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1.
Int J Mol Sci ; 22(17)2021 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-34502454

RESUMO

COVID-19 is a global threat that has spread since the end of 2019, causing severe clinical sequelae and deaths, in the context of a world pandemic. The infection of the highly pathogenetic and infectious SARS-CoV-2 coronavirus has been proven to exert systemic effects impacting the metabolism. Yet, the metabolic pathways involved in the pathophysiology and progression of COVID-19 are still unclear. Here, we present the results of a mass spectrometry-based targeted metabolomic analysis on a cohort of 52 hospitalized COVID-19 patients, classified according to disease severity as mild, moderate, and severe. Our analysis defines a clear signature of COVID-19 that includes increased serum levels of lactic acid in all the forms of the disease. Pathway analysis revealed dysregulation of energy production and amino acid metabolism. Globally, the variations found in the serum metabolome of COVID-19 patients may reflect a more complex systemic perturbation induced by SARS-CoV-2, possibly affecting carbon and nitrogen liver metabolism.


Assuntos
Biomarcadores/sangue , Carbono/metabolismo , Fígado/metabolismo , Metaboloma , Nitrogênio/metabolismo , Aminoácidos/metabolismo , COVID-19/sangue , COVID-19/patologia , COVID-19/virologia , Citocinas/sangue , Análise Discriminante , Humanos , Análise dos Mínimos Quadrados , Redes e Vias Metabólicas/genética , Metabolômica/métodos , SARS-CoV-2/isolamento & purificação , Índice de Gravidade de Doença
2.
Sensors (Basel) ; 21(17)2021 Aug 26.
Artigo em Inglês | MEDLINE | ID: mdl-34502650

RESUMO

The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering signals, a variable forgetting factor (VFF) strategy is introduced to optimize the forgetting factor in the traditional RLS algorithm. It can distinguish the scattering signal from the stray light signal and dynamically adapt to the change in pulse amplitude according to different light absorptions and different particle sizes. To verify the filtering effect, small-angle scattering pulse extraction experiments were carried out in a simulated smoke box with different particle properties. The experiments show that the proposed VFF-RLS algorithm can effectively suppress system stray light and background noise. When the particle detection signal appears, the algorithm has fast convergence and tracking speed and highlights the particle pulse signal well. Compared with that of the traditional scattering pulse extraction method, the resolution of the processed scattering pulse signal of particles is greatly improved, and the extraction of weak particle scattering pulses at a small angle has a greater advantage. Finally, the effect of filter order in the algorithm on the results of extracting scattering pulses is discussed.


Assuntos
Algoritmos , Análise dos Mínimos Quadrados , Tamanho da Partícula
3.
Talanta ; 235: 122804, 2021 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-34517662

RESUMO

Identifying the writing sequence of seals and signatures in documents is often performed and difficult to resolve in forensic determination. Morphological and physical-chemical analysis methods are often limited by the destructive nature of samples, a high signal response strength and specific materials. Mass spectrometry imaging (MSI) has been used as an alternative method because it can generate molecular images from many surfaces and produce rich chemical information. Herein, we developed a sequence identification method by coupling an air flow-assisted desorption electrospray ionization (AFADESI)-MSI system with a chemometric analysis, which can holistically and directly analyse document samples under ambient, moderate and selectable conditions and maintain the original appearance of the paper documents after sampling. By integrating principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), equivocal point analysis can be objectively performed, where knowing the components of the seal or signature is not necessary to identify the sequence. In total, 28 prepared samples with known sequences and two original blind test samples were analysed. One prepared sample was analysed in negative ionization mode, and other samples were inferred in positive ionization mode. All writing sequences were in accordance with the actual case. The writing sequence of the blind testing of the original samples was correctly identified. This study provided a convenient, objective and quasi-nondestructive method to investigate the sequence differences among equivocal document samples and is promising for providing an alternative method for the sequence identification of seals and signatures in questionable documents.


