Relevant bibliographies by topics / Partial Least Squares Discriminant Analysis (PLS-DA) / Journal articles
To see the other types of publications on this topic, follow the link: Partial Least Squares Discriminant Analysis (PLS-DA).

Journal articles on the topic 'Partial Least Squares Discriminant Analysis (PLS-DA)'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Partial Least Squares Discriminant Analysis (PLS-DA).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Wang, Chih-Yu, Chin-Tin Chen, Chun-Pin Chiang, Shueng-Tsong Young, Song-Nan Chow, and Huihua Kenny Chiang. "Partial Least-Squares Discriminant Analysis on Autofluorescence Spectra of Oral Carcinogenesis." Applied Spectroscopy 52, no. 9 (September 1998): 1190–96. http://dx.doi.org/10.1366/0003702981945002.

Full text
Abstract:
A partial least-squares (PLS) discriminant analysis on the autofluorescence spectra of oral squamous cell carcinoma based on the cross-validation technique was conducted to discriminate among oral tissues at different cancer development stages. These tissues were obtained from hamsters of DMBA-induced buccal pouch carcinogenesis. The study on the fluorescence spectra of the cancer tissues revealed that 320 nm might be the optimal excitation wavelength, and it was selected for the discriminating analysis. The PLS discriminant plot based on cross-validation showed that tissues of oral carcinogenesis belonging to four clinically important cancer development stages—normal tissues, hyperplasia, dysplasia and early cancers, and frankly invasive cancers—could be classified by using the first two PLS factors that emerged from the fluorescence spectra at 320 nm excitation. The PLS factor loading plots of the first PLS factor of 320 and 360 nm excitations showed that the first PLS factor was correlated to the fluorescent structure changes. This study indicates that further development of the PLS discriminant analysis on the autofluorescence spectra may be useful for developing a simple and efficient discriminating algorithm for the identification of different stages of human oral carcinogenesis.
APA, Harvard, Vancouver, ISO, and other styles
2

Bevilacqua, Marta, and Federico Marini. "Local classification: Locally weighted–partial least squares-discriminant analysis (LW–PLS-DA)." Analytica Chimica Acta 838 (August 2014): 20–30. http://dx.doi.org/10.1016/j.aca.2014.05.057.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Douglas de Sousa Fernandes, David, Valber Elias Almeida, Licarion Pinto, Germano Véras, Roberto Kawakami Harrop Galvão, Adriano Araújo Gomes, and Mário Cesar Ugulino Araújo. "The successive projections algorithm for interval selection in partial least squares discriminant analysis." Analytical Methods 8, no. 41 (2016): 7522–30. http://dx.doi.org/10.1039/c6ay01840h.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

de Oliveira, Vitória Maria Almeida Teodoro, Michel Rocha Baqueta, Paulo Henrique Março, and Patrícia Valderrama. "Authentication of organic sugars by NIR spectroscopy and partial least squares with discriminant analysis." Analytical Methods 12, no. 5 (2020): 701–5. http://dx.doi.org/10.1039/c9ay02025j.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kelly, Rachel, Michael McGeachie, Kathleen Lee-Sarwar, Priyadarshini Kachroo, Su Chu, Yamini Virkud, Mengna Huang, Augusto Litonjua, Scott Weiss, and Jessica Lasky-Su. "Partial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma." Metabolites 8, no. 4 (October 23, 2018): 68. http://dx.doi.org/10.3390/metabo8040068.

Full text
Abstract:
To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (n = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (p for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data.
APA, Harvard, Vancouver, ISO, and other styles
6

Mohd Ruah, Mas Ezatul Nadia, Nor Fazila Rasaruddin, Siong Fong Sim, and Mohd Zuli Jaafar. "Application of Partial Least Squares Discriminant Analysis for Discrimination of Palm Oil." Scientific Research Journal 11, no. 1 (June 1, 2014): 1. http://dx.doi.org/10.24191/srj.v11i1.5415.

Full text
Abstract:
This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm-1 to 4000 cm-1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.
APA, Harvard, Vancouver, ISO, and other styles
7

Mohd Ruah, Mas Ezatul Nadia, Nor Fazila Rasaruddin, Siong Fong Sim, and Mohd Zuli Jaafar. "Application of Partial Least Squares Discriminant Analysis for Discrimination of Palm Oil." Scientific Research Journal 11, no. 1 (June 1, 2014): 1. http://dx.doi.org/10.24191/srj.v11i1.9397.

Full text
Abstract:
This paper outlines the application of chemometrics and pattern recognition tools to classify palm oil using Fourier Transform Mid Infrared spectroscopy (FT-MIR). FT-MIR spectroscopy is used as an effective analytical tool in order to categorise the oil into the category of unused palm oil and used palm oil for frying. The samples used in this study consist of 28 types of pure palm oil, and 28 types of frying palm oils. FT-MIR spectral was obtained in absorbance mode at the spectral range from 650 cm·1 to 4000 cm·1 using FT-MIR-ATR sample handling. The aim of this work is to develop fast method in discriminating the palm oil by implementing Partial Least Square Discriminant Analysis (PLS-DA), Leaming Vector Quantisation (LVQ) and Support Vector Machine (SVM). Raw FT-MIR spectra were subjected to Savitzky-Golay smoothing and standardised before developing the classification models. The classification model was validated by finding the value of percentage correctly classified using test set for every model in order to show which classifier provided the best classification. In order to improve the performance of the classification model, variable selection method known as /-statistic method was applied. The significant variable in developing classification model was selected through this method. The result revealed that PLS-DA classifier of the standardised data with application of t-statistic showed the best performance with highest percentage correctly classified among the classifiers.
APA, Harvard, Vancouver, ISO, and other styles
8

Bi, Yaoshan, Jiwen Wu, Xiaorong Zhai, Shuhao Shen, Libin Tang, Kai Huang, and Dawei Zhang. "Application of Partial Least Squares-Discriminate Analysis Model Based on Water Chemical Compositions in Identifying Water Inrush Sources from Multiple Aquifers in Mines." Geofluids 2021 (February 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/6663827.

