Partial Least-squares Regression Analysis Research Articles

Bioindicator responses to extreme conditions: Insights into pH and bioavailable metals under acidic metal environments

Acid mine drainage (AMD) caused environmental risks from heavy metal pollution, requiring treatment methods such as chemical precipitation and biological treatment. Monitoring and adapting treatment processes was crucial for success, but cost-effective pollution monitoring methods were lacking. Using bioindicators measured through 16S rRNA was a promising method to assess environmental pollution. This study evaluated the effects of AMD on ecological health using the ecological risk index (RI) and the Risk Assessment Code (RAC) indices. Additionally, we also examined how acidic metal stress affected the diversity of bacteria and fungi, as well as their networks. Bioindicators were identified using linear discriminant analysis effect size (LEfSe), Partial least squares regression (PLS-R), and Spearman analyses. The study found that Cd, Cu, Pb, and As pose potential ecological risks in that order. Fungal diversity decreased by 44.88% in AMD-affected areas, more than the 33.61% decrease in bacterial diversity. Microbial diversity was positively correlated with pH (r = 0.88, p = 0.04) and negatively correlated with bioavailable metal concentrations (r = −0.59, p = 0.05). Similarly, microbial diversity was negatively correlated with bioavailable metal concentrations (bio_Cu, bio_Pb, bio_Cd) (r = 0.79, p = 0.03). Acidiferrobacter and Thermoplasmataceae were prevalent in acidic metal environments, while Puia and Chitinophagaceae were identified as biomarker species in the control area (LDA>4). Acidiferrobacter and Thermoplasmataceae were found to be pH-tolerant bioindicators with high reliability (r = 1, P < 0.05, BW > 0.1) through PLS-R and Spearman analysis. Conversely, Puia and Chitinophagaceae were pH-sensitive bioindicators, while Teratosphaeriaceae was a potential bioindicator for Cu–Zn–Cd metal pollution. This study identified bioindicator species for acid and metal pollution in AMD habitats. This study outlined the focus of biological monitoring in AMD acidic stress environments, including extreme pH, heavy metal pollutants, and indicator species. It also provided essential information for heavy metal bioremediation, such as the role of omics and the effects of organic matter on metal bioavailability.

Characterization of the effect of non-Saccharomyces cerevisiaes on the non-volatile constituents and volatile profiles of low-alcoholic pomegranate beverages

To create the low-alcoholic beverages with excellent nutritional properties and flavor qualities, pomegranate juice was fermented with 16 strains of non-Saccharomyces yeasts from 9 species without added sugar. The effects of different yeasts on the non-volatile constituents (vitamins, organic acids, polyphenols), volatile compounds and sensory characteristics of the fermented beverages were comprehensively evaluated. The study revealed that non-Saccharomyces fermentations resulted in a higher abundance of polyphenols compared to the pomegranate juice and S. cerevisiae WLS21 (Sc21). The monomer phenols in Meyerozyma guilliermondii 12 (Mg12) were approximately twice the total of CK and 1.3 times of Sc21. Moreover, they led to a higher concentration of volatile compounds than the pomegranate juice. A total of 94 volatile compounds were identified in the pomegranate fermented beverages. In contrast to S. cerevisiae, Wickerhamomyces anomalus 1-1 and 10 (Wa1-1, Wa10), Pichia kudriavzevii 8 (Pk8) and Mg12 resulted in more acetate esters, and Zygosaccharomyces rouxii IFO 1130 and 53 (Zr1130, Zr53) yield higher levels of ethyl esters. A total of 40 volatiles with a relative odour activity value (rOAV) of ≥ 0.1 were identified, of which 14 were selected for principal component analysis (PCA). The sensory analysis confirmed that Zr1130, Zr53, Wa1-1, Wa10 and Pk8 strains exhibited superior aroma generation compared to Sc21. Furthermore, the partial least-squares regression (PLSR) analysis revealed that cherry, red fruits, black currant, citrus fruits, raspberry, black berry, pineapple, floral, sour and alcoholic odours had a significantly positive impact on the overall acceptability of the fermented beverages.