Assuntos
Espectrometria de Massas por Ionização por Electrospray , Redação , Análise Discriminante , Análise dos Mínimos Quadrados , Espectrometria de Massas , Análise de Componente Principal
4.
Anal Chim Acta ; 1177: 338771, 2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34482899

RESUMO

Near-infrared (NIR) spectroscopy models for fresh fruit quality prediction often fail when used on a new batch or scenario having new variability which was absent in the primary calibration. To handle the new variability often model updating is required. In this study, to solve the challenge of updating NIR models related to fresh fruit quality properties, the use of a semi-supervised parameter-free calibration enhancement (PFCE) approach was proposed. Model updating with PFCE was shown in two ways: first where the model on the primary batch was updated individually for each new fruit batch, and second where the model was sequentially updated for the next batches. Furthermore, for the first time, a case of updating an instrument transferred model was also presented. The PFCE approach was shown in two real cases related to moisture and total soluble solids prediction in pear and kiwi fruit. In the case of pear, the model was later updated for 3 new measurement batches, while, for kiwi, a commercial model was updated to incorporate the variability of a new experiment carried out with a new instrument in the laboratory environment. For each modelling demonstration, the performance was benchmarked with the partial least-square (PLS) regression analysis on the primary batch. The results showed that the models updated with a semi-supervised approach kept a high predictive performance on new measurement batches, without any extra parameter optimization. An instrument transferred model was also updated to maintain its performance on different batches. Further, the sequential updating approach was found to be performing better than the update for individual batches, as the models were able to learn from multiple batches. Model updating with a semi-supervised approach can allow the NIR spectroscopy of fresh fruit to be scalable, where models can be shared between scientific or application community.


Assuntos
Frutas , Pyrus , Calibragem , Humanos , Análise dos Mínimos Quadrados , Espectroscopia de Luz Próxima ao Infravermelho
5.
Anal Chim Acta ; 1177: 338784, 2021 Sep 08.
Artigo em Inglês | MEDLINE | ID: mdl-34482902

RESUMO

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.


Assuntos
Carcinoma , Análise Espectral Raman , Análise Discriminante , Humanos , Análise dos Mínimos Quadrados , Análise de Componente Principal
6.
Molecules ; 26(16)2021 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-34443325

RESUMO

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.


Assuntos
Camellia/química , Qualidade dos Alimentos , Óleos Vegetais/química , Espectroscopia de Prótons por Ressonância Magnética , Análise Discriminante , Contaminação de Alimentos , Análise dos Mínimos Quadrados
7.
Comput Math Methods Med ; 2021: 8873059, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34426747

RESUMO

When encountering the outbreak and early spreading of COVID-19, the Government of Japan imposed gradually upgraded restriction policies and declared the state of emergency in April 2020 for the first time. To evaluate the efficacy of the countering strategies in different periods, we constructed a SEIADR (susceptible-exposed-infected-asymptomatic-documented-recovered) model to simulate the cases and determined corresponding spreading coefficients. The effective reproduction number R t was obtained to evaluate the measures controlling the COVID-19 conducted by the Government of Japan during different stages. It was found that the strict containing strategies during the state of emergency period drastically inhibit the COVID-19 trend. R t was decreased to 1.1123 and 0.8911 in stages 4 and 5 (a state of emergency in April and May 2020) from 3.5736, 2.0126, 3.0672 in the previous three stages when the containing strategies were weak. The state of emergency was declared again in view of the second wave of massive infections in January 2021. We estimated the cumulative infected cases and additional days to contain the COVID-19 transmission for the second state of emergency using this model. R t was 1.028 which illustrated that the strategies were less effective than the previous state of emergency. Finally, the overall infected population was predicted using combined isolation and testing intensity; the effectiveness and the expected peak time were evaluated. If using the optimized control strategies in the current stage, the spread of COVID-19 in Japan could be controlled within 30 days. The total confirmed cases should reduce to less than 4.2 × 105 by April 2021. This model study suggested stricter isolating measures may be required to shorten the period of the state of emergency.