Full text
Abstract:
Mine water inrush seriously threatens the safety of coal mine production. Quick and accurate identification of mine water inrush sources is of great significance to preventing mine water hazards. This paper combined partial least squares-discriminate analysis (PLS-DA) with inrush water chemical composition to identify the source of water inrush from multiple aquifers in mines. The Renlou Coal Mine in the Linhuan mining area was selected for this study, and seven conventional water chemical compositions from 54 water samples in three aquifers were collected and tested, of which 45 water samples were used to establish the PLS-DA discriminant model, and nine were used to test the prediction effect. To improve model accuracy and predictive ability, hierarchical clustering analysis method was used to eliminate seven unqualified water samples to reduce the errors caused by improper data. PCA and PLS-DA methods were used to analyze and process the remaining water sample data, and on the basis of PCA analysis, the remaining 38 water samples were used to establish the PLS-DA discriminant model. The model was validated using permutation and external prediction tests. The research shows the following results: (1) Both PCA and PLS-DA methods can distinguish water samples from three different water sources, but the classification effect of PLS-DA was better than PCA because it can strengthen the difference of water chemical composition between different water sources. (2) The correct discrimination rate of the PLS-DA discriminant model was as high as 100%, and permutation tests showed that the model was not overfit. External validation found that the model had good stability and discrimination. (3) HCO3- and total dissolved solids (TDS) were the most important differential marker compositions that affected the discrimination results based on Variable Importance for the Projection (VIP) scores. The discriminant model established in this study combined the advantages of principal component analysis and multiple regression analysis, providing a new method for accurately identifying the sources of water inrush in mines.
APA, Harvard, Vancouver, ISO, and other styles
9

Lin, Jingjun, Xiaomei Lin, Lianbo Guo, Yangmin Guo, Yun Tang, Yanwu Chu, Shisong Tang, and Changjin Che. "Identification accuracy improvement for steel species using a least squares support vector machine and laser-induced breakdown spectroscopy." Journal of Analytical Atomic Spectrometry 33, no. 9 (2018): 1545–51. http://dx.doi.org/10.1039/c8ja00216a.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Wagala, Adolphus, Graciela González-Farías, Rogelio Ramos, and Oscar Dalmau. "PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem." Revista Colombiana de Estadística 43, no. 2 (July 1, 2020): 233–49. http://dx.doi.org/10.15446/rce.v43n2.81811.

Full text
Abstract:
This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classifiers with the classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based on the lowest classification error rates compared to the others when applied to the types of data are considered; the un- preprocessed and preprocessed.
APA, Harvard, Vancouver, ISO, and other styles
11

Hedegaard, Martin A. B., Kristy L. Cloyd, Christine-Maria Horejs, and Molly M. Stevens. "Model based variable selection as a tool to highlight biological differences in Raman spectra of cells." Analyst 139, no. 18 (2014): 4629–33. http://dx.doi.org/10.1039/c4an00731j.

Full text
Abstract:
Here we present a novel approach to analyse cells using Partial Least Squares – Discriminant Analysis (PLS-DA) Variable Importance Projection (VIP) scores normally used for variable selection as heat maps combined with group difference spectra to highlight significant differences in Raman band shapes and position.
APA, Harvard, Vancouver, ISO, and other styles
12

Zontov, Y. V., O. Ye Rodionova, S. V. Kucheryavskiy, and A. L. Pomerantsev. "PLS-DA – A MATLAB GUI tool for hard and soft approaches to partial least squares discriminant analysis." Chemometrics and Intelligent Laboratory Systems 203 (August 2020): 104064. http://dx.doi.org/10.1016/j.chemolab.2020.104064.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

Pace, José-Henrique Camargo, João-Vicente De Figueiredo Latorraca, Paulo-Ricardo Gherardi Hein, Alexandre Monteiro de Carvalho, Jonnys Paz Castro, and Carlos-Eduardo Silveira da Silva. "Wood species identification from Atlantic forest by near infrared spectroscopy." Forest Systems 28, no. 3 (October 8, 2019): e015. http://dx.doi.org/10.5424/fs/2019283-14558.

Full text
Abstract:
Aim of study: Fast and reliable wood identification solutions are needed to combat the illegal trade in native woods. In this study, multivariate analysis was applied in near-infrared (NIR) spectra to identify wood of the Atlantic Forest species.Area of study: Planted forests located in the Vale Natural Reserve in the county of Sooretama (19 ° 01'09 "S 40 ° 05'51" W), Espírito Santo, Brazil.Material and methods: Three trees of 12 native species from homogeneous plantations. The principal component analysis (PCA) and partial least squares regression by discriminant function (PLS-DA) were performed on the woods spectral signatures.Main results: The PCA scores allowed to agroup some wood species from their spectra. The percentage of correct classifications generated by the PLS-DA model was 93.2%. In the independent validation, the PLS-DA model correctly classified 91.3% of the samples.Research highlights: The PLS-DA models were adequate to classify and identify the twelve native wood species based on the respective NIR spectra, showing good ability to classify independent native wood samples.Keywords: native woods; NIR spectra; principal components; partial least squares regression.
APA, Harvard, Vancouver, ISO, and other styles
14

Mao, Zhi-Hua, Yue-Chao Wu, Xue-Xi Zhang, Hao Gao, and Jian-Hua Yin. "Comparative study on identification of healthy and osteoarthritic articular cartilages by fourier transform infrared imaging and chemometrics methods." Journal of Innovative Optical Health Sciences 10, no. 03 (April 4, 2017): 1650054. http://dx.doi.org/10.1142/s1793545816500541.

Full text
Abstract:
Two discriminant methods, partial least squares-discriminant analysis (PLS-DA) and Fisher’s discriminant analysis (FDA), were combined with Fourier transform infrared imaging (FTIRI) to differentiate healthy and osteoarthritic articular cartilage in a canine model. Osteoarthritic cartilage had been developed for up to two years after the anterior cruciate ligament (ACL) transection in one knee. Cartilage specimens were sectioned into 10 [Formula: see text]m thickness for FTIRI. A PLS-DA model was developed after spectral pre-processing. All IR spectra extracted from FTIR images were calculated by PLS-DA with the discriminant accuracy of 90%. Prior to FDA, principal component analysis (PCA) was performed to decompose the IR spectral matrix into informative principal component matrices. Based on the different discriminant mechanism, the discriminant accuracy (96%) of PCA-FDA with high convenience was higher than that of PLS-DA. No healthy cartilage sample was mis-assigned by these two methods. The above mentioned suggested that both integrated technologies of FTIRI-PLS-DA and, especially, FTIRI-PCA-FDA could become a promising tool for the discrimination of healthy and osteoarthritic cartilage specimen as well as the diagnosis of cartilage lesion at microscopic level. The results of the study would be helpful for better understanding the pathology of osteoarthritics.
APA, Harvard, Vancouver, ISO, and other styles
15

Bevilacqua, Marta, and Rasmus Bro. "Can We Trust Score Plots?" Metabolites 10, no. 7 (July 8, 2020): 278. http://dx.doi.org/10.3390/metabo10070278.

