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Coconut water (CW) is a popular and healthful beverage, and ensuring its quality is crucial for consumer satisfaction. This study aimed to explore the potential of near-infrared spectroscopy (NIRS) and chemometric methods for analyzing CW quality and distinguishing samples based on postharvest storage time, cultivar, and maturity. CW from nuts of Wenye No. 2 and Wenye No. 4 cultivars in China, with varying postharvest storage time and maturities, were subjected to NIRS analysis. Partial least squares regression (PLSR) models were developed to predict reducing sugar and soluble sugar contents, revealing moderate applicability but lacking accuracy, with the residual prediction deviation (RPD) values ranging from 1.54 to 1.83. Models for TSS, pH, and TSS/pH exhibited poor performance with RPD values below 1.4, indicating limited predictability. However, the study achieved a total correct classification rate exceeding 95% through orthogonal partial least squares discriminant analysis (OPLS-DA) models, effectively discriminating CW samples based on postharvest storage time, cultivar, and maturity. These findings highlight the potential of NIRS combined with appropriate chemometric methods as a valuable tool for analyzing CW quality and efficiently distinguishing samples. NIRS and chemometric techniques enhance quality control in coconut water, ensuring consumer satisfaction and product integrity.

The aim of the present study was to investigate the effect of glycosylation with sugar beet pectin (SBP) on the interfacial behaviour and emulsifying ability of coconut protein (CP). The physical stabilities of the emulsions were predicted by transmission variation, droplet distribution and zeta potentials. The results showed that SBP-CP-stabilized emulsions showed better stability during centrifugation than those stabilized by CP because SBP-CP reduced the degree of variation in the CP transmission profile. The adsorption kinetics of all emulsifiers at the oil-water interface were determined to investigate the relationship between the interfacial behaviour and emulsion stability. The presence of SBP considerably reduced the adsorption rate of CP (0.698 mN/m/s1/2) and hampered the development of a highly viscoelastic network at the oil-water interface. The values of the dilatational elastic modulus (Ed = 19.477 mN/m) and dilatational viscous modulus (E = 19.719 mN/m) were approximately equal, indicating that the adsorption process was mainly dominated by elastic behaviour. Additionally, the SBP-CP interaction enhanced the dilatational property of the CP-absorbed layer.

Wheat (Triticum aestivum L.) carries three waxy loci (Wx-A1, Wx-B1, and Wx-D1) encoding granule-bound starch synthase (GBSS) which are related to amylose synthesis. The present study was to investigate the possibility of using near-infrared (NIR) hyperspectral spectroscopy to differentiate waxy wheat and three partial waxy wheats from wild-type wheat. Nearly 267 seeds from the same harvest year were used to obtain hyperspectral imaging maps in the near-infrared range (930–2548 nm), and then the data were analyzed based on chemometric methods. The first derivative, standard normal variable (SNV) transformation, and multivariate scattering correction (MSC) were used for spectral pretreatment. Support vector machine (SVM) and partial least square discriminant analysis (PLS-DA) and backpropagation neural network (BPNN) were applied to build discrimination models for wheat line classification. The results demonstrate that the highest classification accuracy of 98.51% for the prediction set was achieved by SVM model based on raw spectral data in full NIR region, and the classification accuracy of wild-type and partial waxy wheats all reached 100%. SVM models’ prediction accuracy (98.51%) is higher than PLS-DA and BPNN models’ (75.76% and 82.10%). The above results show that NIR hyperspectral spectroscopy combined SVM model was useful in identification of waxy and partial waxy wheats from wild-type wheat.

Washing rice water (WRW) refers to the sewage produced by rice washing in China and other parts of Asia people's daily life. As in the WRW is rich a variety of nutrients, microorganisms are prone to multiply and pollute the environment. In this article, high-throughput sequencing is used to describe the microbial diversity in different fermentation time WRW. The results showed that the sequencing depth effectively covered the microbial species in the samples, and the bacterial community structure in the samples of WRW at different fermentation periods was rich in diversity. Preominant taxa included Proteobacteria (62%), Firmicutes (28%), approximately Cyanobacteria (10%) and Bacteroidetes (0.5%). The core WRW microbiome comprises Trabulsiella, Pseudomonas, Serratia, Lactobacillus, Erwinia, Enterobacter, Clostridium and Acinetobacter, some of which are potential beneficial microbes. The change of microbial community composition with the change of habitat was assessed. It was found that environmental factors had significant influence on the assembly structure of microbial community.