Assuntos
COVID-19/epidemiologia , COVID-19/transmissão , Emergências , Modelos Biológicos , Pandemias , SARS-CoV-2 , Algoritmos , COVID-19/prevenção & controle , Teste para COVID-19/métodos , Teste para COVID-19/estatística & dados numéricos , Controle de Doenças Transmissíveis/legislação & jurisprudência , Controle de Doenças Transmissíveis/métodos , Controle de Doenças Transmissíveis/estatística & dados numéricos , Biologia Computacional , Simulação por Computador , Humanos , Japão/epidemiologia , Análise dos Mínimos Quadrados , Conceitos Matemáticos , Modelos Estatísticos , Programas Nacionais de Saúde/legislação & jurisprudência , Dinâmica não Linear , Pandemias/prevenção & controle , Pandemias/estatística & dados numéricos
8.
Zhongguo Zhong Yao Za Zhi ; 46(14): 3583-3591, 2021 Jul.
Artigo em Chinês | MEDLINE | ID: mdl-34402281

RESUMO

This study explores the emulsifying material basis of Angelicae Sinensis Radix volatile oil (ASRVO) based on partial least squares (PLS) method and hydrophile-lipophile balance (HLB) value.The turbidity of ASRVO emulsion samples from Gansu,Yunnan,and Qinghai was determined and the chemical components in the emulsion were analyzed by GC-MS.The PLS model was established with the chemical components as the independent variable and the turbidity as the dependent variable and evaluated with indexes R~2X and R~2Y.The chemical components which were in positive correlation with the turbidity were selected and the HLB values were calculated to determine the emulsification material basis of ASRVO.The PLS models for the 81 emulsion samples had high R~2X and R~2Y values,which showed good fitting ability.Seven chemical components,2-methoxy-4-vinylphenol,trans-ligustilide,3-butylidene-1(3H)-isobenzofuranone,dodecane,1-methyl-4-(1-methylethylidene)-cyclohexene,trans-beta-ocimene,and decane,had positive correlation with turbidity.Particularly,the HLB value of 2-methoxy-4-vinylphenol was 4.4,which was the HLB range of surfactants to be emulsifiers and 2-methoxy-4-vinylphenol was positively correlated with turbidity of the ASRVO emulsion samples from the main producing area.Therefore,2-methoxy-4-vinylphenol was the emulsifying material basis of ASRVO.The selected emulsifying substances can lay a foundation for exploring the emulsification mechanism and demulsification solution of ASRVO.


Assuntos
Óleos Voláteis , China , Emulsões , Análise dos Mínimos Quadrados , Tensoativos
9.
BMC Genomics ; 22(1): 605, 2021 Aug 09.
Artigo em Inglês | MEDLINE | ID: mdl-34372777

RESUMO

BACKGROUND: Identifying potential associations between genes and diseases via biomedical experiments must be the time-consuming and expensive research works. The computational technologies based on machine learning models have been widely utilized to explore genetic information related to complex diseases. Importantly, the gene-disease association detection can be defined as the link prediction problem in bipartite network. However, many existing methods do not utilize multiple sources of biological information; Additionally, they do not extract higher-order relationships among genes and diseases. RESULTS: In this study, we propose a novel method called Dual Hypergraph Regularized Least Squares (DHRLS) with Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL), in order to detect all potential gene-disease associations. First, we construct multiple kernels based on various biological data sources in gene and disease spaces respectively. After that, we use CAK-MKL to obtain the optimal kernels in the two spaces respectively. To specific, hypergraph can be employed to establish higher-order relationships. Finally, our DHRLS model is solved by the Alternating Least squares algorithm (ALSA), for predicting gene-disease associations. CONCLUSION: Comparing with many outstanding prediction tools, DHRLS achieves best performance on gene-disease associations network under two types of cross validation. To verify robustness, our proposed approach has excellent prediction performance on six real-world networks. Our research work can effectively discover potential disease-associated genes and provide guidance for the follow-up verification methods of complex diseases.