Full text
Abstract:
In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.
APA, Harvard, Vancouver, ISO, and other styles
16

Guo, Zhiming, Chuang Guo, Quansheng Chen, Qin Ouyang, Jiyong Shi, Hesham R. El-Seedi, and Xiaobo Zou. "Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics." Sensors 20, no. 7 (April 9, 2020): 2130. http://dx.doi.org/10.3390/s20072130.

Full text
Abstract:
It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.
APA, Harvard, Vancouver, ISO, and other styles
17

Lu, Chenghui, Bingren Xiang, Gang Hao, Jianping Xu, Zhengwu Wang, and Changyun Chen. "Rapid Detection of Melamine in Milk Powder by near Infrared Spectroscopy." Journal of Near Infrared Spectroscopy 17, no. 2 (January 1, 2009): 59–67. http://dx.doi.org/10.1255/jnirs.829.

Full text
Abstract:
This paper establishes a novel and rapid method for detecting pure melamine in milk powder using near infrared (NIR) spectroscopy based on least squares-support vector machine (LS-SVM). Partial least square discriminant analysis (PLS-DA) was used for the extraction of principal components (PCs). The scores of the first two PCs have been applied as inputs to LS-SVM. Compared to PLS-DA, the performance of LS-SVM was better, with higher classification accuracy, both 100% for the training and testing set. The detection limit was lower than 1 ppm. Based on the results, it was concluded that NIR spectroscopy combined with LS-SVM could be used as a rapid and accurate method for detecting pure melamine in milk powder.
APA, Harvard, Vancouver, ISO, and other styles
18

Wong, Ka H., Valentina Razmovski-Naumovski, Kong M. Li, George Q. Li, and Kelvin Chan. "Differentiation of Pueraria lobata and Pueraria thomsonii using partial least square discriminant analysis (PLS-DA)." Journal of Pharmaceutical and Biomedical Analysis 84 (October 2013): 5–13. http://dx.doi.org/10.1016/j.jpba.2013.05.040.

Full text
APA, Harvard, Vancouver, ISO, and other styles
19

Wu, Tong, Hui Chen, Zan Lin, and Chao Tan. "Identification and Quantitation of Melamine in Milk by Near-Infrared Spectroscopy and Chemometrics." Journal of Spectroscopy 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/6184987.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
20

Windarsih, Anjar, Abdul Rohman, and Respati Tri Swasono. "AUTHENTICATION OF TURMERIC USING PROTON-NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY AND MULTIVARIATE ANALYSIS." International Journal of Applied Pharmaceutics 10, no. 6 (November 22, 2018): 174. http://dx.doi.org/10.22159/ijap.2018v10i6.29014.

Full text
Abstract:
Objective: The objective of this study was to apply 1H-NMR spectroscopy-based metabolite fingerprinting in combination with multivariate analysis for authentication of turmeric (Curcuma longa) from C. heyneana and C. manga.Methods: Partial least square-discriminant analysis (PLS-DA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for differentiation of authentic and adulterated C. longa with C. manga and C. heyneana. The variables used were peaks with certain chemical shifts at optimized 1H-NMR spectra of authentic and adulterated C. longa.Results: All of the authentic C. longa samples were clearly separated from the adulterated ones. The multivariate calibration of partial least square (PLS) was successfully applied to predict of adulterants in C. longa. The lower RMSEC (root mean square error of calibration) values, 0.94% for adulterated C. longa with C. heyneana and 1.37% for adulterated C. longa with C. manga, and the lower RMSEP (root mean square error of prediction) values, 0.83% for adulterated C. longa with C. heyneana and 1.34% for adulterated C. longa with C. manga indicated the good of accuracy and precision of the calibration models.Conclusion: The combination of 1H-NMR spectroscopy and chemometrics of multivariate analysis PLS-DA, OPLS-DA, and PLS proves an adequate technique for authentication of turmeric.
APA, Harvard, Vancouver, ISO, and other styles
21

Marín, Javier, Núria Serrano, Cristina Ariño, and José Manuel Díaz-Cruz. "A Chemometric Survey about the Ability of Voltammetry to Discriminate Pharmaceutical Products from the Evolution of Signals as a Function of pH." Chemosensors 8, no. 3 (June 29, 2020): 46. http://dx.doi.org/10.3390/chemosensors8030046.

Full text
Abstract:
Many pharmaceutical products are electroactive and, therefore, can be determined by voltammetry. However, most of these substances produce signals in the same region of oxidative potentials, which makes it difficult to identify them. In this work, chemometric tools are applied to extract characteristic information not only from the peak potential of differential pulse voltammograms (DPV), but also from their evolution as a function of pH. The chemometric approach is based on principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) yielding promising results for the future discrimination of pharmaceutical products in water samples.
APA, Harvard, Vancouver, ISO, and other styles
22

Fang, Kang, Xiao-Hua Zhang, Yao-Tian Han, Gao-Rong Wu, De-Sheng Cai, Nan-Nan Xue, Wen-Bo Guo, et al. "Design, Synthesis, and Cytotoxic Analysis of Novel Hederagenin–Pyrazine Derivatives Based on Partial Least Squares Discriminant Analysis." International Journal of Molecular Sciences 19, no. 10 (September 30, 2018): 2994. http://dx.doi.org/10.3390/ijms19102994.

