MOLECULES
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2019
·Vol. 24
·期 23
·4300
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IF 3.06
Fu-Jia Chen,Xiao-Hua Long,En-Zhong Li
Phytophthora capsiciLeonian causes destructive economical losses in pepper production, and a promising source of natural fungicides-Helianthus tuberosusleaves was reported. The antifungal activities of different extracts and compounds fromH. tuberosusleaves against the phytopathogen,P. capsiciLeonian, were examined by chemometric analysis, including HPLC-MS/MS and multivariate data analyses. Principal component analysis and orthogonal partial least squares-discriminate analysis were applied to examine the four groups ofH. tuberosusleaves samples, including crude extracts obtained by different methods, including refluxing, macerating, and refluxing under vacuum; four fractions, namely, petroleum ether (PE), chloroform (Chl), ethyl acetate (EA), and n-butanol (NB) fractions; the samples of threeH. tuberosuscultivars; and the samples at three growth stages of cultivar Nan Yu. The phenolics contents were categorized based on 3,5-Dicaffeoylquinic acid (3,5-DiCQA), 1,5-Dicaffeoylquinic acid (1,5-DiCQA), 3-O-Caffeoylquinic acid (3-CQA), and 4,5-Dicaffeoylquinic acid (4,5-DiCQA), which were predominant in all the samples. Antifungal activity assay revealed that Chl and NB fractions were more active againstP. capsiciLeonian with lower IC50(half of maximal inhibitory concentration) values, whereas partial least squares-discriminate analysis suggested caffeoylquinic acid isomer(4-CQA), methyl-quercetin glycoside(MQG), and caffeic acid(CA) might be the main active components inH. tuberosusleaves againstP. capsiciLeonian. Furthermore, microscopic evaluation demonstrated structural deformities inP. capsiciLeonian treated with Chl and NB fractions, indicating the antifungal effects ofH. tuberosusleaves. These results imply thatH. tuberosusleaves with a high concentration of phenolics might be a promising source of natural fungicides.Keywords:Helianthus tuberosus;antifungal activity;phenolic acids;chemometric analysis;PLS-DA
研究领域:
植物科学
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植物化学
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真菌生物学
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分析化学
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产品关联
DOI
PubMed
高影响因子
Foods
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2021
·Vol. 10
·期 11
·2709
·
IF 4.35
Yan Huang,Dandan Pu,Zhilin Hao,Xiao Yang,Yuyu Zhang
This work aims to explore the contribution of prickly ash (Zanthoxylum bungeanumMaxim) on the taste perception of stewed sheep tail fat. Liquid chromatography-tandem quadrupole time of flight mass spectrometry (LC-QTOF-MS) was applied to analyze the taste-related compounds. A total of 99 compounds in different sheep tail fat samples were identified. The semi-quantitative results showed that there were differences between the samples. The partial least squares discriminant analysis (PLS-DA) model without overfitting was used to investigate the effect of prickly ash. Eleven marker compounds were predicted with a variable importance for projection > 1, fold change > 2 andp< 0.05. An additional experiment showed that guanosine 5′-monophosphate, malic acid, inosine and adenosine 5′-monophosphate could improve the umami and saltiness taste of stewed sheep tail fat.Keywords:sheep tail fat;taste compounds;LC-QTOF-MS;PLS-DA;sensory analysis;additional experiment
研究领域:
食品科学
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化学计量学
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植物学
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产品关联
DOI
PubMed
高影响因子
RSC Advances
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2022
·Vol. 13
·期 1
·499-505
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IF 4.036
Rong Wang,Jiamin Luo
Rapid and accurate identification of bacteria is essential to ensure food safety and prevent pathogenic bacterial infection. In this study, a highly efficient method was established for accurately identifying bacterial species by applying Ag NP-filter paper based Surface enhanced Raman spectroscopy (SERS) analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) statistical methods. The flexible Ag NP filter paper substrate with high sensitivity and uniformity was prepared by a facile and low-cost silver mirror reaction at room temperature, which exhibited desirable SERS activity in bacteria detection. Furthermore, PLS-DA was successfully employed to distinguish SERS spectra from S. aureus CMCC 26003, E. faecalis ATCC29212 and L. monocytogenes ATCC 19115 with a sensitivity of 93.3–100%, specificity of 96.7–97%, and overall predicting accuracy of 95.8%. This exploratory study demonstrates that a Ag NP-filter paper based SERS sensor coupled with PLS-DA has great potential for rapid and effective detection and identification of bacteria.