Non-Destructive Evaluation of the Physiochemical Properties of Milk Drink Flavored with Date Syrup Utilizing VIS-NIR Spectroscopy and ANN Analysis.

A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow's and camel's milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible-near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, and the R2 value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Aroma and taste analysis of pickled tea from spontaneous and yeast-enhanced fermentation by mass spectrometry and sensory evaluation

Anaerobically fermented pickled tea (PT) can be produced by spontaneous fermentation (SF) or yeast-enhanced fermentation (YF). Aroma and taste characteristics of PT during YF and SF were investigated using sensory evaluation, odour activity, aroma character impact values, HS-SPME-GC–MS, UPLC-QQQ-MS/MS, and spectrophotometry, annotating 198 volatile and 115 non-volatile components. The main contributing volatile components were β-ionone, and 1-octanol, promoted by YF and SF, and yielding floral and fruity aromas respectively. Additionally, compared with SF, YF promoted the formation of citronellol yielding a floral aroma, inhibited the stale aroma of methoxybenzenes, and reduced bitter, astringent, and sour tastes. Furthermore, partial least-squares regression analysis identified the main components related to the ‘acides aroma’ of PT as linalool oxide, n-decanoic acid, hexanoic acid, 3,7-dimethyl-2,6-octadienoic acid, 3-methyl-1-dodecyn-3-ol, and nerolidol. This application could be used as methodology for the comprehensive analysis of tea aroma and taste and these results can act as guidelines for PT production and quality control.

Providing Innovation Opportunities Can Increase Employee Satisfaction and Performance on Small and Medium Shoes Industry

To be able to survive the global economic, every business organization is forced to continue to innovate and small and medium industry (SMEs) are deemed capable of winning the competition in the industry. SMEs have the freedom to move and innovate because they are still small in scope, so innovation are needed in order to have a competitive advantage. This study investigates the direct and indirect effects of strategic innovation on employee performance by analysing its influence on work satisfaction. The research sample consists of 120 employees from the small and medium shoe business in East Java. The cluster random sampling approach was used to choose the participants. The data analysis model employs the PLS (Partial Least Squares regression) analysis approach with the Smart PLS version 3.0 software. The findings of hypothesis testing indicate that there is no significant direct impact of strategic innovation on employee performance. Employee performance is directly influenced by job happiness. However, strategic innovation has an indirect impact on employee performance by influencing work happiness. This suggests that work happiness might establish a link between the impact of strategic innovation and employee performance.

Assessing the differences of two vineyards soils’ by NIR spectroscopy and chemometrics

Soil properties influence greatly the status of vine plants which consequently influences the quality of wine. Therefore, in the context of viticulture management, it is extremely important to assess the physical and chemical parameters of vineyards soils. In this study, the soils of two vineyards were analysed by near-infrared (NIR) spectroscopy and established analytical reference procedures. The main objective of this study was to verify if NIR spectroscopy is a potential tool to discriminate the soils of both vineyards as well as to quantify differences of soil's parameters. For that, a total of eight sampling spots were selected at each vineyard taking into consideration the soil type and sampled at different depths. The data analysis was performed using analysis of variance (ANOVA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression. The ANOVA results revealed that 12 out of the 18 parameters analysed through the reference procedures can be considered statistically different (p < 0.05). Regarding PCA, the obtained results revealed a clear separation between the scores of both vineyards either considering NIR spectra or the chemical parameters. The PLS-DA model was able to obtain 100 % of correct predictions for the discrimination of both vineyards. PLS regression analysis using NIR spectra revealed R2P and RER values higher than 0.85 and 10, respectively, for 8 (pH (H2O), N, Ca2+, Mg2+, SB, CEC, ECEC and GSB) of the 18 chemical parameters evaluated. Concluding, these results demonstrate that it is possible to discriminate the soils of the different vineyards through NIR spectroscopy as well as to quantify several chemical parameters through soils NIR spectra in a rapid, accurate, cost-effective, simple and environmentally friendly way when compared to the reference procedures.