Assuntos
Algoritmos , Redes Reguladoras de Genes , Humanos , Análise dos Mínimos Quadrados , Aprendizado de Máquina
10.
Comput Intell Neurosci ; 2021: 7592064, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34373686

RESUMO

A pure acoustic signal can be easy to realize signal analysis and feature extraction. However, the surrounding noises will affect the content of acoustic signals as well as auditory fatigue to the audience. Therefore, it is vital to overcome the problem of noises that affect the acoustic signal. An indoor acoustic signal enhanced method based on image source (IS) method, filtered-x least mean square (FxLMS) algorithm, and the combination of Delaunay triangulation and fuzzy c-means (FCM) clustering algorithm is proposed. In the first stage of the proposed system, the IS method was used to simulate indoor impulse response. Next, the FxLMS algorithm was used to reduce the acoustic signals with noise. Lastly, the quiet areas are optimized and visualized by combining the Delaunay triangulation and FCM clustering algorithm. The experimental analysis results on the proposed system show that better noise reduction can be achieved than the most widely used least mean square algorithm. Visualization was validated with an intuitive understanding of the indoor sound field distribution and the quiet areas.


Assuntos
Acústica , Algoritmos , Análise dos Mínimos Quadrados , Ruído
11.
Talanta ; 234: 122595, 2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34364417

RESUMO

In this study, a new PLS-DA modelling approach for multi-class discriminant analysis, called Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis is proposed and evaluated. Because in this method Partial-Least-Squares models for one component are used, it is denoted as CSIV-PLS1-DA. In this method for each class vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1-DA combines a common variable set for all class vectors, which is a characteristic of PLS2-DA, with the individual model complexity for each class vector, which is a characteristic of PLS1-DA. These characteristics make CSIV-PLS1-DA more flexible than PLS2-DA. CSIV-PLS1-DA is found to be an alternative for PLS1-DA or PLS2-DA when the correlations between the responses are low, which is often the case in discriminant analysis. The performance of the CSIV-PLS1-DA method is investigated using one simulated and eight real multi-class data sets from different sources. The classification abilities, measured by the percentage classification accuracy rates (%Acc), resulting from CSIV-PLS1-DA, are statistically compared with those of PLS1-DA and PLS2-DA, using one-tailed paired t-tests at the 95% confidence level. The results show that the %Acc values resulting from the CSIV-PLS1-DA method are significantly higher than those of the corresponding PLS1-DA and PLS2-DA methods, meaning that the classification ability of the CSIV-PLS1-DA method is significantly better.


Assuntos
Análise Discriminante , Análise dos Mínimos Quadrados
12.
Talanta ; 234: 122653, 2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34364462

RESUMO

Deoxynivalenol (DON) contamination in wheat flour induces a number of adverse health effects to consumers and livestock, even at very low concentrations. Direct detection methods for massive screening of DON in wheat flour is still lacking. A new methodology integrating multi-molecular infrared spectroscopy (MM-IR) with two-trace two-dimensional correlation spectroscopy (2T-2DCOS) was developed for in-situ qualitative and quantitative determination of DON in wheat flour as a whole. Typical spectral variation of wheat flour samples with diverse concentration of DON were stepwise characterized by MM-IR and tiny spectral profile differences resulting from concentration variation of DON were visually disclosed by 2T-2DCOS. Based on the obtained key spectral features of DON, 180 of wheat flour samples with DON higher and lower than 1.00 mg/kg were undoubtedly classified by Principal Component Analysis (PCA) and Support Vector Machines (SVM) with an accuracy rate up to 100% (for Second derivative spectra consisted of selected bands, SD-SS). Furthermore, a robust quantitative prediction model was established based on partial least squares (PLS) of SD-SS (Rc: 0.998, RMSEC: 0.135; Rp: 0.968, RMSEP: 0.421), and its excellent predictive capacity of model was validated by both residual prediction deviation (RPD) value of 3.2 and t-test. It was demonstrated that the developed methodology was applicable for screening and quantitative detection of DON in wheat flour based on the novel correlation analysis methods (SD-2DCOS-IR and 2T-2DCOS-IR) with chemometrics tools, which could be utilized both at laboratory and industrial level for quality control purposes of a large wheat flour sample set.