Full text
Abstract:
Hederagenin (He) is a novel triterpene template for the development of new antitumor compounds. In this study, 26 new He–pyrazine derivatives were synthetized in an attempt to develop potent antitumor agents; they were screened for in vitro cytotoxicity against tumor and non-tumor cell lines. The majority of these derivatives showed much stronger cytotoxic activity than He. Remarkably, the most potent was compound 9 (half maximal inhibitory concentration (IC50) was 3.45 ± 0.59 μM), which exhibited similar antitumor activities against A549 (human non-small-cell lung cancer) as the positive drug cisplatin (DDP; IC50 was 3.85 ± 0.63 μM), while it showed lower cytotoxicity on H9c2 (murine heart myoblast; IC50 was 16.69 ± 0.12 μM) cell lines. Compound 9 could induce the early apoptosis and evoke cell-cycle arrest at the synthesis (S) phase of A549 cells. Impressively, we innovatively introduced the method of cluster analysis modeled as partial least squares discriminant analysis (PLS-DA) into the structure–activity relationship (SAR) evaluation, and SAR confirmed that pyrazine had a profound effect on the antitumor activity of He. The present studies highlight the importance of pyrazine derivatives of He in the discovery and development of novel antitumor agents.
APA, Harvard, Vancouver, ISO, and other styles
23

Khan, Asma, Muhammad Tajammal Munir, Wei Yu, and Brent Young. "Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging." Sensors 20, no. 16 (August 18, 2020): 4645. http://dx.doi.org/10.3390/s20164645.

Full text
Abstract:
Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.
APA, Harvard, Vancouver, ISO, and other styles
24

Hobro, Alison J., Julia Kuligowski, Markus Döll, and Bernhard Lendl. "Differentiation of walnut wood species and steam treatment using ATR-FTIR and partial least squares discriminant analysis (PLS-DA)." Analytical and Bioanalytical Chemistry 398, no. 6 (September 30, 2010): 2713–22. http://dx.doi.org/10.1007/s00216-010-4199-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
25

Lin, Zhaozhou, Qiao Zhang, Shengyun Dai, and Xiaoyan Gao. "Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression." Metabolites 10, no. 1 (January 13, 2020): 33. http://dx.doi.org/10.3390/metabo10010033.

Full text
Abstract:
Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.
APA, Harvard, Vancouver, ISO, and other styles
26

Stavropoulou, Maria-Ioanna, Aikaterini Termentzi, Konstantinos M. Kasiotis, Antigoni Cheilari, Konstantina Stathopoulou, Kyriaki Machera, and Nektarios Aligiannis. "Untargeted Ultrahigh-Performance Liquid Chromatography-Hybrid Quadrupole-Orbitrap Mass Spectrometry (UHPLC-HRMS) Metabolomics Reveals Propolis Markers of Greek and Chinese Origin." Molecules 26, no. 2 (January 16, 2021): 456. http://dx.doi.org/10.3390/molecules26020456.

Full text
Abstract:
Chemical composition of propolis depends on the plant source and thus on the geographic and climatic characteristics of the site of collection. The aim of this study was to investigate the chemical profile of Greek and Chinese propolis extracts from different regions and suggest similarities and differences between them. Untargeted ultrahigh-performance liquid chromatography coupled to hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-HRMS) method was developed and 22 and 23 propolis samples from Greece and China, respectively, were analyzed. The experimental data led to the observation that there is considerable variability in terms of quality of the distinctive propolis samples. Partial least squares - discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed and allowed the identification of significant features for sample discrimination, adding relevant information for the identification of class-determining metabolites. Chinese samples overexpressed compounds that are characteristic of the poplar type propolis, whereas Greek samples overexpress the latter and the diterpenes characteristic of the Mediterranean propolis type.
APA, Harvard, Vancouver, ISO, and other styles
27

Lee, Loong Chuen, Choong-Yeun Liong, and Abdul Aziz Jemain. "Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps." Analyst 143, no. 15 (2018): 3526–39. http://dx.doi.org/10.1039/c8an00599k.

Full text
APA, Harvard, Vancouver, ISO, and other styles
28

Brixner Riça, Larissa, Ornella Sari Cassol, Alexandre Rieger, and Valeriano Antonio Corbellini. "Discrimination of healthy and colorectal cancer patients using FTIR and PLS-DA." Revista Jovens Pesquisadores 9, no. 2 (July 6, 2019): 115–30. http://dx.doi.org/10.17058/rjp.v9i2.13372.

Full text
Abstract:
Spectroscopic methods have already been used as effective tools in several studies involving the detection of cancer. Fourier transform infrared spectroscopy (FTIR) has already been applied in the discrimination of cancer cells and tissues or blood of patients with the disease, observing that this technique requires the use of chemometric algorithms to obtain such results. The aim of this study was to employ a partial least squares discriminant analysis (PLS-DA) with FTIR data in the discrimination of plasma samples from patients with colorectal cancer (RCC) and healthy individuals. Multivariate analysis was performed using PLS-DA of the sample triplicates (n=90) with different types of processing. The best PLS-DA condition was obtained using the 1st derivative, 1 orthogonal signal correction (OSC) and no pre-processing. With 1 factor only, the model presented a mean square error of cross-validation (RMSECV) of 0.0004 and coefficient of determination (r^2) of 1.0000. The accuracy, precision and sensitivity of the model were 100%.
APA, Harvard, Vancouver, ISO, and other styles
29

Paradowska, Katarzyna, Marta Katarzyna Jamróz, Mariola Kobyłka, Ewelina Gowin, Paulina Mączka, Robert Skibiński, and Łukasz Komsta. "Detection of Drug Active Ingredients by Chemometric Processing of Solid-State NMR Spectrometry Data—The Case of Acetaminophen." Journal of AOAC INTERNATIONAL 95, no. 3 (May 1, 2012): 704–7. http://dx.doi.org/10.5740/jaoacint.sge_paradowska.

Full text
Abstract:
Abstract This paper presents a preliminary study in building discriminant models from solid-state NMR spectrometry data to detect the presence of acetaminophen in over-the-counter pharmaceutical formulations. The dataset, containing 11 spectra of pure substances and 21 spectra of various formulations, was processed by partial least squares discriminant analysis (PLS-DA). The model found coped with the discrimination, and its quality parameters were acceptable. It was found that standard normal variate preprocessing had almost no influence on unsupervised investigation of the dataset. The influence of variable selection with the uninformative variable elimination by PLS method was studied, reducing the dataset from 7601 variables to around 300 informative variables, but not improving the model performance. The results showed the possibility to construct well-working PLS-DA models from such small datasets without a full experimental design.
APA, Harvard, Vancouver, ISO, and other styles
30

Sibanda, M., O. Mutanga, L. S. Magwaza, T. Dube, S. T. Magwaza, A. O. Odindo, A. Mditshwa, and P. L. Mafongoya. "DISCRIMINATION OF TOMATO PLANTS (SOLANUM LYCOPERSICUM) GROWN UNDER ANAEROBIC BAFFLED REACTOR EFFLUENT, NITRIFIED URINE CONCENTRATE AND COMMERCIAL HYDROPONIC FERTILIZER REGIMES USING MULTI-SOURCE SATELLITE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 1023–29. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-1023-2019.