This work investigated the effects of hot air drying pretreatment (HAD), freeze drying pretreatment (FD) and vacuum drying pretreatment (VD) on the physicochemical properties and structural characterizations of starch isolated from canistels. X-ray diffraction displayed that the starches separated from canistel by different drying pretreatments showed a typical A-type crystal structure. The SEM image showed that cracks and debris appeared on the surface of HVD and VD particles. The molecular structure of starches obtained by different drying pretreatments was studied using Fourier infrared and solid state 13C CP/MAS NMR analysis. The results indicated that vacuum drying pretreatment could promote the formation of the double helix of starch granules, and hot air drying and freeze drying destroyed the ordered structure of starch granules. These structural changed to affect the physicochemical properties of starch granules. The study of different drying pretreatments to separate starches provided practical value for drying pretreatments. Furthermore, the current study affords information for canistel starches cultivated in China that would be convenient for commercial applications.

Polyphenol can improve osteoporosis and is closely associated with gut microbiota, while the mechanism and the relationship among polyphenol, osteoporosis, and gut microbiota colonization remain unclear. Here, an osteoporosis rat model established by ovariectomy was employed to investigate the improving mechanism of arecanut (Areca catechu L.) seed polyphenol (ACP) on osteoporosis by regulating gut microbiota. We analyzed the bone microstructure, Paneth cells, regulating microbial protein (lysozyme (LYZ)), proinflammatory cytokines, macrophage infiltration levels, and gut microbial communities in a rat. ACP improved the trabecular microstructure compared to OVX, including the increased trabecular number (Tb.N) (P < 0.01) and trabecular thickness (Tb.Th) (P < 0.001) and decreased trabecular separation (Tb.Sp) (P < 0.01). At the phylum level, Bacteroidetes was increased after ovariectomy (P < 0.001) and Firmicutes and Proteobacteria were increased in ACP (P < 0.001). Antiosteoporosis groups with lower LYZ and Paneth cells (P < 0.001) showed that the microbiota Alistipes, which have a negative effect on bone metabolism were decreased in ACP (P < 0.001). Altogether, these studies showed that the estrogen deficiency could induce the shedding of Paneth cells, which leads to the decrease of LYZ, while ACP could increase the LYZ expression by maintaining the population of Paneth cells in an estrogen-deficient host, which were implicated in gut microbiota regulation and improved osteoporosis by controlling the inflammatory reaction.

To assess the freshness of tilapia fillets rapidly and nondestructively, near-infrared (NIR) spectroscopy technology in the spectral range from 900 to1700 nm was conducted by means of a portable spectrometer to determine the total volatile basic nitrogen (TVB-N) value of tilapia fillets stored at 4 °C. To establish a robust and high predictive NIR model between the collected NIR spectra and measured TVB-N values, a series of chemometric methods, including outlier elimination, spectral preprocessing, and optimal wavelength selection, were conducted. First, outlier elimination was utilized continuously by two times for the removement of the outlier samples. The results illustrated that outlier elimination was effective and useful to reduce outliers and develop the robust models. Second, spectral preprocessing was executed based on five preprocessing methods as well as their hybrid methods to pretreat the spectral data, and smoothing-first derivative (SM-D1) was proved to be the most effective methodology as result of its highest R2p and lowest RMSEP. Finally, optimal wavelength selection was carried out by several variable selection methods to establish simpler models with better predictive performance. It was manifested that the combination of iPLS-mVCPA-IRIV was proved to be the most reliable and outstanding method based on the R2p of 0.9201 and RMSEP of 2.0907. In general, it has been proved that NIR spectroscopy technique has the ability to measure TVB-N content of fillets for the evaluation of fish freshness in a rapid and nondestructive manner.