Assuntos
Farinha , Triticum , Farinha/análise , Contaminação de Alimentos/análise , Humanos , Análise dos Mínimos Quadrados , Espectrofotometria Infravermelho
13.
Phytochemistry ; 191: 112928, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34455289

RESUMO

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy in tandem with chemometrics was used for accurate quantification of total eugenol, eugenyl acetate, and ß-caryophyllene compounds of clove oil (CO) using partial least squares (PLS) regression with various spectral derivatization methods. A set of six of the fifty-one CO samples was chosen to build up the calibration sets for the compounds, while the rest were selected as the prediction set. Data for total eugenol, eugenyl acetate, and ß-caryophyllene was acquired by gas chromatography-mass spectrometry (GC-MS) and used as reference values for ATR-FTIR calibration. The best calibration results were achieved using raw spectra in the region 1560-1480, 1814-1700, and 2954-2780 cm-1 for total eugenol, eugenyl acetate, and ß-caryophyllene with high regression coefficients (R-square) of 0.9999, 0.9966, and 0.9997, respectively and low root mean square error of prediction (RMSEP) values of 0.5054%, 0.2330%, and 0.4593%, respectively. The results of the study indicated that ATR-FTIR with PLS regression could be used for accurate and simultaneous quantification of total eugenol, eugenyl acetate, and ß-caryophyllene compounds of COs without using any toxic chemicals or pretreatments.


Assuntos
Óleos Voláteis , Syzygium , Óleo de Cravo , Eugenol/análogos & derivados , Análise dos Mínimos Quadrados , Sesquiterpenos Policíclicos , Espectroscopia de Infravermelho com Transformada de Fourier
14.
Eur J Pharm Sci ; 166: 105963, 2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34352284

RESUMO

PURPOSE: The objective of the present study was to explore and compare fast and non-destructive Transmission Raman Spectroscopy (TRS) and Near Infrared Hyperspectral imaging (NIR HSI) for the development of predictive quantitative methods to determine content uniformity (CU) of tablets. METHODS: A set of single Active Pharmaceutical Ingredients (API) tablets with nine concentration levels of caffeine ranging from 12.75%w/w to 17.75%w/w and another set of double API tablets with five concentration levels of model API A* (5.25%w/w - 9.25%w/w) and caffeine (7%w/w - 13%w/w) were prepared. Chemometric prediction models were developed using partial least square (PLS 1) and later tested using a test set for both single and double API tablets. RESULTS: Calibration PLS1 models were developed for both single and double APIs using a combination of S-G 1st derivative and SNV data pre-processing steps that offer an optimal model performance with the lowest cross-validation error and bias. The root mean square error of prediction (RMSEP) for the PLS1 model for single API caffeine tablets using TRS and NIR HSI was 0.27% and 0.36% respectively. The RMSEP for the PLS1 models built using TRS for the double API tablets was 0.29% for API A and 0.34% for caffeine. Similarly, for the NIR HIS prediction models the RMSEP was 0.43% for API A and 0.56% for caffeine. CONCLUSION: Overall TRS presented a 25-30% more accurate prediction capability compared to NIR HSI in this specific sample sets. Nevertheless, both TRS ad NIR HSI possess the potential to be employed as rapid, nondestructive techniques to replace classical wet- chemistry methods for at- or off-line determination of tablet CU.