Full text
Abstract:
Abstract. We evaluate the detection and discriminative strength of three different satellite spectral settings, namely, HyspIRI, the forthcoming Landsat 9 and Sentinel 2-MSI, in mapping tomato (Solanum lycopersicum) plants grown under hydroponic system using humanexcreta derived materials (HEDM), namely, anaerobic baffled reactor (ABR) effluent and nitrified urine concentrate (NUC) and commercial hydroponic fertilizer mix (CHFM) as nutrient sources. Partial least squares – discriminant analysis (PLS-DA) and discriminant analysis (DA) were applied to discriminate tomatoes grown under these different nutrient sources. Results of this study showed that spectral settings of HyspIRI sensor can better discriminate tomatoes grown under different fertilizer regimes when compared to Landsat 9 OLI and Sentinel-2 MSI spectral configurations. For instance, based on DA algorithm, HyspIRI exhibited high overall accuracy of 0.99 and a kappa statistic of 0.99 whereas Landsat OLI and Sentinel-2 MSI exhibited over accuracies of 0.94 and 0.95 as well as kappa statistics of 0.79 and 0.85, respectively. Further, the performance of DA was significantly different (α = 0.05) from that of PLS-DA based on the MaNemar tests. Overall, the performance of HyspIRI, Landsat 9 OLI-2 and Sentinel-2 MSI data seem to bring new opportunities for crop monitoring at farm scale.
APA, Harvard, Vancouver, ISO, and other styles
31

Qiu, Guangjun, Enli Lü, Ning Wang, Huazhong Lu, Feiren Wang, and Fanguo Zeng. "Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis." Applied Sciences 9, no. 8 (April 12, 2019): 1530. http://dx.doi.org/10.3390/app9081530.

Full text
Abstract:
Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.
APA, Harvard, Vancouver, ISO, and other styles
32

Ortiz, Alberto, Lucía León, Rebeca Contador, and David Tejerina. "Near-Infrared Spectroscopy (NIRS) as a Tool for Classification of Pre-Sliced Iberian Salchichón, Modified Atmosphere Packaged (MAP) According to the Official Commercial Categories of Raw Meat." Foods 10, no. 8 (August 12, 2021): 1865. http://dx.doi.org/10.3390/foods10081865.

Full text
Abstract:
This study evaluates near-infrared spectroscopy (NIRS) feasibility in combination with various pre-treatments and chemometric approaches for pre-sliced Iberian salchichón under modified atmosphere (MAP) classification according to the official commercial category (defined by the combination of genotype and feeding regime) of the raw material used for its manufacturing (Black and Red purebred Iberian and Iberian × Duroc crossed (50%) pigs, respectively, reared outdoors in a Montanera system and White Iberian × Duroc crossed (50%) pigs with feed based on commercial fodder) without opening the package. In parallel, NIRS feasibility in combination with partial least squares regression (PLSR) to predict main quality traits was assessed. The best-fitting models developed by means of partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) yielded high discriminant ability and thus offered a tool to support the assignment of pre-sliced MAP Iberian salchichón according to the commercial category of the raw material. In addition, good predictive ability for C18:3 n-3 was obtained, which may help to support quality control.
APA, Harvard, Vancouver, ISO, and other styles
33

Liu, Wenjing, Zhaotian Sun, Jinyu Chen, and Chuanbo Jing. "Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models." Journal of Spectroscopy 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/1603609.

Full text
Abstract:
Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.
APA, Harvard, Vancouver, ISO, and other styles
34

Campmajó, Cayero, Saurina, and Núñez. "Classification of Hen Eggs by HPLC-UV Fingerprinting and Chemometric Methods." Foods 8, no. 8 (August 1, 2019): 310. http://dx.doi.org/10.3390/foods8080310.

Full text
Abstract:
Hen eggs are classified into four groups according to their production method: Organic, free-range, barn, or caged. It is known that a fraudulent practice is the misrepresentation of a high-quality egg with a lower one. In this work, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs. non-organic hen eggs) and demonstrating the suitability of the proposed method.
APA, Harvard, Vancouver, ISO, and other styles
35

Tran, Thi Hue, Quoc Toan Tran, Thi Thao Ta, and Si Hung Le. "Geographical origin identification of teas using UV-VIS spectroscopy." E3S Web of Conferences 265 (2021): 05013. http://dx.doi.org/10.1051/e3sconf/202126505013.

Full text
Abstract:
In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone, which represents the final product as ingested by the consumers. For this purpose, total of 125 tea samples from different geographical provines of Vietnam have been analyzed in UV-Vis spectroscopy associated with multivariate statistical methods. Principal Component Analysis-Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN) were compared to construct the identification model. The experimental results showed that the performance of ANN model was better than PCA-DA and PLS-DA model. The optimal ANN model was achieved when neuron numbers were 200, identification rate being 99% in the training set and 84% predition set. The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation.
APA, Harvard, Vancouver, ISO, and other styles
36

Zhang, Yan, Ping Wang, Youdong Xu, Xianli Meng, and Yi Zhang. "Metabolomic Analysis of Biochemical Changes in the Plasma of High-Fat Diet and Streptozotocin-Induced Diabetic Rats after Treatment with Isoflavones Extract of Radix Puerariae." Evidence-Based Complementary and Alternative Medicine 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/4701890.