Pseudomonas fragi is primarily responsible for the spoilage of various foods, especially meat. The aim of this study was to investigate the antibacterial mechanism of 3-carene against P. fragi. 3-Carene treatment decreased the phospholipid content and the fluidity of the cell membrane, induced reactive oxygen species (ROS) generation and affected respiratory chain dehydrogenase, oxoglutarate dehydrogenase and citrate synthase in P. fragi. Metabolomics and proteomics analyses further showed that in the presence of 3-carene, 519 proteins, 136 metabolites in positive ion mode and 100 metabolites in negative ion mode were differentially expressed. These proteins and metabolites were primarily involved in amino acid metabolism, fatty acid degradation, the tricarboxylic acid cycle (TCA cycle) and other processes. Consequently, the stimulation of 3-carene altered cell membrane properties, disturbed important amino acid and energy metabolism, and even caused oxidative stress. Additionally, the results of total viable counts and the total volatile base nitrogen indicated that 3-carene could significantly improve the preservation of refrigerated pork. This study suggested that 3-carene has promising potential to be developed as a food preservative.

Background With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. Results We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. Conclusion BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

Lipophilicity, as evaluated by the n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4), is a major determinant of various absorption, distribution, metabolism, elimination, and toxicology (ADMET) parameters of drug candidates. In this study, we developed several quantitative structure–property relationship (QSPR) models to predict log D7.4 based on a large and structurally diverse data set. Eight popular machine learning algorithms were employed to build the prediction models with 43 molecular descriptors selected by a wrapper feature selection method. The results demonstrated that XGBoost yielded better prediction performance than any other single model (RT2 = 0.906 and RMSET = 0.395). Moreover, the consensus model from the top three models could continue to improve the prediction performance (RT2 = 0.922 and RMSET = 0.359). The robustness, reliability, and generalization ability of the models were strictly evaluated by the Y-randomization test and applicability domain analysis. Mor...

Partridge tea (Mallotus oblongifolius (Miq.) Müll.Arg.) is a local characteristic tea in Hainan, the southernmost province of China, and the quality of partridge tea may be affected by the producing areas. In this study, stable isotope and targeted metabolomics combined chemometrics were used as potential tools for analyzing and identifying partridge tea from different origins. Elemental analysis—stable isotope ratio mass spectrometer and liquid chromatography-tandem mass spectrometrywas used to analyze the characteristics of C/N/O/H stable isotopes and 54 chemical components, including polyphenols and alkaloids in partridge tea samples from four regions in Hainan (Wanning, Wenchang, Sanya and Baoting). The results showed that there were significant differences in the stable isotope ratios and polyphenol and alkaloid contents of partridge tea from different origins, and both could accurately classify partridge tea from different origins. The correct separation and clustering of the samples were observed by principal component analysis and the cross-validated Q2 values by orthogonal partial least squares discriminant analysis (OPLS-DA) were 0.949 (based on stable isotope) and 0.974 (based on polyphenol and alkaloid), respectively. Potential significance indicators for origin identification were screened out by OPLS-DA and random forest algorithm, including three stable isotopes (δ13C, δ D, and δ18O) and four polyphenols (luteolin, protocatechuic acid, astragalin, and naringenin). This study can provide a preliminary guide for the origin identification of Hainan partridge tea.

Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission spectroscopic technique for multi-elemental analysis and has the advantages of real-time multi-element measurement, minimal sample preparation and remote detection. However, the quality of LIBS data can be low due to matrix effects and signal uncertainty which hinders the wide application of LIBS. Recent studies attempt to improve the performance of LIBS quantitative analysis using linear and nonlinear multivariate analysis models. Linear models can easily present how important variables contribute to the prediction but suffer from performance degradation if data has a high degree of nonlinearity. Nonlinear models tend to have good performance, but lack simple and intuitive variable interpretability and are prone to overfitting. Moreover, neither linear nor nonlinear models used in LIBS quantitative analysis are originally designed to explicitly consider the domain knowledge of atomic spectroscopy. In this work, a new machine learning algorithm is proposed, namely spectral knowledge-based regression (SKR), which integrates linear and nonlinear models to improve the performance of LIBS quantitative analysis. The linear model is knowledge-driven and built on key variables correlated with analyte composition. While the nonlinear model is data-driven and transforms the input data into a kernel matrix. The proposed SKR is tested on 18 quantification tasks in 4 LIBS datasets and compared with 5 baseline methods. It yields the lowest and the second-lowest root-mean-square errors in 12/18 and 2/18 tasks, respectively. Moreover, SKR intuitively explains the contribution of key variables to prediction and has the same low computational complexity as ridge regression. These results demonstrate that SKR inherits the high accuracy of nonlinear modelling and the simple variable interpretability of linear models. Therefore, it can serve as a promising candidate for improving the accuracy and reliability of LIBS quantitative analysis.

β-Cyclodextrin (β-CD) was modified using octenyl succinic anhydride (OSA) to introduce amphiphilic groups (hydrophilic carboxyl and lipophilic octenyl chains) by esterification under alkaline conditions. The FT-IR results indicated that the OSA-modified β-CD (OCD) showed new absorption peaks of an ester bond and a carboxylate (RCOO-) at 1724 and 1570 cm-1, respectively, confirming the successful preparation of OCD. Then the embedding effects of β-CD and OCD on β-carotene and the emulsifying and antioxidant properties of their inclusion complexes were evaluated. The results of XRD showed that the β-CD (or OCD)/β-carotene inclusion complexes were converted from a cage-type structure to a channel-type structure. AFM and SEM showed that the crystal characteristics and surface morphologies of the inclusion complexes were different from those of the physical mixture. The emulsion stabilized by OCD exhibited smaller droplet sizes and larger zeta-potentials than that stabilized by β-CD. In addition, the inclusion complexes-prepared emulsion exhibited lower POV values and TBARS contents than did the physical mixture. OCD/β-carotene inclusion complexes can improve the physical and oxidative stability of the emulsion, which is of great significance to the food industry.

A ferulic acid-sugar beet pulp pectin complex (FA-SBPP) was prepared using an immobilized enzyme (Novozym 435®) as the catalyst at 60 °C in a vacuum-controlled system. The structure of FA-SBPP was characterized by FT-IR and NMR (1H and 13C). In addition, the antioxidant activity of FA-SBPP was evaluated according to the DPPH free radical scavenging ability and ORAC values. Moreover, the physical and oxidation stability of the emulsion was evaluated by particle size distribution, cream index (CI), peroxide value (POV), and 2-thiobarbituric acid reactive substance (TBARS) formation. The results showed that esterification between FA and SBPP was confirmed, and the reaction mainly occurred at the C-2, C-3, and C-6 positions. When the concentration was 1.0 mg/mL, the DPPH scavenging activity and total antioxidant activity of FA-SBPP-3 were 80.03% and 355.72 μmol TE/g, respectively. Compared with SBPP, FA-SBPP-stabilized emulsions exhibited significant smaller droplet sizes and lower CIs, POVs and TBARS amounts. Thus, the introduction of FA changed not only the chemical reactivity but also the polarity of SBPP, thereby improving its antioxidant ability and affinity for the oil-water interface. Thus, we provide a multifunctional stabilizer that can reduce the dose of antioxidants or even replace them in oil-in-water emulsions.

When analyzing high-dimensional near-infrared (NIR) spectral datasets, variable selection is critical to improving models' predictive abilities. However, some methods have many limitations, such as a high risk of overfitting, time-intensiveness, or large computation demands, when dealing with a high number of variables. In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA-iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP-GA) were used to investigate the improvement of VCPA-based hybrid strategy. The results show that VCPA-GA and VCPA-IRIV significantly improve model's prediction performance when compared with other methods, indicating that the modified VCPA step is a very efficient way to filter the uninformative variables and VCPA-based hybrid strategy is a good and promising strategy for variable selection in NIR. The MATLAB source codes of VCPA-GA and VCPA-IRIV can be freely downloaded in the website: https://cn.mathworks.com/matlabcentral/profile/authors/5526470-yonghuan-yun.