Assuntos
Imageamento Hiperespectral , Análise Espectral Raman , Calibragem , Análise dos Mínimos Quadrados , Espectroscopia de Luz Próxima ao Infravermelho , Comprimidos
15.
Molecules ; 26(16)2021 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-34443424

RESUMO

Fourier transform infrared spectroscopy on the middle infrared region (ATR-FTIR-MIR) proved to be a convenient and reliable technique to evaluate foods' quality and authenticity. Plants' essential oils are bioactive mixtures used as such or in different oily or microencapsulated formulations, beneficial to human health. Six essential oils (thyme, oregano, juniperus, tea tree, clove, and cinnamon) were introduced in three oily formulations (Biomicin, Biomicin Forte, and Biomicin urinary) and these formulations were microencapsulated on fructose and maltodextrin matrices. To study their stability, the microencapsulated powders were kept under light irradiation for 14 days at 25 °C or introduced in biopolymer capsules. All variants were analysed by ATR-FTIR-MIR, recording wavenumbers and peak intensities (3600-650 cm-1). The data were processed by Unscrambler and Metaboanalyst software, with specific algorithms (PCA, PLSDA, heatmaps, and random forest analysis). The results demonstrated that ATR-FTIR-MIR can be successfully applied for fingerprinting and finding essential oil biomarkers as well as to recognize this pattern in final microencapsulated food supplements. This study offers an improved ATR-FTIR-MIR procedure coupled with an adequate chemometric analysis and accurate data interpretation, to be applied for the evaluation of authenticity, quality, traceability, and stability during storage of essential oils incorporated in different matrices.


Assuntos
Suplementos Nutricionais , Composição de Medicamentos , Óleos Voláteis/análise , Reconhecimento Automatizado de Padrão , Análise Discriminante , Análise dos Mínimos Quadrados , Análise Multivariada , Análise de Componente Principal , Espectroscopia de Infravermelho com Transformada de Fourier
16.
Molecules ; 26(15)2021 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-34361796

RESUMO

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.


Assuntos
Contaminação de Alimentos/análise , Carne/análise , Metaboloma , Metabolômica/métodos , Aminoácidos/análise , Animais , Bovinos , Galinhas , Colina/análise , Creatina/análise , Equidae , Contaminação de Alimentos/prevenção & controle , Cabras , Humanos , Ácido Láctico/análise , Análise dos Mínimos Quadrados , Espectroscopia de Ressonância Magnética , Manose/análise , Análise Multivariada , Análise de Componente Principal , Especificidade da Espécie
17.
Anal Chem ; 93(36): 12187-12194, 2021 09 14.
Artigo em Inglês | MEDLINE | ID: mdl-34459578

RESUMO

Spectral histopathology has shown promise for the classification and diagnosis of tumors with defined morphology, but application in tumors with variable or diffuse morphologies is yet to be investigated. To address this gap, we evaluated the application of Fourier transform infrared (FTIR) imaging as an accessory diagnostic tool for canine hemangiosarcoma (HSA), a vascular endothelial cell cancer that is difficult to diagnose. To preserve the delicate vascular tumor tissue structure, and potential classification of single endothelial cells, paraffin removal was not performed, and a partial least square discrimination analysis (PLSDA) and Random Forest (RF) models to classify different tissue types at individual pixel level were established using a calibration set (24 FTIR images from 13 spleen specimens). Next, the prediction capability of the PLSDA model was tested with an independent test set (n = 11), resulting in 74% correct classification of different tissue types at an individual pixel level. Finally, the performance of the FTIR spectropathology and chemometric algorithm for diagnosis of HSA was established in a blinded set of tissue samples (n = 24), with sensitivity and specificity of 80 and 81%, respectively. Taken together, these results show that FTIR imaging without paraffin removal can be applied to tumors with diffuse morphology, and this technique is a promising tool to assist in canine splenic HSA differential diagnosis.