研究领域:
分析化学
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微生物学
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食品安全
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产品关联
DOI
PubMed
高影响因子
Food Analytical Methods
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2022
·Vol. 16
·期 1
·132-142
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IF 3.498
Pu Keyuan,Qiu Jiamin,Li Jiaying,Huang Wei,Lai Xiaopin,Liu Cheng,Lin Yan,Ng Kwan-Ming
The problem of adulteration and mislabeling in meat products has raised the public concerns globally. An easy-operation, fast, and robust method that is applicable to routine inspections is urgently needed. This study showed that the MALDI-TOF MS protein profiling of four meat species (beef, chicken, duck, and pork) combining with partial least squares discriminant analysis (PLS-DA) discovered 57 feature peaks for their unambiguous differentiation. Among them, 36 were identified in Uniprot. Based on the linear relation between the intensities of feature peaks, the partial least squares regression was successfully applied to build the prediction models for determining the adulteration ratios of beef meat mixtures containing one of the other three species. Blind tests were applied to evaluate the method and the average prediction accuracy at 94.7% was achieved. Taking duck meat as the adulterant, the detection sensitivity of the method could be down to 5%. Moreover, the method has also been successfully applied to analyze market samples and the results were in agreement with the PCR method, showing the potential of its practical application for qualitative and quantitative analysis of adulterated beef products.
研究领域:
食品科学
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生物化学
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分析化学
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DOI
高影响因子
Applied Sciences-Basel
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2019
·Vol. 9
·期 24
·5336
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IF 2.217
Qi XIA,Lei-ming YUAN,Xiaojing CHEN,Liuwei MENG,Guangzao HUANG
Featured ApplicationThe quality of methanol gasoline is important to internal combustion engines. Infrared spectroscopy technology shows its feasibility to qualitatively and quantitatively measure the quality of gasoline. Combined with the chemometrics, this technology can be used to determinate the adulteration category or fineness, providing the qualified gasoline for the market. Spectra analytical technology is a promising method in applying in the gasoline industry and speeding up the industrial application.AbstractMethanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.Keywords:methanol gasoline;infrared spectroscopy;partial least square discriminant analysis (PLS-DA);multivariate regression;variable selection
研究领域:
化学计量学
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能源科学
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产品关联
DOI
高影响因子
COMPUTERS IN BIOLOGY AND MEDICINE
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2024
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IF 7
Mengling Wang,Yongkang Wang,Lin He,Ju Huang,Yanying Tan,Wen Zhang,Zhinan Mei,Hui Xiong
Background Chrysanthemi Flos as a medicine food homology species is widely used in the prevention and treatment of diseases, whereas comprehensive research of its active compounds related to multi-pharmacological effects remains limited. This study aimed to systematically explore the active compounds through artificial intelligence-based target prediction and activity evaluation. Methods The information on compounds in Chrysanthemi Flos was obtained from six cultivars containing Gongju, Chuju, Huaiju, Boju, Hangbaiju, and Fubaiju, using UPLC-Q-TOF/MS. The main differential metabolites in six cultivars were also screened through the PLS-DA model. Then the potential targets of differential compounds were predicted via the DrugBAN model. Enrichment and topological analysis of compound-target networks were performed to identify key pharmaceutical compounds. Subsequently, the pharmacological effects of predictively active compounds were confirmed in vitro . Based on the active compounds, the pharmacological activities of Chrysanthemi Flos from the six origins were also investigated and compared for the further evaluation of medicinal quality. Results A total of 155 secondary metabolites were obtained from Chrysanthemi Flos . Among them, 26 differential components were screened, and 9 key pharmacological compounds with 1141 targets were identified. Enrichment analysis indicated the main pharmacological effects of Chrysanthemi Flos related to inflammation, oxidative stress, and lipid metabolism. In addition, 9 key pharmaceutical compounds were evaluated in vitro experiments, indicating the significant therapeutic effect in regulating inflammation, oxidative stress, and lipid metabolism. Conclusion This study successfully identified 9 key pharmaceutical compounds in Chrysanthemi Flos and predicted the pharmacodynamic advantages of six origins. The findings would provide improved guidance for the discovery of active constituents and the assessment of pharmacodynamic advantages of different geographical origins.
