drug dissolution profile of carvedilol in matrix tablets?

Wet granulation is mostly used process for manufacturing matrix tablets. Compared to the direct compression method, it allows for a better flow and compressibility properties of compression mixtures. Granulation, including process parameters and tableting, can influence critical quality attributes (CQAs) of hydrophilic matrix tablets. One of the most important CQAs is the drug release profile. We studied the influence of granulation process parameters (type of nozzle and water quantity used as granulation liquid) and tablet hardness on the drug release profile. Matrix tablets contained HPMC K4M hydrophilic matrix former and carvedilol as a model drug. The influence of selected HPMC characteristics on the drug release profile was also evaluated using two additional HPMC batches. For statistical evaluation, partial least square (PLS) models were generated for each time point of the drug release profile using the same number of latent factors. In this way, it was possible to evaluate how the importance of factors influencing drug dissolution changes in dependence on time throughout the drug release profile. The results of statistical evaluation show that the granulation process parameters (granulation liquid quantity and type of nozzle) and tablet hardness significantly influence the release profile. On the other hand, the influence of HPMC characteristics is negligible in comparison to the other factors studied. Using a higher granulation liquid quantity and the standard nozzle type results in larger granules with a higher density and lower porosity, which leads to a slower drug release profile. Lower tablet hardness also slows down the release profile.

Molecular modeling-driven approach for identification of Janus kinase 1 inhibitors through 3D-QSAR, docking and molecular dynamics simulations.

Janus kinase 1 (JAK 1) belongs to the JAK family of intracellular nonreceptor tyrosine kinase. JAK-signal transducer and activator of transcription (JAK-STAT) pathway mediate signaling by cytokines, which control survival, proliferation and differentiation of a variety of cells. Three-dimensional quantitative structure activity relationship (3 D-QSAR), molecular docking and molecular dynamics (MD) methods was carried out on a dataset of Janus kinase 1(JAK 1) inhibitors. Ligands were constructed and docked into the active site of protein using GLIDE 5.6. Best docked poses were selected after analysis for further 3 D-QSAR analysis using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodology. Employing 60 molecules in the training set, 3 D-QSAR models were generate that showed good statistical reliability, which is clearly observed in terms of r2ncv and q2loo values. The predictive ability of these models was determined using a test set of 25 molecules that gave acceptable predictive correlation (r2Pred) values. The key amino acid residues were identified by means of molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good consonance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the reasonable modification of molecules in order to design more efficient JAK 1 inhibitors. The developed models are expected to provide some directives for further synthesis of highly effective JAK 1 inhibitors.

[Studies on difference of chemical compositions in plant species of Tripterygium genus].

We studied the content of chemical compositions and correlation among species of Tripterygium genus by principal component analysis(PCA) and variance analysis(ANOVA), and we also studied the difference among the 3 species.Using [BMIm]PF6 ionic liquid-based ultrasonic-assisted extraction, we determined the contents of 11 compounds including wilforgine, wilforzine, triptophenolide, wilforine, triptoquinone A, triptolide, tripterin, egallocatechin, epigallocatechin, catechin, and epicatechin in 28 batches of the Tripterygium species by HPLC and PCA. Partial least squares analysis (PLS) and ANOVA were also performed to verify the results.The analysis results of PCA and PLS showed that three species of Tripterygium genus were clustered into three regions respectively, and triptoquinone A was the important factor which affected the aggregation of these three species.There was a significant difference among the contents of 11 chemical components in the three species(P<0.000 1).These results indicated that there was a certain correlation between the chemical compositions and the classification of the species, and the difference of the chemical compositions among the three species was obvious. In this work, the content determination method is rapid and accurate, and the analysis method is simple and convenient, which provides a reference for the classification, the efficacy and the toxicity of the species.

Optimal secretion of alkali-tolerant xylanase in Bacillus subtilis by signal peptide screening.

Xylanases are industrially important enzymes for xylan digestion. We experimentally screened over 114 Sec and 24 Tat pathway signal peptides, with two different promoters, for optimal production of an alkaline active xylanase (XynBYG) from Bacillus pumilus BYG in a Bacillus subtilis host. Though both promoters yielded highly consistent secretion levels (0.97 Pearson correlation coefficient), the Sec pathway was found to be more efficient than the Tat pathway for XynBYG secretion. Furthermore, the optimal signal peptide (phoB) for XynBYG secretion was found to be different from the optimal peptides for cutinase and esterase reported in previous studies. A partial least squares regression analysis further identified several statistically important variables: helical properties, amino acid composition bias, and the discrimination score in Signal P. These variables explain the observed 23 % variance in the secretion yield of XynBYG by the different signal peptides. The results also suggest that the helical propensity of a signal peptide plays a significant role in the beta-rich xylanase, but not in the helix-rich cutinase, suggesting a coupling of the conformations between the signal peptide and its cargo protein for optimal secretion.