Full text
Abstract:
The main purpose of this study was to investigate the protective effects of total isoflavones from Radix Puerariae (PTIF) in diabetic rats. Diabetes was induced by a high-fat diet and intraperitoneal injection of low-dose streptozotocin (STZ; 40 mg/kg). At 26 weeks onwards, PTIF 421 mg/kg was administrated to the rats once daily consecutively for 10 weeks. Metabolic profiling changes were analyzed by Ultraperformance Liquid Chromatography-Quadrupole-Exactive Orbitrap-Mass Spectrometry (UPLC-Q-Exactive Orbitrap-MS). The principal component discriminant analysis (PCA-DA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used for multivariate analysis. Moreover, free amino acids in serum were determined by high-performance liquid chromatography with fluorescence detector (HPLC-FLD). Additionally, oxidative stress and inflammatory cytokines were evaluated. Eleven potential metabolite biomarkers, which are mainly related to the coagulation, lipid metabolism, and amino acid metabolism, have been identified. PCA-DA scores plots indicated that biochemical changes in diabetic rats were gradually restored to normal after administration of PTIF. Furthermore, the levels of BCAAs, glutamate, arginine, and tyrosine were significantly increased in diabetic rats. Treatment with PTIF could regulate the disturbed amino acid metabolism. Consequently, PTIF has great therapeutic potential in the treatment of DM by improving metabolism disorders and inhibiting oxidative damage.
APA, Harvard, Vancouver, ISO, and other styles
37

Muñoz-Redondo, José Manuel, Belén Puertas, Gema Pereira-Caro, José Luis Ordóñez-Díaz, María José Ruiz-Moreno, Emma Cantos-Villar, and José Manuel Moreno-Rojas. "A Statistical Workflow to Evaluate the Modulation of Wine Metabolome and Its Contribution to the Sensory Attributes." Fermentation 7, no. 2 (May 5, 2021): 72. http://dx.doi.org/10.3390/fermentation7020072.

Full text
Abstract:
A data-processing and statistical analysis workflow was proposed to evaluate the metabolic changes and its contribution to the sensory characteristics of different wines. This workflow was applied to rosé wines from different fermentation strategies. The metabolome was acquired by means of two high-throughput techniques: gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) for volatile and non-volatile metabolites, respectively, in an untargeted approach, while the sensory evaluation of the wines was performed by a trained panel. Wine volatile and non-volatile metabolites modulation was independently evaluated by means of partial least squares discriminant analysis (PLS-DA), obtaining potential markers of the fermentation strategies. Then, the complete metabolome was integrated by means of sparse generalised canonical correlation analysis discriminant analysis (sGCC-DA). This integrative approach revealed a high link between the volatile and non-volatile data, and additional potential metabolite markers of the fermentation strategies were found. Subsequently, the evaluation of the contribution of metabolome to the sensory characteristics of wines was carried out. First, the all-relevant metabolites affected by the different fermentation processes were selected using PLS-DA and random forest (RF). Each set of volatile and non-volatile metabolites selected was then related to the sensory attributes of the wines by means of partial least squares regression (PLSR). Finally, the relationships among the three datasets were complementary evaluated using regularised generalised canonical correlation analysis (RGCCA), revealing new correlations among metabolites and sensory data.
APA, Harvard, Vancouver, ISO, and other styles
38

Sun, Fei, Yu Chen, Yunqi Qiu, Shumei Wang, and Shengwang Liang. "Systematic vs. stepwise parameter optimization for discriminant model development: A case study of differentiating Pinellia ternata from Pinellia pedatisecta with near infrared spectroscopy." Journal of Near Infrared Spectroscopy 28, no. 5-6 (June 14, 2020): 287–97. http://dx.doi.org/10.1177/0967033520924579.

Full text
Abstract:
Near infrared (NIR) spectroscopy is an effective technique for adulteration detection in traditional Chinese medicine. The aim is to develop a discriminant model with the aid of chemometrics tools. The discriminant model is conventionally established by the means of stepwise optimization. This approach is often limited to trial-and-error and considered as a burden. In this study, a systematic optimization approach was proposed to develop the discriminant model with the aid of the design of experiment tools and applied to a case study of differentiating Pinellia ternata from Pinellia pedatisecta and adulterated Pinellia ternata using NIR spectroscopy. Spectral pretreatment, variable selection, and discriminant methods were identified as critical factors. The classification accuracy and no-error rate of the calibration set, cross-validation, and the prediction set were calculated to evaluate the performance of discriminant models. A full factorial design was applied to analyze the effect of critical factors at different levels on the model performance and optimize these factors. Three discriminant models including discriminant analysis coupled with principal component analysis (PCA-DA), partial least squares – discriminant analysis (PLS-DA), and k-nearest neighbors (KNN) were obtained by systematic optimization. The performance of PCA-DA and PLS-DA models obtained by systematic optimization was very good, and no samples were misclassified, which were better than those obtained by stepwise optimization. The performance of the KNN model obtained by systematic optimization was not desired and it was equal to that obtained by stepwise optimization. The results showed that Pinellia ternata could be successfully discriminated from Pinellia pedatisecta and adulterated Pinellia ternata by the PCA-DA and PLS-DA models. Compared to the stepwise optimization approach, the systematic optimization approach can improve the PCA-DA and PLS-DA model performance for differentiating Pinellia ternata from Pinellia pedatisecta and adulterated Pinellia ternata.
APA, Harvard, Vancouver, ISO, and other styles
39

SONG, HAN, FENG LI, PEIWEN GUANG, XINHAO YANG, HUANYU PAN, and FURONG HUANG. "Detection of Aflatoxin B1 in Peanut Oil Using Attenuated Total Reflection Fourier Transform Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis and Support Vector Machine Models." Journal of Food Protection 84, no. 8 (March 12, 2021): 1315–20. http://dx.doi.org/10.4315/jfp-20-447.

Full text
Abstract:
ABSTRACT This study was conducted to establish a rapid and accurate method for identifying aflatoxin contamination in peanut oil. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with either partial least squares discriminant analysis (PLS-DA) or a support vector machine (SVM) algorithm were used to construct discriminative models for distinguishing between uncontaminated and aflatoxin-contaminated peanut oil. Peanut oil samples containing various concentrations of aflatoxin B1 were examined with an ATR-FTIR spectrometer. Preprocessed spectral data were input to PLS-DA and SVM algorithms to construct discriminative models for aflatoxin contamination in peanut oil. SVM penalty and kernel function parameters were optimized using grid search, a genetic algorithm, and particle swarm optimization. The PLS-DA model established using spectral data had an accuracy of 94.64% and better discrimination than did models established based on preprocessed data. The SVM model established after data normalization and grid search optimization with a penalty parameter of 16 and a kernel function parameter of 0.0359 had the best discrimination, with 98.2143% accuracy. The discriminative models for aflatoxin contamination in peanut oil established by combining ATR-FTIR spectral data and nonlinear SVM algorithm were superior to the linear PLS-DA models. HIGHLIGHTS
APA, Harvard, Vancouver, ISO, and other styles
40

Lottering, Romano Trent, Mackyla Govender, Kabir Peerbhay, and Shenelle Lottering. "Comparing partial least squares (PLS) discriminant analysis and sparse PLS discriminant analysis in detecting and mapping Solanum mauritianum in commercial forest plantations using image texture." ISPRS Journal of Photogrammetry and Remote Sensing 159 (January 2020): 271–80. http://dx.doi.org/10.1016/j.isprsjprs.2019.11.019.