With the advances in innovative instrumentation and various valuable applications, near-infrared (NIR) spectroscopy has become a mature analytical technique in various fields. Variable (wavelength) selection is a critical step in multivariate calibration of NIR spectra, which can improve the prediction performance, make the calibration reliable and provide simpler interpretation. During the last several decades, there have been a large number of variable selection methods proposed in NIR spectroscopy. In this paper, we generalize variable selection methods in a simple manner to introduce their classifications, merits and drawbacks, to provide a better understanding of their characteristics, similarities and differences. We also introduce some hybrid and modified methods, highlighting their improvements. Finally, we summarize the limitations of existing variable selection methods, providing our remarks and suggestions on the development of variable selection methods, to promote the development of NIR spectroscopy.

Heart failure (HF) has become an important social problem that seriously threatens human health due to its high morbidity and mortality. G Protein-Coupled Receptors Kinase 2 (GRK2), as a novel target of heart failure, is overexpressed in the pathogenesis and progression of HF. In this study, we conducted a multistep virtual screening process from the ZINC database to discover potential GRK2 inhibitors. First, by using 6 known GRK2 inhibitors with high bioactivity and diverse structures as the template molecules, 7499 compounds were obtained from ligand-based similarity screening by 4 types of fingerprints. Subsequently, these hits were further screened by three quantitative structure-activity relationship (QSAR) models, which were built by 197 known GRK2 compounds and represented by two-dimensional MOE descriptors (MOE2D), molecular fragments (MACCS) and 2- dimensional pharmacophore (CATS) based on random forest algorithm, respectively. These hit compounds were then filtered by three molecular docking packages (MOE, GOLD and GLIDE) to improve the docking accuracy for avoid losing candidate compounds. The compounds with high docking scores were selected for the evaluation of absorption, distribution, metabolism, excretion, and toxicity (ADMET). Finally, 17 compounds were identified the binding affinity to GRK2 by using SPR assay, three of which were considered as novel GRK2 inhibitors, and their binding modes with GRK2 active sites were analyzed. As new potential GRK2 inhibitors, COM5(KD = 0.172 μM) and COM17(KD = 0.595 μM) with the KD value less than 1 μM could be directly used in the next study, while the remaining hit COM4 (KD = 4.72 μM) whose KD over 1 μM can be optimized into the required low nanomole range for further study.

In this study, linalool, one of the principal components of essential oils, was used as an antibacterial agent to investigate the antibacterial activity and mechanism of linalool against Pseudomonas fluorescens. The reduction in membrane potential (MP), leakage of alkaline phosphatase (AKP) and the release of macromolecules, including DNA, RNA and protein confirmed that damage to cell wall membrane structure and leakage of cytoplasmic contents were due to the linalool treatment. Furthermore, the decrease of enzyme activity, including the succinate dehydrogenase (SDH), malate dehydrogenase (MDH), pyruvate kinase (PK), and ATPase indicated that linalool could lead to metabolic dysfunction and inhibit energy synthesis. In addition, the activity of respiratory chain dehydrogenase and metabolic activity of respiration indicated that linalool inhibits cellular respiration. These results revealed that linalool had strong antibacterial activity against P. fluorescens via membrane damage, bacterial metabolic and oxidative respiratory perturbations, interfering in cellular functions and even causing cell death. It was suggested that linalool may be a new potential source as food antiseptics in food systems.