Quantifying soil erosion and influential factors in Guwahati's urban watershed using statistical analysis, machine and deep learning

Soil erosion is a complex environmental issue influenced by rapid climate change, resource exploitation, and soil degradation etc. These factors have triggered global acceleration of soil erosion, primarily due to rapid transformation of topographical features and landscape composition. Guwahati, a thriving financial hub in northeast India, witnessing significant landscape change on both the banks of the Brahmaputra river therefore becomes disaster-prone zones. Hence, the objective of the present study is to identify soil erosion factors and assess its impact using statistical, machine learning, and deep learning techniques. It employs Revised Universal Soil Loss Equation (RUSLE) model for soil erosion estimation, furthermore analyzing physical attributes such as morphometrics, topography, drainage networks, and land use fragmentation indicators. Partial Least Squares Regression (PLSR), Random Forest (RF) sensitivity analysis, and Deep Neural Network (DNN) techniques are used in the study. The RUSLE model showed a significant range of soil erosion rates in the study area, spanning from 168.16 to 188.60 tonnes/hectare/year. Particularly, Silsako, Bharalu, North Guwahati, and Foreshore experiences the most severe soil loss. Amongst all influential factors contributing to soil erosion, the most important key parameters are rainfall, drainage density, landscape fragmentation components (such as cohesion index, edge density, and Shannon diversity index), along with stream frequency and basin relief, as indicated by the RF and DNN models. Furthermore, the PLSR analysis assigned linear weights to variables, highlighting the effectiveness of 14 out of 15 independent predictors derived from basin characteristics in accurately estimating soil erosion. This study provides important quantitative insights through rigorous scientific analysis, enabling well-informed decisions in urban watershed management within the Brahmaputra region. Furthermore, it enhances understanding of the area's urgent needs, societal implications, and environmental conditions related to soil erosion.

Brown adipose tissue evaluation using water and triglyceride as indices by diffuse reflectance spectroscopy.

Brown adipose tissue (BAT) is related to lipid and glucose metabolism, and BAT evaluation is expected to contribute to disease prevention and treatment. We aimed to establish a BAT evaluation method using simple and non-invasive diffuse reflectance spectroscopy (DRS). We acquired diffuse reflectance spectra of BAT using DRS from rats with cold stimulation and analyzed the second-derivative spectra. To predict the amount of triglyceride in BAT from the second-derivative spectra, partial least-squares regression analysis was performed, and we examined whether BAT weight can be predicted from the amount of triglyceride by single regression analysis. By focusing on changes in the amount of triglyceride in BAT with cold stimulation, it was suggested that this amount could be predicted spectroscopically, and the predicted amount of triglyceride could be used to estimate the BAT weight with cold stimulation. If these results can be translated into humans, they may contribute to preventing metabolic disorders.

How volatile composition facilitates olfactory discrimination of fat content in beef and pork

Foods differing in fat content can be distinguished through olfaction alone. The mechanisms underlying the ability of humans to discriminate between foods differing in fat content through olfaction are underexplored. In this study, beef and pork samples were prepared (raw and roasted) with low (muscle tissue; raw: 2–5%; roasted: 5%), medium (muscle tissue with lard; raw: 25–30%; roasted: 36–44%), and high (lard; raw: 40–42%; roasted: 69–70%) fat content. Olfactory triangle discrimination tests and ranking tests were performed to explore whether humans can discriminate and rank fat content of the samples through orthonasal olfaction. Headspace-Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry (SPME-GC–MS) was used to characterize the volatile compound composition of the headspace of samples differing in fat content. Partial least-squares regression and partial least squares-discriminant analysis were performed to determine the volatile compounds that were responsible for olfactory fat content discrimination. We found that fat content in both raw and roasted samples can be distinguished through orthonasal olfaction. Perceived odor differences did not always contribute to olfactory identification of fat content. Roasted beef and pork meats with higher fat content had more abundant fatty acids, aldehydes, and ketones. Phthalic acid, isobutyl 2-ropylpentyl ester, and carbon disulfide facilitated the olfactory discrimination of fat content in raw pork and beef samples. 2-Methyl-propanal, benzaldehyde, 1-hydroxy-2-propanone, 2,3-pentanedione, 2,5-octanedione, and 2-butanone contributed to odor differences of roasted beef samples differing in fat content. We conclude that beef and pork samples differing in fat content differ in volatile compound composition of the headspace, and that these differences facilitate discrimination between samples differing in fat content based on olfaction alone.