研究领域:
药理学
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药物化学
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人工智能
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代谢组学
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DOI
PubMed
高影响因子
Journal of Spectroscopy
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2016
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IF 0.814
Tong Wu,Hui Chen,Zan Lin,Chao Tan
Melamine is a nitrogen-rich substance and has been illegally used to increase the apparent protein content in food products such as milk. Therefore, it is imperative to develop sensitive and reliable analytical methods to determine melamine in human foods. Current analytical methods for melamine are mainly chromatography-based methods, which are time-consuming and expensive and require complex pretreatment and well-trained technicians. The present paper investigated the feasibility of using near-infrared (NIR) spectroscopy and chemometrics for identifying and quantifying melamine in liquor milk. A total of 75 samples were prepared. Uninformative variable elimination-partial least square (UVE-PLS) and partial least squares-discriminant analysis (PLS-DA) were used to construct quantitative and qualitative models, respectively. Based on the ratio of performance to standard deviate (RPD), UVE-PLS model with 3 components resulted in a better solution. The PLS-DA model achieved an accuracy of 100% and outperformed the optimal reference model of soft independent modeling of class analogy (SIMCA). Such a method can serve as a potential tool for rapid screening of melamine in milk products.
研究领域:
食品科学
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分析化学
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光谱学
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产品关联
DOI
高影响因子
Small
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2018
·Vol. 14
·期 20
·1704433
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IF 9.598
Yizhi Zhang,Zhuyuan Wang,Lei Wu,Shenfei Zong,Binfeng Yun,Yiping Cui
Isolating and in situ profiling the heterogeneous molecular phenotype of circulating tumor cells are of great significance for clinical cancer diagnosis and personalized therapy. Herein, an on-chip strategy is proposed that combines size-based microfluidic cell isolation with multiple spectrally orthogonal surface-enhanced Raman spectroscopy (SERS) analysis for in situ profiling of cell membrane proteins and identification of cancer subpopulations. With the developed microfluidic chip, tumor cells are sieved from blood on the basis of size discrepancy. To enable multiplex phenotypic analysis, three kinds of spectrally orthogonal SERS aptamer nanovectors are designed, providing individual cells with composite spectral signatures in accordance with surface protein expression. Next, to statistically demultiplex the complex SERS signature and profile the cellular proteomic phenotype, a revised classic least square algorithm is employed to obtain the 3D phenotypic information at single-cell resolution. Combined with categorization algorithm partial least square discriminate analysis, cells from different human breast cancer subtypes can be reliably classified with high sensitivity and selectivity. The results demonstrate that this platform can identify cancer subtypes with the spectral information correlated to the clinically relevant surface receptors, which holds great potential for clinical cancer diagnosis and precision medicine.
研究领域:
生物医学工程
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癌症研究
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分析化学
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微流控技术
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DOI
PubMed
高影响因子
LUMINESCENCE
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2022
·Vol. 37
·期 9
·1414-1426
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IF 2.613
Xiaoyu Yang,Lulu Lei,Donghui Song,Yue Sun,Meng Yang,Zhen Sang,Jianan Zhou,Hui Huang,Yongxin Li
To realize the efficient differential sensing of phenolic pollutants in sewage, a novel sensing strategy was successfully developed based on a nanozyme (GMP-Cu) with polyphenol oxidase activity. Phenolic pollutants can be oxidized using GMP-Cu, and the oxidation products reacts subsequently with 4-aminoantipyrine to produce a quinone-imine compound. The absorption spectra of final quinone-imine products that resulted from different phenolic pollutants showed obvious differences, which were due to the interaction difference between GMP-Cu and phenolic pollutants, as well as the different molecular structures of the quinone-imine products from different phenolic pollutants. Based on the difference in the absorption spectra, a novel differential sensing strategy was developed. A genetic algorithm was used to select the characteristic wavelengths at different enzymatic reaction times. Hierarchical cluster analysis and PLS-DA algorithms were utilized for the discriminant sensing of seven representative phenolic pollutants, including hydroquinone, resorcinol, catechol, resorcinol, phenol, p -chlorophenol, and 2,4-dichlorophenol. A scientific wavelength selection algorithm and a recognition algorithm resulted in the successful identification of phenolic pollutants in sewage with a discriminant accuracy of 100%, and differentiation of the phenolic pollutants regardless of their concentration. These results indicated that a sensing strategy can be used as an effective tool for the efficient identification and differentiation of phenolic pollutants in sewage.