Based on the exchange of the DNA duplex-quadruplex, a multifunctional Pb(II) ion probe was fabricated, in which Pb(II) ion-driven DNA duplexes act as a bridge to link CdSe/ZnS-luminol and Fe3O4 nanoparticles. As Pb(II) ion owns a strong binding capacity for G-quadruplex (G4), it can unwind the DNA duplex and release its complementary strand. In this strategy, three detection modes, chemiluminescence (CL), fluorescence (FL) assay and colorimetric assay (CA), can be realized via DNA duplex bridged CdSe/ZnS and Fe3O4 nanocomposites. Amongst, FL assay shows the best detection performance, and it exhibits a linear range of 10 nM to 10 µM, as well as the limit of detection (LOD) with 1.63 nM (n=3, ±3.5%). In the selective analysis, Pb(II) ion can be specifically detected in the nine different interfering metal ions. Besides, the magnetic probe requires only one “mixing and separation” operation and greatly improves the detection efficiency. The three assays can be combined as one method to achieve a simple and rapid feature. Such a three-in-one analytical platform, therefore, can serve as a powerful tool for selective, accurate and sensitive detection of Pb(II) ion.

Dendrobium officinale, a precious herbal medicine, has been used for a long time in Chinese history. The metabolites of D. officinale, regarded as its effective components to fight diseases, are significantly affected by cultivation substrates. In this study, ultra-performance liquid chromatography mass spectrometry (UPLC-MS/MS) was conducted to analyze D. officinale stems cultured in three different substrates: pine bark (PB), coconut coir (CC), and a pine bark: coconut coir 1:1 mix (PC). A total of 529 metabolites were identified. Multivariate statistical analysis methods were employed to analyze the difference in the content of metabolites extracted from different groups. By the criteria of variable importance in projection (VIP) value ≥1 and absolute log2 (fold change) ≥1, there were a total of 68, 51, and 57 metabolites, with significant differences in content across groups being filtrated out between PB and PC, PB and CC, and PC and CC, respectively. The comparisons among the three groups revealed that flavonoids were the metabolites that fluctuated most. The results suggested the D. officinale stems from the PB group possessed a higher flavonoid content. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that the significantly regulated metabolites were mainly connected with flavonoid biosynthesis. A comprehensive profile of the metabolic differentiation of D. officinale planted in different substrates was provided, which supports the selection of an optimum cultivation substrate for a higher biomass yield of D. officinale.

Vitamin E (VE) and &beta, cyclodextrin (&beta, CD) can form an inclusion complex, however, the inclusion rate is low because of the weak interaction between VE and &beta, CD. The results of a molecular docking study showed that the oxygen atom in the five-membered ring of octenyl succinic anhydride (OSA) formed a strong hydrogen bond interaction (1.89 &Aring, ) with the hydrogen atom in the hydroxyl group of C-6. Therefore, &beta, CD was modified using OSA to produce octenyl succinic-&beta, cyclodextrin (OCD). The inclusion complexes were then prepared using OCD with VE. The properties of the inclusion complex were investigated by Fourier-transform infrared spectroscopy (FT-IR), 13C CP/MAS NMR, scanning electron microscopy (SEM), and atomic force microscopy (AFM). The results demonstrated that VE had been embedded into the cavity of OCD. Furthermore, the emulsifying properties (particle size distribution, &zeta, potential, and creaming index) of the OCD/VE inclusion-complex-stabilized emulsion were compared with that stabilized by &beta, CD, OCD, and an OCD/VE physical mixture. The results showed that the introduction of the OS group and VE could improve the physical stability of the emulsion. In addition, the OCD/VE inclusion complex showed the strongest ability to protect the oil in the emulsion from oxidation. OCD/VE inclusion complex was able to improve the physical and oxidative stability of the emulsion, which is of great significance to the food industry.

Lipophilicity, as evaluated by the

MCPA (4-chloro-2-methylphenoxyacetic acid) is an acidic herbicide, widely used in paddy fields. The presence of dissolved organic matter (DOM) modifies the sorption-desorption of herbicides in soils. In this study, effects of DOM on sorption- desorption of MCPA were tested using three typical ferralsol soil types from China: rhodic ferralsol, haplic ferralsol and paddy soil. DOM preparations were extracted from the paddy soil (DOMP), from a compost mixture of cassava stems with chicken manure (DOMC), and from rice straw (DOMR). Sorption-desorption of MCPA in the tested soil types was shown to follow pseudo first-order kinetics, and the calculated isotherm data fitted well with a Freundlich equilibrium model in the range of the studied concentrations. MCPA was weakly sorbed by the soils, producing low Freundlich coefficient values (Kf) (0.854 to 4.237). The presence of DOM reduced the Kf whereby DOMC had the strongest and DOMR the weakest effect. Presence of DOM also promoted MCPA desorption from the soils, again with DOMC having the strongest effect and DOMR the weakest. DOM coating changed the soil particle surface, as demonstrated by electron microscopy, and DOM also directly interacted with MCPA, as shown by Fourier-transform infrared spectroscopy. The experimental data were interpreted to suggest a competing sorption of DOM to ferralsol and an increased solubility of MCPA in the presence of DOM. The results indicate that the environmental risk of MCPA leaching to groundwater and surface flow is increased by presence of DOM, for instance as a result of organic fertilizer use.

The behavior of a chemical in human or environment mostly depends on its several key physicochemical properties, such as aqueous solubility, octanol-water partition coefficient (logP), boiling point (BP), density, flash point (FP), viscosity, surface tension (ST), vapor pressure (VP) and melting point (MP). Commonly, these properties are important for the environmental sciences and drug discovery, such as the absorption, distribution, metabolism, excretion, and toxicity (ADMET) for medicinal compounds and the common risk assessment for problematic chemicals. At present, the quantitative structure-property relationship (QSPR) model was widely applied to save time and money investment in the early stage of chemical research. Although some satisfactory models were already obtained, most of them are not available for the public researchers and thus cannot be directly applied to practical research projects. Herein, in this study, we developed a user-friendly web server named ChemBCPP that can be used to predict aforementioned 8 important physicochemical properties and calculate several other commonly used properties just by uploading a molecular structure or file. In addition, for a new chemical entity, users can not only get its predicted value but also obtain a leverage value (h value) which can be used to evaluate the reliability of predictive result. We believe that ChemBCPP could be widely applied in environmental science, chemical synthesis and drug ADMET fields with the demand for high quality of chemical properties. ChemBCPP could be freely available via http://chembcpp.scbdd.com .

The potential of two different hyperspectral imaging systems (visible near infrared spectroscopy (Vis-NIR) and NIR) was investigated to determine TVB-N contents in tilapia fillets during cold storage. With Vis-NIR and NIR data, calibration models were established between the average spectra of tilapia fillets in the hyperspectral image and their corresponding TVB-N contents and optimized with various variable selection and data fusion methods. Superior models were obtained with variable selection methods based on low-level fusion data when compared with the corresponding methods based on single data blocks. Mid-level fusion data achieved the best model based on CARS, in comparison with all others. Finally, the respective optimized models of single Vis-NIR and NIR data were employed to visualize TVB-N contents distribution in tilapia fillets. In general, the results showed the great feasibility of hyperspectral imaging in combination with data fusion analysis in the nondestructive evaluation of tilapia fillet freshness.

In recent years, there have been growing concerns about quality evaluation of predictions of developed quantitative structure-activity relationship (QSAR) models. Well-defined applicability domain (AD) is very crucial in the validation of QSAR models as stated in the third principle of Organization for Economic Co-operation and Development (OECD). In this study, a new perspective on defining AD of model based on population analysis (PA) strategy, including model population analysis (MPA) and approach population analysis (APA), was proposed. MPA employed classical AD approaches to define AD with a vast amount of sub-datasets derived from training set. On the basis of MPA, the classical AD approaches could distinguish part of the samples that cannot be distinguished by full training samples. APA was then used to get a union of all results generated by the used AD approaches to give a consensus list of samples as falling outside the AD. In order to investigate the performance of PA strategy in defining AD with the classical AD approaches, two QSAR datasets were used. The results show that implementing PA strategy can assist three classical AD approaches to distinguish the additional samples that cannot be distinguished using full training dataset. When excluding the additional samples, the root mean square error of prediction of test set decreased, suggesting that PA strategy has a potential to distinguish the samples that cannot be reliably predicted.

The aim of this study was to prepare sodium alginates (SAs) with different molecular weight and G/M ratio, and characterize their rheological behaviors and emulsifying properties. The result of Fourier transform infrared (FTIR) showed that the chemical bonds among the &beta, d-mannuronic acid- (M-), &alpha, l-guluronic acid- (G-), and MG-sequential blocks in the SA chains were not changed significantly by acid treatment. Meanwhile, the molecular weight and G/M ratio of the SA exhibited drastic variation after acid modification. The result of rheological analysis suggesting that the apparent viscosity of SA reduced from 30 to 16.4 mPa.s with the increase of shear rate, reveals that SA solution belongs to pseudoplastic liquid. Also, the apparent viscosity of acid-modified SA solution dropped rapidly with the decrease of the molecular weight. The properties of emulsions stabilized by SA, SA-Ms, and commercial SAs were evaluated via the interface tensiometry and determination of the zeta potential, droplet size, creaming index (CI), and Turbiscan stability index (TSI). Compared with the SA-stabilized emulsion, the interfacial tension of the emulsion stabilized by SA-M increased with the decrease of the molecular weight reduced at the similar M/G ratio. The decrease in zeta potential and the increase in TSI of the emulsion were observed with the decrease of molecular weight, indicating that molecular weight plays an important role on the emulsifying ability of SA. In addition, the SA with low G/M ratio can form emulsions with stable and fine droplets.

Tender coconut water tastes sweet and is enjoyed by consumers, but its commercial development is restricted by an extremely short shelf life, which cannot be explained by existing research. UPLC-MS/MS-based metabolomics methods were used to identify and statistically analyze metabolites in coconut water under refrigerated storage. A multivariate statistical analysis method was used to analyze the UPLC-MS/MS datasets from 35 tender coconut water samples stored for 0&ndash, 6 weeks. In addition, we identified other differentially expressed metabolites by selecting p-values and fold changes. Hierarchical cluster analysis and association analysis were performed with the differentially expressed metabolites. Metabolic pathways were analyzed using the KEGG database and the MetPA module of MetaboAnalyst. A total of 72 differentially expressed metabolites were identified in all groups. The OPLS-DA score chart showed that all samples were well grouped. Thirty-one metabolic pathways were enriched in the week 0&ndash, 1 samples. The results showed that after a tender coconut is peeled, the maximum storage time at 4 &deg, C is 1 week. Analysis of metabolic pathways related to coconut water storage using the KEGG and MetPA databases revealed that amino acid metabolism is one of the main causes of coconut water quality deterioration.

3-Carene is an antimicrobial monoterpene that occurs naturally in a variety of plants and has an ambiguous antibacterial mechanism against food-borne germs. The antibacterial effects and action mechanism of 3-carene against Gram-positive Brochothrix thermosphacta ACCC 03870 and Gram-negative Pseudomonas fluorescens ATCC 13525 were studied. Scanning electron microscopy (SEM) examination and leakage of alkaline phosphatase (AKP) verified that 3-carene caused more obvious damage to the morphology and wall structure of B. thermosphacta than P. fluorescens. The release of potassium ions and proteins, the reduction in membrane potential (MP), and fluorescein diacetate (FDA) staining further confirmed that the loss of the barrier function of the cell membrane and the leakage of cytoplasmic contents were due to the 3-carene treatment. Furthermore, the disorder of succinate dehydrogenase (SDH), malate dehydrogenase (MDH), pyruvate kinase (PK), and ATP content indicated that 3-carene could lead to metabolic dysfunction and inhibit energy synthesis. In addition, the results from the fluorescence analysis revealed that 3-carene could probably bind to bacterial DNA and affect the conformation and structure of genomic DNA. These results revealed that 3-carene had strong antibacterial activity against B. thermosphacta and P. fluorescens via membrane damage, bacterial metabolic perturbations, and genomic DNA structure disruption, interfering in cellular functions and even causing cell death.