Fast and Facile Etching of Gold Nanorods by N-Halosuccinimides: Toward Multicolorimetric Identification and Quantification of 20 Natural Amino Acids.

Gold nanorods (AuNRs) have recently become fascinating chromophores in the field of colorimetric sensing because of their eye-catching rainbow colors along with the high dimensionality of their optical profile. The etching of AuNRs using an analyte-sensitive oxidizing agent is particularly an attractive tool not only for adjusting their plasmonic behavior through altering their aspect ratio but also for correlating the observed signal with the identity and concentration of the analyte. However, the deployment of this strategy in the field of sensing has been seriously hindered by various factors ranging from slow etching kinetics and the need for nonambient temperatures to low degrees of controllability along with the high toxicity of the etchants. To resolve these challenges, the present study aims to introduce the outstanding potentials of two inexpensive mild oxidants comprising N-bromosuccinimide (NBS) and N-chlorosuccinimide (NCS) in the highly fast and controllable etching of AuNRs at room temperature. By controlling the concentration of the etchant and the pH of the medium, the longitudinal and transversal peaks could be well adjusted with nanometer precision. In an attempt to elucidate the etching mechanism, the effects of various parameters including the etchant concentration and pH, as well as the kinetics of the etching process were thoroughly investigated. After all, the capability of NBS in decarboxylating the amino acids was further exploited in the design of an all-inclusive multicolorimetric sensor array based on the etching of AuNRs for the sensitive quantification and highly accurate discrimination of all 20 amino acids in the micromolar range. To this end, the acquired data set was analyzed by two machine learning techniques including partial least-squares regression (PLSR) and linear discriminant analysis (LDA). The versatility of N-halosuccinimide reactions with various categories of organic compounds underlies ample opportunities for the design of diverse multicolorimetric sensors, further glamorizing the prospect of this approach.

Malt quality profile of barley predicted by near‐infrared spectroscopy using partial least squares, Bayesian regression, and artificial neural network models

AbstractDue to the significant cost and time involved in identifying barley lines with superior malting quality, the malting industry is searching for accurate and rapid methods to expedite the selection of superior barley lines that meet breeder's goals. The aim of this study is to compare partial least squares regression (PLSR) with advanced statistical models (Bayesian and machine learning) and reliably assess their performance in predicting malt quality traits from near infra‐red (NIR) spectral data using barley grains. Using spectral data as predictors and the malt quality traits as references, PLSR outperformed Bayesian and PCA‐ANN models for diastatic power (DP), alpha amylase (AA), malt extract (ME), wort protein (WP), soluble to total protein (S/T) ratio, and free amino nitrogen (FAN). WP had the best prediction performance for all models, with the best‐performing model, PLSR, having (RPD) values of 0.55 (1.5). The influential wavelength regions identified based on the variable importance in projection (VIP) scores and coefficient estimates for PLSR and Bayesian models, respectively, were comparatively similar for all malt quality traits. Based on these findings, PLSR analysis and wavelength selection techniques would enhance the future design and optimization of NIR prediction models in malt quality improvement programs.