Morphological Covariation between the Maxillary Sinus and Midfacial Skeleton among Sub-Saharan and Circumpolar Modern Humans.

OBJECTIVES: Maxillary sinus volume tracks ecogeographic differences in nasal form and may serve as a zone of accommodation for ontogenetic and evolutionary changes in nasal cavity breadth. However, little is known regarding how sinus volume is distributed within the midface. This study investigates morphological covariation between midfacial and sinus shape to better understand structural and functional relationships between the sinus, midface, and nasal cavity. METHODS: Cranial and sinus models were rendered from CT scans of modern human samples from two disparate climates: sub-Saharan (South Africans [n = 15], West Africans [n = 17]), and circumpolar (Siberian Buriats [n = 18], Alaskan Inuit [n = 20]). Twenty-five 3D coordinate landmarks were placed on the models and subjected to generalized Procrustes analysis. Two-block partial least squares (2B-PLS) analysis was employed to identify patterns of covariation. RESULTS: The 2B-PLS analysis indicates PLS1 (58.6% total covariation) relates to height and breadth relationships between the midface, nasal cavity, and maxillary sinus. Significant regional differences in PLS1 scores are evident: circumpolar samples possess taller/narrower noses with taller/wider sinuses compared to sub-Saharan samples. Importantly, PLS1 indicates that sinus breadth is not exclusively related to nasal cavity breadth; variation in lateral sinus expansion toward the zygoma represents an important contributing factor. PLS2 (16%) relates to supero-inferior positioning of the sinus within the midface. Allometric trends, while statistically significant, explain only a small portion of these covariation patterns. CONCLUSIONS: These results suggest that the maxillary sinus serves as a zone of accommodation at the confluence of multiple facial components, potentially minimizing effects of morphological alterations to certain components on adjacent structures. Am J Phys Anthropol 160:483-497, 2016. © 2016 Wiley Periodicals, Inc.

The contribution of subsistence to global human cranial variation.

Diet-related cranial variation in modern humans is well documented on a regional scale, with ample examples of cranial changes related to the agricultural transition. However, the influence of subsistence strategy on global cranial variation is less clear, having been confirmed only for the mandible, and dietary effects beyond agriculture are often neglected. Here we identify global patterns of subsistence-related human cranial shape variation. We analysed a worldwide sample of 15 populations (n = 255) with known subsistence strategies using 3-D landmark datasets designed to capture the shape of different units of the cranium. Results show significant correlations between global cranial shape and diet, especially for temporalis muscle shape and general cranial shape. Importantly, the differences between populations with either a plant- or an animal-based diet are more pronounced than those between agriculturalists and hunter-gatherers, suggesting that the influence of diet as driver of cranial variation is not limited to Holocene transitions to agricultural subsistence. Dental arch shape did not correlate with subsistence pattern, possibly indicating the high plasticity of this region of the face in relation to age, disease and individual use of the dentition. Our results highlight the importance of subsistence strategy as one of the factors underlying the evolution of human geographic cranial variation.

Novel phenolic inhibitors of the sarco/endoplasmic reticulum calcium ATPase: identification and characterization by quantitative structure-activity relationship modeling and virtual screening.

Inhibitors of the sarco/endoplasmic reticulum calcium ATPase (SERCA) are valuable research tools and hold promise as a new generation of anti-prostate cancer agents. Based on previously determined potencies of phenolic SERCA inhibitors, we created quantitative structure-activity relationship (QSAR) models using three independent development strategies. The obtained QSAR models facilitated virtual screens of several commercial compound collections for novel inhibitors. Sixteen compounds were subsequently evaluated in SERCA activity inhibition assays and 11 showed detectable potencies in the micro- to millimolar range. The experimental results were then incorporated into a comprehensive master QSAR model, whose physical interpretation by partial least squares analysis revealed that properly positioned substituents at the central phenyl ring capable of forming hydrogen bonds and of undergoing hydrophobic interactions were prerequisites for effective SERCA inhibition. The established SAR was in good agreement with findings from previous structural studies, even though it was obtained independently using standard QSAR methodologies.

Exploring Conventional and Green Extraction Methods for Enhancing the Polyphenol Yield and Antioxidant Activity of Hyssopus officinalis Extracts.