Full text
APA, Harvard, Vancouver, ISO, and other styles
41

Chen, Pei, James M. Harnly, and Peter de B. Harrington. "Flow Injection Mass Spectroscopic Fingerprinting and Multivariate Analysis for Differentiation of Three Panax Species." Journal of AOAC INTERNATIONAL 94, no. 1 (January 1, 2011): 90–99. http://dx.doi.org/10.1093/jaoac/94.1.90.

Full text
Abstract:
Abstract This study describes the use of spectral fingerprints acquired by flow injection (FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min.
APA, Harvard, Vancouver, ISO, and other styles
42

Jiang, Hui, Jian Liu, Ting Wang, Jia-rong Gao, Yue Sun, Chuan-bing Huang, Mei Meng, and Xiu-juan Qin. "Mechanism of Xinfeng Capsule on Adjuvant-Induced Arthritis via Analysis of Urinary Metabolomic Profiles." Autoimmune Diseases 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5690935.

Full text
Abstract:
We aimed to explore the potential effects of Xinfeng capsule (XFC) on urine metabolic profiling in adjuvant-induced arthritis (AA) rats by using gas chromatography time-of-flight mass spectrometry (GC-TOF/MS). GC-TOF/MS technology was combined with multivariate statistical approaches, such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA). These methods were used to distinguish the healthy group, untreated group, and XFC treated group and elucidate potential biomarkers. Nine potential biomarkers such as hippuric acid, adenine, and L-dopa were identified as potential biomarkers, indicating that purine metabolism, fat metabolism, amino acid metabolism, and energy metabolism were disturbed in AA rats. This study demonstrated that XFC is efficacious for RA and explained its potential metabolomics mechanism.
APA, Harvard, Vancouver, ISO, and other styles
43

Nieuwoudt, Michél K., Stephen E. Holroyd, Cushla M. McGoverin, M. Cather Simpson, and David E. Williams. "Screening for Adulterants in Liquid Milk Using a Portable Raman Miniature Spectrometer with Immersion Probe." Applied Spectroscopy 71, no. 2 (July 20, 2016): 308–12. http://dx.doi.org/10.1177/0003702816653130.

Full text
Abstract:
A portable Raman system with an immersion fiber optic probe was assessed for point-of-collection screening for the presence of adulterants in liquid milk. N-rich adulterants and sucrose were measured in this proof-of-concept demonstration. Reproducibility, limit of detection range and other figures of merit such as specificity, sensitivity, ratio of predicted to standard deviation, standard error of prediction and root mean squared error for cross validation were determined from partial least squares (PLS) and partial least squares with discriminant analysis (PLS-DA) calibrations of milk mixtures containing 50–1000 ppm (parts per million) of melamine, ammonium sulphate, Dicyandiamide, urea and sucrose. The spectra were recorded by immersing the fiber optic probe directly in the milk solutions. Despite the high scattering background which was easily and reliably estimated and subtracted, the reproducibility for four N-rich compounds averaged to 11% residual standard deviation (RSD) and to 5% RSD for sucrose. PLS calibration models predicted the concentrations of separate validation sets with standard errors of prediction of between 44 and 76 ppm for the four N-rich compounds and 0.17% for sucrose. The sensitivity and specificity of the PLS-DA calibration were 92% and 89%, respectively. The study shows promise for use of portable mini Raman systems for routine rapid point-of-collection screening of liquid milk for the presence of adulterants, without the need for sample preparation or addition of chemicals.
APA, Harvard, Vancouver, ISO, and other styles
44

Núñez, Nerea, Xavi Collado, Clara Martínez, Javier Saurina, and Oscar Núñez. "Authentication of the Origin, Variety and Roasting Degree of Coffee Samples by Non-Targeted HPLC-UV Fingerprinting and Chemometrics. Application to the Detection and Quantitation of Adulterated Coffee Samples." Foods 9, no. 3 (March 24, 2020): 378. http://dx.doi.org/10.3390/foods9030378.

Full text
Abstract:
In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.
APA, Harvard, Vancouver, ISO, and other styles
45

Azcarate, Silvana Mariela, Miguel Angel Cantarelli, Eduardo Jorge Marchevsky, and José Manuel Camiña. "Evaluation of Geographic Origin of Torrontés Wines by Chemometrics." Journal of Food Research 2, no. 5 (August 11, 2013): 48. http://dx.doi.org/10.5539/jfr.v2n5p48.

Full text
Abstract:
<p>This work discusses the determination of the provenance of commercial Torrontés wines from different Argentinean provinces (Mendoza, San Juan, Salta and Rio Negro) by the use of UV-vis spectroscopy and chemometric techniques. In order to find classification models, wines (n = 80) were analyzed using UV-Vis region of the electromagnetic spectrum. Principal component analysis (PCA), linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA) were used to classify Torrontés wines according to their geographical origin. Classification rates obtained were highly satisfactory. The PLS-DA and LDA calibration models showed that 100% of the Mendoza, San Juan, Salta and Rio Negro Torrontés wine samples had been correctly classified. These results demonstrate the potential use of UV spectroscopy with chemometric data analysis as a method to classify Torrontés wines according to their geographical origin, a procedure which requires low-cost equipment and short-time analysis in comparison with other techniques.</p>
APA, Harvard, Vancouver, ISO, and other styles
46

Du, Zhixia, Jinhua Li, Xiang Zhang, Jin Pei, and Linfang Huang. "An Integrated LC-MS-Based Strategy for the Quality Assessment and Discrimination of Three Panax Species." Molecules 23, no. 11 (November 15, 2018): 2988. http://dx.doi.org/10.3390/molecules23112988.