Effects of ultrasound pretreatment on the enzymolysis of hybrid sturgeon (Huso dauricus × Acipenser schrenckii) protein and the structural, functional, and antioxidant properties of hydrolysates

In the current study, hybrid sturgeon hydrolysates were prepared using ultrasound (US) pretreatments for different periods (0, 15, 30, and 45 min) followed by an enzymolysis process using compound proteinase, and their structural, functional, and antioxidant properties were examined. The application of US pretreatments significantly increased the hydrolysis process. SDS-PAGE analysis revealed that as sonication time was increased, peptides with lower molecular weights were obtained. Based on US pretreatment time, hydrolysates demonstrated significant changes in free amino acids, solubility, emulsifying properties, surface hydrophobicity, and water-oil holding ability. Furthermore, US pretreatments had a significant impact on the antiradical activity (DPPH• and ABTS•+) at different hydrolysate concentrations. Partial least-squares regression (PLSR) analysis revealed a positive relationship between most hydrolysate properties and the degree of hydrolysis. These findings indicated that using US pretreatments would be a promising procedure for improving the functional, structural, and antioxidant properties of hybrid sturgeon hydrolysates, thereby expanding their application in the field of food processing.

In vitro evaluation of antiatherogenic potential of Origanum × paniculatum, Lippia alba, Clinopodium nepeta, and Eucalyptus globulus essential oils

IntroductionEssential oils (EOs) are mixtures of secondary plant metabolism compounds with potential lipid-lowering activity and antioxidant properties. They may therefore be considered as add-on therapy for the prevention and treatment of cardiovascular diseases. MethodsEOs of Origanum × paniculatum (OpEO), Lippia alba (chemotype linalool) (LaLEO), Clinopodium nepeta (CnEO) and Eucalyptus globulus (EgEO), were evaluated on THP-1 foam cell macrophages as a cellular model of atherogenesis. Cell viability was determined by the MTT assay, lipid content by thin-layer chromatography, and Oil Red O staining. Antioxidant activity was evaluated by TBARS on low-density lipoprotein lipid peroxidation, and by ABTS•+, DPPH•, and FRAP assays. EOs were characterised by gas chromatography–mass spectrometry and a predictive multivariate data analysis was performed by partial least square regression (PLS) to identify the components related to antiatherogenic potential. ResultsOpEO reduced foam cell viability (IC50 = 45 µl/l) and cholesterol and triacylglycerol content and demonstrated antioxidant activity. In contrast, LaLEO increased lipid content and showed a prooxidant capacity with low reduction of cell viability (IC50 = 340 µl/l). CnEO showed prooxidant activity and reduction of cell viability (IC50 = 130 µl/l) without changes in lipid content. EgEO showed antioxidant activity, lipid increase, and low cytotoxicity (IC50 = 770 µl/l). PLS analysis suggests that cis-sabinene hydrate is responsible for cell viability inhibition, thymol and β-cis-terpineol decrease lipid content and possess antioxidant activity, and linalool increases lipid levels and has prooxidant capacity. ConclusionsOpEO is a promising natural product for therapy against atherosclerosis, with cis-sabinene hydrate, thymol, and β-cis-terpineol as potential active principles.

Quartz-Enhanced Photoacoustic Spectroscopy Assisted by Partial Least-Squares Regression for Multi-Gas Measurements.

We report on the use of quartz-enhanced photoacoustic spectroscopy (QEPAS) for multi-gas detection. Photoacoustic (PA) spectra of mixtures of water (H2O), ammonia (NH3), and methane (CH4) were measured in the mid-infrared (MIR) wavelength range using a mid-infrared (MIR) optical parametric oscillator (OPO) light source. Highly overlapping absorption spectra are a common challenge for gas spectroscopy. To mitigate this, we used a partial least-squares regression (PLS) method to estimate the mixing ratio and concentrations of the individual gasses. The concentration range explored in the analysis varies from a few parts per million (ppm) to thousands of ppm. Spectra obtained from HITRAN and experimental single-molecule reference spectra of each of the molecular species were acquired and used as training data sets. These spectra were used to generate simulated spectra of the gas mixtures (linear combinations of the reference spectra). Here, in this proof-of-concept experiment, we demonstrate that after an absolute calibration of the QEPAS cell, the PLS analyses could be used to determine concentrations of single molecular species with a relative accuracy within a few % for mixtures of H2O, NH3, and CH4 and with an absolute sensitivity of approximately 300 (±50) ppm/V, 50 (±5) ppm/V, and 5 (±2) ppm/V for water, ammonia, and methane, respectively. This demonstrates that QEPAS assisted by PLS is a powerful approach to estimate concentrations of individual gas components with considerable spectral overlap, which is a typical scenario for real-life adoptions and applications.

In-depth exploration on the effects of organic nitrogen addition during different fermentation periods on the quality of cider

Nitrogen is an essential element for the growth and metabolism of Saccharomyces cerevisiae, and has a great impact on the quality of cider. In this study, the effects of organic nitrogen addition at different stages of fermentation process on the quality of cider were systematically explored. The main projects included the analysis of fermentation rate, yeast growth, organic acids, amino acids, and volatile aroma compounds. The results indicated that organic nitrogen addition speeded up fermentation rate and shortened fermentation time. The organic nitrogen addition before fermentation and at the early stationary phase altered the composition of organic acids in cider. Moreover, with the addition of organic nitrogen at three stages of fermentation (pre-fermentation, logarithmic and stable phases of yeast growth), the total concentrations of volatile aroma substances in ciders were 10.57 mg/L, 11.97 mg/L and 11.05 mg/L respectively, which were all significantly higher than that in control group (9.69 mg/L). A close relationship between free amino acids consumed during fermentation and volatile aroma compounds produced was revealed by partial least-squares regression analysis. Overall, this study provided a new strategy for cider quality improvement by evaluating the effects of organic nitrogen on the aroma composition of cider.

Detection of Toxic Contaminants in Alcohol-Based Hand Sanitizers Using Infrared Spectroscopy

Recent fatalities due to methanol and 1-propanol toxicity in alcohol-based hand sanitizers has led the U.S. Food and Drug Administration (FDA) to ban 594 different hand sanitizer brands. The FDA also introduced methanol testing in alcohol-based hand sanitizers before allowing the products to enter the United States This requirement creates a need for inexpensive, rapid, and portable testing methods to measure methanol and 1-propanol concentrations in alcohol-based hand sanitizers. Here we study the performance of infrared (IR) spectroscopy for measuring methanol and 1-propanol concentrations in an alcohol-based hand sanitizer, and compare the performance of two portable spectrometers, Texas Instruments near-infrared (NIR) spectrometer (TI NIR) and NeoSpectra mid-infrared (MIR) spectrometer. The IR absorbance spectra were measured in transmission mode at different path lengths for 52 different hand sanitizer samples spiked with 0%–1% v/v concentrations of methanol and 1-propanol. A partial least-squares regression analysis shows ability to detect contaminant concentrations with a correlation coefficient of determination ( r2) up to 0.99 and root mean square error of prediction as low as 0.34% v/v.

α-Galactosidase interacts with persistent organic pollutants to induce oxidative stresses in rice (Oryza sativa L.)

Persistent organic pollutants (POPs) in agricultural soil often triggered metabolic alterations and phytotoxicity in plants, ultimately threatening crop quality. Unraveling the phytotoxic mechanisms of POPs in crops is critical for evaluating their environmental risks. Herein, the molecular mechanism of POP-induced phytotoxicity in rice (Oryza sativa L.) was analyzed using metabolic profile, enzyme activity, and gene expression as linkages, including polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers, polychlorinated biphenyls, and phthalate esters. Despite no observable changes in phenotypic traits (e.g., biomass and length of aboveground), the levels of reactive oxygen species (ROS) were promoted under stresses of the tested POPs, particularly 2,2′,4,4′-tetrabromodiphenyl ether (BDE-47), dibutyl phthalate (DBP), and di(2-ethylhexyl) phthalate (DEHP). Metabolomics analysis revealed that ROS contents positively correlated with metabolic perturbation levels (r = 0.83), among which the galactose metabolism was significantly inhibited when exposed to DBP, DEHP, or BDE-47. The α-Galactosidase (α-Gal) involved in galactose metabolism was targeted as the key enzyme for the phytotoxicity of DBP, DEHP, and BDE-47, which was revealed by the inhibition of saccharide levels (45.5−82.1%), the catalytic activity of α-Gal (18.5−24.3%), and the gene expression (28.5−34.5%). Molecular docking simulation suggested that the three POPs occupied the active sites of α-Gal and formed a stable protein-ligand complex, thus inhibiting the catalytic activity of α-Gal. Partial least-squares regression analysis indicated that α-Gal activity was negatively associated with hydrogen bond acceptor, rotatable bond, and topological polar surface area of POPs. The results offered novel insights into the molecular mechanisms of phytotoxicity of POPs and provided important information for evaluating the environmental risk of POPs.

E-nose and Attenuated Total Reflectance-Fourier Transform Infrared data to estimate the shelf-life of fresh-cut Barattiere packaged in polypropylene or in biodegradable polylactic acid bags

Fresh-cut Barattiere was packaged in polylactic acid or polypropylene bags for 11 days at 4 °C. According to the quality traits and the volatile organic compounds (VOCs) profiles, fruit resulted marketable up to 7 days, irrespective of the packaging material. A significant relationship between VOCs and Electronic-nose sensors (S) was achieved, highlighting that the sensor S5 (sensitive to hydrocarbons and aromatic compounds) and the related VOCs (1-nonanol and cis-6-nonenol) can be indicators of freshness, while the sensors S2 (broadrange), S4 (sensitive to hydrogen) and S6 (broad-methane), correlated to some specific esters, can be associated to the stored products. Partial least square regression (PLSR) analysis performed on E-nose sensors allowed to obtain a model (R2 = 0.84 in validation) to estimate the storage days of fresh-cut Barattiere. PLSR analysis was also carried on Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) data obtaining a predictive model with higher performance (R2 = 0.91 in validation). In conclusion, both techniques were able to make suitable predictions of fresh-cut Barattiere shelf-life.

Study on the Fingerprint Spectrum and the Spectrum-Effect Relationship of Analgesic and Anti-Inflammatory Effects of the Aqueous Extract from Dalbergia hancai Benth.

Dalbergia hancai Benth. (D. hancai) is one of the most frequently utilized traditional Chinese medicine in Zhuang medicine. Simultaneously, it has been included in the "Quality Standard of Zhuang medicine in Guangxi Zhuang Autonomous Region (Vol. 2)" and possessed outstanding pharmacological effects. However, the pharmacodynamic material basis of D. hancai still remains unclear. In this study, the high-performance liquid chromatography (HPLC) method had been employed to establish the fingerprint of 10 batches of aqueous extract of D. hancai originated from different parts of China. At the same time, similarity evaluation, cluster analysis, and principal component analysis (PCA) had also been conducted to evaluate the common peaks. The acetic acid-induced writhing in mice had been employed as an analgesic model, and the carrageenan-induced toe swelling in mice was utilized as an anti-inflammatory model for pharmacodynamic experiments. The gray relational analysis (GRA) and partial least squares regression (PLSR) were applied to correlate the fingerprint and pharmacodynamic data to thoroughly examine its spectrum-effect relationship, whereby its analgesic and anti-inflammatory material basis had been comprehensively explored. The results revealed that the HPLC fingerprint of the aqueous extract of D. hancai had successfully identified 12 common peaks whereby two of which were further identified as protocatechuic acid and vitexin. Subsequently, through the analysis of GRA and PLSR, the chromatographic peaks that possess a critical correlation degree with the analgesic and anti-inflammatory effects of D. hancai had also been successfully discovered. Ultimately, the analgesic and anti-inflammatory effects of the 10 batches of D. hancai aqueous extract had been conclusively proved, and it was evidently indicated that these effects were attributable to the synergistic interactions between various components. Therefore, this study aims to serve as an effective analytical method for screening and predicting the effective substances of traditional Chinese medicine on the basis of the spectrum-effect relationship.