Hyssopus officinalis L. (HO) is, as one of the most prevalently utilized plants, used in traditional medicine to cure various diseases as well as the in food and cosmetic industries. Moreover, HO is a rich source of polyphenols with potent antioxidant properties. However, the studies on the extraction of such compounds from HO are scanty and sparse. This study aims to optimize the extraction of polyphenols and maximize the antioxidant activity in HO extracts. A comprehensive experimental design was employed, encompassing varied extraction parameters to determine the most effective ones. Alongside conventional stirring (ST), two green approaches, the ultrasonic treatment (US) and the pulsed electric field (PEF), were explored, either alone or in combination. The extracted polyphenolic compounds were identified with a high-performance liquid chromatography-diode array detector (HPLC-DAD). According to the results, the employment of ST along with an ethanolic solvent at 80 °C for 150 min seems beneficial in maximizing the extraction of polyphenols from HO, resulting in extracts with enhanced antioxidant activity. The total polyphenol was noted at 70.65 ± 2.76 mg gallic acid equivalents (GAE)/g dry weight (dw) using the aforementioned techniques, and the antioxidant activity was noted as 582.23 ± 16.88 µmol ascorbic acid equivalents (AAE)/g dw (with FRAP method) and 343.75 ± 15.61 µmol AAE/g dw (with the DPPH method). The as-prepared extracts can be utilized in the food and cosmetics industries to bestow or enhance the antioxidant properties of commercial products.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production.

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

Multifactorial Modeling of Cognitive Trajectories Using an Advanced Regression Technique: Improving Our Understanding of Biomarkers and Modifiable Variables that Support Cognition.

Current research trends emphasize complex models of cognitive outcomes, with multiple, interacting predictors, including factors amenable to interventions toward sustaining healthy cognitive aging. Such models often require advanced analysis techniques. The article by Stark et al., 'Partial least squares regression analysis of Alzheimer's disease biomarkers, modifiable health variables, and cognitive change in older adults with mild cognitive impairment', uses partial least squares regression to examine the associations to memory and executive change of 29 biomarker and demographic variables. This commentary discusses the significance of their results and methods within the context of current research foci.

Decoupling effect and driving factors of carbon footprint in megacity Wuhan, Central China.

Background: China's 35 largest cities, including Wuhan, are inhabited by approximately 18% of the Chinese population, and account for 40% energy consumption and greenhouse gas emissions. Wuhan is the only sub-provincial city in Central China and, as the eighth largest economy nationwide, has experienced a notable increase in energy consumption. However, major knowledge gaps exist in understanding the nexus of economic development and carbon footprint and their drivers in Wuhan. Methods: We studied Wuhan for the evolutionary characteristics of its carbon footprint (CF), the decoupling relationship between economic development and CF, and the essential drivers of CF. Based on the CF model, we quantified the dynamic trends of CF, carbon carrying capacity, carbon deficit, and carbon deficit pressure index from 2001 to 2020. We also adopted a decoupling model to clarify the coupled dynamics among total CF, its accounts, and economic development. We used the partial least squares method to analyze the influencing factors of Wuhan's CF and determine the main drivers. Results: The CF of Wuhan increased from 36.01 million t CO2eq in 2001 to 70.07 million t CO2eq in 2020, a growth rate of 94.61%, which was much faster than that of the carbon carrying capacity. The energy consumption account (84.15%) far exceeded other accounts, and was mostly contributed by raw coal, coke, and crude oil. The carbon deficit pressure index fluctuated in the range of 8.44-6.74%, indicating that Wuhan was in the relief zone and the mild enhancement zone during 2001-2020. Around the same time, Wuhan was in a transition stage between weak and strong CF decoupling and economic growth. The main driving factor of CF growth was the urban per capita residential building area, while energy consumption per unit of GDP was responsible for the CF decline. Conclusions: Our research highlights the interaction of urban ecological and economic systems, and that Wuhan's CF changes were mainly affected by four factors: city size, economic development, social consumption, and technological progress. The findings are of realistic significance in promoting low-carbon urban development and improving the city's sustainability, and the related policies can offer an excellent benchmark for other cities with similar challenges. Supplementary Information: The online version contains supplementary material available at 10.1186/s13717-023-00435-y.

Rapid detection of phenytoin sodium by partial-least squares and linear regression models combined with surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) for quantitative analysis is challenging owing to the unstable enhanced effect. However, it can be improved by combining it with chemometrics. In this study, we established a quantitative analysis method for phenytoin sodium (PS) based on partial least-squares (PLS) and linear regression (LR) models combined with SERS. Gold nanoparticles (AuNPs) were optimally enhanced substrates for PS. 180 PS samples in the concentration range of 0.98 - 980 µg mL-1 were used to establish a quantitative prediction model by PLS regression, and an accurate and robust prediction was achieved. Furthermore, we found that SERS peak intensity showed a good linear correlation with the concentration of PS in the concentration range of 1 - 80 µg mL -1. After using P-mercaptobenzoic acid as an internal standard, the accuracy and precision of the LR model were significantly improved compared with that of the model without an internal standard. In general, PLS chemometrics and LR model with internal standard which were combined with SERS in this paper provide new possible analytical methods for analytes to develop a rapid and sensitive quantitative analysis method.

Biosynthesis, behavior and fate of volatile organic sulfide in Lentinus edodes (Berk.) upon hot-air drying treatment.

This study elucidated the biosynthesis and changing behaviors of organic sulfide in shiitake mushrooms upon hot-air drying treatment. The changes of aw, moisture migration, contours of taste and flavor, organic sulfide, and 4 key enzyme activities were monitored throughout three drying procedures (CT/ST1/ST2). Results showed that drying rate was related to the moisture migration. Key enzymes of γ-GTase, ASFase and CS lyase were heat-resistant proteases, while C-Dase exhibited low thermal stability with the activity decreased during treatment. A total of 17 organic sulfides were identified and PLS analyses suggested 6 cyclic polysulfides were formed by C-Dase desulfurization, while 5 thioethers generation were related to the thermal cleavage of direct precursors (straight-chain di/tris/tetrasulfonyl esters) and Maillard reaction. These results indicated that ST2 drying procedures had a positive effect on the formation of cyclic polysulfides at the end of drying pried and the achievement of premium flavor qualities.

Optimization of Extraction Parameters for Enhanced Recovery of Bioactive Compounds from Quince Peels Using Response Surface Methodology.

Quinces are well known for their multiple health benefits, including antioxidant, hypoglycemic, antimicrobial, anti-inflammatory, anticarcinogenic, etc., properties. Despite the widespread utilization of various plant parts, the peel has been largely ignored in the industry. In this study, we explored the effects of different extraction parameters, such as temperature, time, and composition of the extraction solvent, and techniques such as ultrasound (US) and a pulsed electric field (PEF), either alone or in combination, and optimized these parameters using a response surface methodology (RSM) to enhance the extraction of bioactive compounds such as chlorogenic acid, total polyphenols, flavonoids, and ascorbic acid from waste quince peels. From our results, it was apparent that quince peels are a great source of many bioactive compounds with high antioxidant activity. More specifically, after principal component analysis (PCA) and partial least squares (PLS) analysis, quince peels contain high levels of total polyphenols (43.99 mg gallic acid equivalents/g dw), total flavonoids (3.86 mg rutin equivalents/g dw), chlorogenic acid (2.12 mg/g dw), and ascorbic acid (543.93 mg/100 g dw), as well as antioxidant activity of 627.73 µmol AAE/g and 699.61 µmol DPPH/g as evidenced by FRAP and DPPH assays, respectively. These results emphasize the potential of utilizing quince peels as an eco-friendly and cost-effective source of bioactive compounds with various applications in the food and pharmaceutical industries for the prepared extracts.

Analysis of volatile compound changes in fried shallot (Allium cepa L. var. aggregatum) oil at different frying temperatures by GC-MS, OAV, and multivariate analysis.

Flavor is a key attribute of fried oil that shows a critical correlation with temperature. Therefore, selecting the appropriate temperature is important in preparing fried shallot oil (FSO). Volatile compounds from five different FSOs were identified and comparatively studied using gas chromatography-mass spectrometry (GC-MS) coupled with multivariate data analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). GC-MS results identified a total of 93 volatiles, among which aldehydes, alcohols, pyrazines, and sulfur-containing compounds were the major compounds. Eighteen compounds had odor active values (OAV) >1. Among the compounds, hexanal, (E)-2-heptenal, (E)-2-octenal, dipropyl disulfide, 2-ethyl-3,5-dimethylpyrazine, and 1-octen-3-ol were important to the overall aroma profile of FSOs. In the PCA model, all the detected FSOs were divided into three clusters, which were assigned as cluster A (FSO5), B (FSO4), and C (the rest FSOs). Multivariate data analyses revealed that nonanal, 2-ethyl-5-methylpyrazine, (E,E)-2,4-decadienal, (E)-2-heptenal, and hexanal contributed positively to the classification of different FSOs. GC-MS coupled with multivariate data analysis could be used as a convenient and efficient analytical method to classify raw materials.