Assuntos
Hemangiossarcoma , Animais , Cães , Células Endoteliais , Hemangiossarcoma/diagnóstico por imagem , Hemangiossarcoma/veterinária , Análise dos Mínimos Quadrados , Espectroscopia de Infravermelho com Transformada de Fourier , Baço
18.
Sensors (Basel) ; 21(15)2021 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-34372298

RESUMO

The aim of this study is to establish the usefulness of an electronic tongue based on cyclic voltammetry e-tongue using five working electrodes (gold, silver, copper, platinum and glass) in honey adulteration detection. Authentic honey samples of different botanical origin (acacia, tilia, sunflower, polyfloral and raspberry) were adulterated with agave, maple, inverted sugar, corn and rice syrups in percentages of 5%, 10%, 20% and 50%. The silver and copper electrodes provided the clearest voltammograms, the differences between authentic and adulterated honey samples being highlighted by the maximum current intensity. The electronic tongue results have been correlated with physicochemical parameters (pH, free acidity, hydroxymethylfurfural content-5 HMF and electrical conductivity-EC). Using statistical methods such as Linear discriminant analysis (LDA) and Support vector machines (SVM), an accuracy of 94.87% and 100% respectively was obtained in the calibration step and 89.65% and 100% respectively in the validation step. The PLS-R (Partial Least Squares Regression) model (constructed from the minimum and maximum current intensity obtained for all electrodes) was used in physicochemical parameters prediction; EC reached the highest regression coefficients (0.840 in the calibration step and 0.842 in the validation step, respectively), being followed by pH (0.704 in the calibration step and 0.516 in the validation step, respectively).


Assuntos
Mel , Análise Discriminante , Contaminação de Alimentos/análise , Mel/análise , Análise dos Mínimos Quadrados , Língua
19.
Sensors (Basel) ; 21(16)2021 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-34451076

RESUMO

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.


Assuntos
Panax , Análise Discriminante , Resposta ao Choque Térmico , Humanos , Análise dos Mínimos Quadrados , Espectroscopia de Luz Próxima ao Infravermelho
20.
Int J Mol Sci ; 22(12)2021 Jun 11.
Artigo em Inglês | MEDLINE | ID: mdl-34208417

RESUMO

In the present study, we analyze the nuclear magnetic resonance (NMR) blood serum metabolic profiles of 106 head and neck squamous cell carcinoma (HNSCC) patients during radio (RT) and concurrent radio-chemotherapy (CHRT). Four different fractionation schemes were compared. The blood samples were collected weekly, from the day before the treatment until the last week of CHRT/RT. The NMR spectra were acquired on A Bruker 400 MHz spectrometer at 310 K and analyzed using multivariate methods. Seven metabolites were found significantly to be altered solely by radiotherapy: N-acetyl-glycoprotein (NAG), N-acetylcysteine, glycerol, glycolate and the lipids at 0.9, 1.3 and 3.2 ppm. The NMR results were correlated with the tissue volumes receiving a particular dose of radiation. The influence of the irradiated volume on the metabolic profile is weak and mainly limited to sparse correlations with the inflammatory markers, creatinine and the lymphocyte count in RT and the branched-chain amino-acids in CHRT. This is probably due to the optimal planning and delivery of radiotherapy improving sparing of the surrounding normal tissues and minimizing the differences between the patients (caused by the tumor location and size).


Assuntos
Neoplasias de Cabeça e Pescoço/sangue , Neoplasias de Cabeça e Pescoço/radioterapia , Espectroscopia de Ressonância Magnética , Metabolômica , Dosagem Radioterapêutica , Adulto , Idoso , Análise Discriminante , Fracionamento da Dose de Radiação , Relação Dose-Resposta à Radiação , Feminino , Humanos , Análise dos Mínimos Quadrados , Masculino , Pessoa de Meia-Idade , Estatísticas não Paramétricas
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