研究领域:
环境科学
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分析化学
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纳米技术
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产品关联
DOI
PubMed
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
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2022
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IF 6.056
Bingsong Ma,Jiacai Wang,Binxing Zhou,Zihao Wang,Youyi Huang,Cunqiang Ma,Xinghui Li
To explore the impact of harvest season in white tea, high-performance liquid chromatography (HPLC) and amino acid analyzer were developed for the determinations of 17 phenolic compounds, 3 purine alkaloids, 19 amino acids, γ-aminobutyric acid (GABA) and 4 main quality components in 18 Bai mudan sub-type of Xinyang white teas harvested in spring, summer and autumn seasons, respectively. Additionally, the in vitro antioxidant capacity was evaluated by 5 various assays. Principal component analysis (PCA), hierarchical cluster analysis (HCA) and partial least squares-discriminant analysis (PLS-DA) could completely classify these white teas by the harvest season, and indicated its profound impact on chemical composition in white tea. 14 Characteristic compounds (VIP ≥1.0), such as (−)-epigallocatechin (EGC), gallic acid, caffeine, l -theanine, conduced to identify the harvest season of white tea. Through comparison, the spring tea was observed to possess the highest amino acids, catechins contents and in vitro antioxidant capacity. (−)-Epicatechin (EC), EGC, (−)-epicatechin gallate (ECG) and (−)-epigallocatechin gallate (EGCG) were considered as the major antioxidants in white tea due to their highly significantly ( p < 0.01) positive correlations. Overall, the research results provided support for the identification of harvest season in white tea.
研究领域:
食品科学
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化学
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农业科学
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产品关联
DOI
高影响因子
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
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2020
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IF 3.209
Lijuan Huang,Tongtong Li,Yingjun Zhang,Xiaohong Sun,Yongan Wang,Zhiyong Nie
The detection of psychoactive substances is an important branch of modern analytical chemistry and has many legally and socially relevant implications. The use of a surface plasmon resonance (SPR)-based gene–nanoparticle system has emerged as a promising technique for the rapid and ultrasensitive detection of molecular species such as drugs of abuse in biofluids. However, the development of a viable screening tool for the detection of multiple classes of drugs in complex media is a considerable challenge because the existing techniques lack affinity toward certain species due to matrix interference. Our aim was to develop a simple optical sensor array for the classification of nine narcotic drugs in aqueous solution and human urine. The UV–vis spectra of DNA–gold nanoparticles in the presence of nine narcotic drugs (pentobarbital sodium, caffeine, morphine, remifentanil, fentanyl, ketamine, etomidate, carfentanil, and sulfentanyl) were distinctly different. Furthermore, the narcotic drugs present in aqueous solution and in human urine were classified correctly through partial least squares discriminant analysis (PLS-DA). Combination with a multi-sensor unit further improved the prediction accuracy of the PLS-DA models. The proposed method has potential for on-site drug detection and drug abuse screening.
研究领域:
分析化学
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纳米技术
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化学计量学
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法医学
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产品关联
DOI
PubMed
高影响因子
BIOSENSORS & BIOELECTRONICS
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2023
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IF 12.6
Dechan Lu,Yanping Chen,Longfeng Ke,Weilin Wu,Liwen Yuan,Shangyuan Feng,Zufang Huang,Yudong Lu,Jing Wang
DNA methylation plays a critical role in the development of human tumors. However, routine characterization of DNA methylation can be time-consuming and labor-intensive. We herein describe a sensitive, simple surface-enhanced Raman spectroscopy (SERS) approach for identifying the DNA methylation pattern in early-stage lung cancer (LC) patients. By comparing SERS spectra of methylated DNA bases or sequences with their counterparts, we identified a reliable spectral marker of cytosine methylation. To move toward clinical applications, we applied our SERS strategy to detect the methylation patterns of genomic DNA (gDNA) extracted from cell line models as well as formalin-fixed paraffin-embedded tissues of early-stage LC and benign lung diseases (BLD) patients. In a clinical cohort of 106 individuals, our results showed distinct methylation patterns in gDNA between early-stage LC (n = 65) and BLD patients (n = 41), suggesting cancer-induced DNA methylation alterations. Combined with partial least square discriminant analysis, early-stage LC and BLD patients were differentiated with an area under the curve (AUC) value of 0.85. We believe that the SERS profiling of DNA methylation alterations, together with machine learning could potentially offer a promising new route toward the early detection of LC.
研究领域:
癌症生物学
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分子生物学
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生物医学工程
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生物信息学
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产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）