Full text
Abstract:
: The quality assessment and discrimination of Panax herbs are very challenging to perform due to the complexity and variability of their chemical compositions. An integrated strategy was established using UHPLC-Q-Exactive/HRMS and HPLC-ESI-MS/MS to achieve an accurate, rapid, and comprehensive qualitative and quantitative analysis of Panax japonicas (PJ), Panax japonicus var. major (PM), and Panax zingiberensis (PZ). Additionally, discrimination among the three species was explored with partial least squares–discriminant analysis (PLS-DA) and orthogonal partial least squares–discriminant analysis (OPLS-DA) score plots. A total of 101 compounds were plausibly or unambiguously identified, including 82 from PJ, 78 from PM, and 67 from PZ. Among them, 16 representative ginsenosides were further quantified in three herbs. A clear discrimination between the three species was observed through a multivariate statistical analysis on the quantitative data. Nine compounds that allowed for discrimination between PJ, PM, and PZ were discovered. Notably, ginsenoside Rf (G-Rf), ginsenoside F3 (G-F3), and chikusetsu saponin IV (CS-IV) were the three most important differential compounds. The research indicated that the integrated LC-MS-based strategy can be applied for the quality assessment and discrimination of the three Panax herbs.
APA, Harvard, Vancouver, ISO, and other styles
47

Xu, Shaochen, Jiangyu Zhu, Qi Zhao, Jin Gao, Huining Zhang, and Boran Hu. "Quality evaluation of Cabernet Sauvignon wines in different vintages by 1H nuclear magnetic resonance-based metabolomics." Open Chemistry 19, no. 1 (January 1, 2021): 385–99. http://dx.doi.org/10.1515/chem-2020-0126.

Full text
Abstract:
Abstract A proton nuclear magnetic resonance (NMR)-based metabolomic study was used to characterize 2009, 2010, 2011, and 2012 vintages of Cabernet Sauvignon wines from Ningxia, which were vinified using the same fermentation technique. The pattern recognition methods of principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA) clearly distinguished between the different vintages of wine driven by the following metabolites: valine, 2,3-butanediol, ethyl acetate, proline, succinic acid, lactic acid, acetic acid, glycerol, gallic acid, and choline. The PLS-DA loading plots also differentiated among the metabolites of different vintages. In the 2009 vintage wines, we found the highest levels of gallic acid, valine, proline, and 2,3-butanediol. The 2011 vintage wines contained the highest levels of lactic acid, and the highest levels of ethyl acetate, succinic acid, glycerol, and choline were observed in the 2012 vintage wines. We selected eight metabolites from the 1H NMR spectra that were quantified according to their peak areas, and the concentrations were in agreement with the results of PLS-DA and OPLS-DA analyses.
APA, Harvard, Vancouver, ISO, and other styles
48

Steidle Neto, A. J., D. C. Lopes, J. V. Toledo, S. Zolnier, and T. G. F. Silva. "Classification of sugarcane varieties using visible/near infrared spectral reflectance of stalks and multivariate methods." Journal of Agricultural Science 156, no. 4 (May 2018): 537–46. http://dx.doi.org/10.1017/s0021859618000539.

Full text
Abstract:
AbstractThe use of fast and non-destructive techniques for identifying sugarcane varieties enables the development of automatic sorting systems, contributing towards improving pre-processing steps in the alcohol and sugar industries. In this context, principal component analysis (PCA), factorial discriminant analysis (FDA), stepwise forward discriminant analysis (SFDA) and partial least-squares discriminant analysis (PLS-DA) were used to classify four Brazilian sugarcane varieties based on visible/near infrared (Vis/NIR) spectral reflectance measurements (450–1000 nm range) of stalks. All wavelengths contributed towards discriminating the sugarcane varieties, but the 600–750 nm range was most relevant. When evaluating PCA results considering the four sugarcane varieties, two of them overlapped and it was only possible to use classifiers of three varieties. Factorial discriminant analysis, PLS-DA and SFDA reached correct classifications of 0.81, 0.82 and 0.74, respectively, when considering the external validation data and the four sugarcane varieties evaluated. Results showed that Vis/NIR spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast sugarcane variety classification, which can be used in the agro-food industry or directly in the field.
APA, Harvard, Vancouver, ISO, and other styles
49

Li, Yan Kun, and Shuo Yang. "Research on Serum Spectrum Analysis Model Applied to Pathema Identification." Advanced Materials Research 864-867 (December 2013): 486–89. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.486.

Full text
Abstract:
The SELDI-TOF MS serum peptide profiles of normal and malignant tumor samples were studied by pattern recognition method. In this study, Partial Least Squares-Discriminate Analysis (PLS-DA) combined with consensus classification model was constructed to predict practical serum samples and compared with the results of principal component analysis (PCA) method. The correctness of consensus PLS-DA classification model for normal and malignant samples was 90% and 84% respectively. So the approach proposed was proved to be a reliable and practicable method for cancer identification.
APA, Harvard, Vancouver, ISO, and other styles
50

Du, Lijuan, Weiying Lu, Boyan Gao, Jing Wang, and Liangli (Lucy) Yu. "Authenticating Raw from Reconstituted Milk Using Fourier Transform Infrared Spectroscopy and Chemometrics." Journal of Food Quality 2019 (March 24, 2019): 1–6. http://dx.doi.org/10.1155/2019/5487890.

Full text
Abstract:
Fourier transform infrared (FTIR) spectroscopy combined with chemometrics was used to authenticate raw milk from their reconstituted counterparts. First, the explanatory principal component analysis (PCA) was employed to visualize the relationship between raw and reconstituted milk samples. However, the degree of separation between two sample classes was not significant according to direct observation of the scores plot, indicating FTIR spectra may contain complicated chemical information. Second, partial least-squares-discriminant analysis (PLS-DA) that incorporate additional class membership information as modelling input was further calculated. The PLS-DA scores yielded clear separation between two classes of samples. Additionally, possible components from the model loading were studied, and the PLS-DA model was validated internally under the model population analysis framework, as well as externally using an independent test set. This study gave insights into the authentication of milk using FTIR spectroscopy with chemometrics techniques.
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography