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1
Yan, Si-Min, Zi-Feng Hu, Cheng-Xin Wu, et al. "Electronic Tongue Combined with Chemometrics to Provenance Discrimination for a Green Tea (Anji-White Tea)." Journal of Food Quality 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/3573197.
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This paper aims to provide a stable instrumental method for provenance discrimination of Anji-White tea by its distinctive taste. 180 authentic and 60 counterfeit white tea samples were collected for specific geographical origins detection; all of them were measured by electronic tongue coupled with 7 independent sensors. Therefore, chemometrics methods, principal component analysis (PCA), and partial least squares discriminant analysis (PLSDA) were performed in classification. The PCA distribution shows that, in provenance analysis, PCA is a simple and reliable tool for small sample sets, but for sets with large objects, PCA seems powerless in classification. Therefore, PLSDA was applied to develop a classification model. The prediction sensitivity and specificity of PLSDA, respectively, reached 0.917 and 0.950. This study demonstrates the potential of combining electronic tongue system and chemometrics as an effective tool for specific geographical origins detection in Anji-White tea.
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Ma, Yue, Yichao Xu, Hui Yan, and Guozheng Zhang. "On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology." Journal of Near Infrared Spectroscopy 29, no. 4 (2021): 207–15. http://dx.doi.org/10.1177/0967033521999745.
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The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties. The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.
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Raimondo, Mariangela, Anna Borioni, Francesca Prestinaci, Isabella Sestili, and Maria Cristina Gaudiano. "A NIR, 1H-NMR, LC-MS and chemometrics pilot study on the origin of carvedilol drug substances: a tool for discovering falsified active pharmaceutical ingredients." Analytical Methods 14, no. 14 (2022): 1396–405. http://dx.doi.org/10.1039/d1ay02035h.
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The study explores the profile of carvedilol active ingredients by NIR, 1H-NMR and LC-MS Q-TOF and data were analysed by PCA, cluster analysis and PLSDA. Two different groups of manufacturers based on the geographical area are classified.
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Fu, Hai-Yan, Shuang-Yan Huan, Lu Xu, et al. "Moving Window Partial Least-Squares Discriminant Analysis for Identification of Different Kinds of Bezoar Samples by near Infrared Spectroscopy and Comparison of Different Pattern Recognition Methods." Journal of Near Infrared Spectroscopy 15, no. 5 (2007): 291–97. http://dx.doi.org/10.1255/jnirs.743.
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Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.
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Fu, Haiyan, Yao Fan, Xu Zhang, et al. "Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods." Journal of Analytical Methods in Chemistry 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/727589.
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As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information ofHibiscus mutabilisL. andBerberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines.
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Mudasir Majeed, Mudasir Majeed, Abdullah Ijaz Hussain Abdullah Ijaz Hussain, Shahzad Ali Shahid Chatha Shahzad Ali Shahid Chatha, and Ghulam Mustafa Kamal and Qasim Ali Ghulam Mustafa Kamal and Qasim Ali. "Discrimination of Mungbean Cultivars/Varieties Based on Minor Saccharides Composition by HPLC Coupled with Multivariate Statistical Analysis." Journal of the chemical society of pakistan 42, no. 3 (2020): 418. http://dx.doi.org/10.52568/000643.
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Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.
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Mudasir Majeed, Mudasir Majeed, Abdullah Ijaz Hussain Abdullah Ijaz Hussain, Shahzad Ali Shahid Chatha Shahzad Ali Shahid Chatha, and Ghulam Mustafa Kamal and Qasim Ali Ghulam Mustafa Kamal and Qasim Ali. "Discrimination of Mungbean Cultivars/Varieties Based on Minor Saccharides Composition by HPLC Coupled with Multivariate Statistical Analysis." Journal of the chemical society of pakistan 42, no. 3 (2020): 418. http://dx.doi.org/10.52568/000643/jcsp/42.03.2020.
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Present study reports the potential use of HPLC coupled with principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA), for differentiation of approved mungbean variety from the promising lines based on minor saccharides profiles. A total of 48 mungbean samples from one approved variety and seven promising lines were analyzed for minor saccharides using HPLC and multivariate statistical analysis. PCA showed a clear separation among the classes. PLSDA was conducted to extract the variables that were responsible for the separation of mungbean approved variety from the lines. Maltoheptaose, maltohexaose, maltopentaose, maltotretraose, maltitol, maltose, mannitole, betaine varied significantly while stachyose, raffinose, sucrose, lectitol, dulcitol, xylitol, galactose showed non-significant differences. Maltoheptaose, maltohexaose, maltotretraose, maltitol, mannitole and galactose were found as the most abundant compounds while stachyose, raffinose, sucrose, lectitol and betaine were found less abundant in all lines and approved variety of V. radiata. The study highlights metabolic variation among mungbean variety and lines for minor saccharides profiles and its usefulness for consumers to choose for their desired variety or line as well as for breeders to look into the genetic factors responsible for this variation.
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Fu, Haiyan, Qiong Shi, Liuna Wei, et al. "Rapid Recognition of Geoherbalism and Authenticity of a Chinese Herb by Data Fusion of Near-Infrared Spectroscopy (NIR) and Mid-Infrared (MIR) Spectroscopy Combined with Chemometrics." Journal of Spectroscopy 2019 (April 30, 2019): 1–9. http://dx.doi.org/10.1155/2019/2467185.
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Fourier transform near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy play important roles in all fingerprint techniques because of their unique characteristics such as reliability, versatility, precision, and ease of measurement. In this paper, a supervised pattern recognition method based on the PLSDA algorithm by NIR and the NIR-MIR fusion spectra has been established to identify geoherbalism of Angelica dahurica from different regions and authenticity of Corydalis yanhusuo W. T. Wang. Comparing principle component analysis (PCA) cannot successfully identify geographical origins of Angelica dahurica. Linear discriminant analysis (LDA) also hardly distinguishes those origins. Furthermore, the PLSDA model based on the data fusion of NIR and IR was more accurate and efficient. But, the identification of authenticity of Corydalis yanhusuo W. T. Wang was still inaccurate in the PLSDA model. Consequently, data fusion of NIR-MIR original spectra combined with moving window partial least-squares discriminant analysis was firstly used and showed perfect properties on authenticity and adulteration discrimination of Corydalis yanhusuo W. T. Wang. It indicated that data fusion of NIR-MIR spectra combined with MWPLSDA could be considered as the promising tool for rapid discrimination of the geoherbalism and authenticity of more Chinese herbs in the future.
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Rui, Wen, Hong Yuan Chen, Yi Fan Feng, Zhong Feng Shi, and Miao Miao Jiang. "Comparision of Bupleurum scorzoneri folium Willd. Grouping from Different Habitats Based on Pattern Recognition with R Software." Advanced Materials Research 393-395 (November 2011): 1139–42. http://dx.doi.org/10.4028/www.scientific.net/amr.393-395.1139.
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Bupleurum scorzoneri folium Willd.(BSFW) is a traditional Chinese medicine which is widely distributed in China. To evaluate the quality of BSFW from different habitats, samples from 5 different areas in China were determined by UPLC/MS. The chemical data were dealed with hierarchical clustering, PCA, SPCA, PLSDA and SPLSDA using R software. The results show that these pattern recognition methods can fully reflect the chemical composition of different areas of BSFW, which make it possible to control the quality.
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Rittiron, Ronnarit, Sureeporn Narongwongwattana, Unaruj Boonprakob, and Worapa Seehalak. "Rapid and nondestructive detection of watercore and sugar content in Asian pear by near infrared spectroscopy for commercial trade." Journal of Innovative Optical Health Sciences 07, no. 06 (2014): 1350073. http://dx.doi.org/10.1142/s1793545813500739.
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Watercore and sugar content are internal qualities which are impossible for exterior determination. Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectroscopy (NIR) technique. A total of 93 samples of Asian pear variety "SH-078" were used. For sugar content, spectrum of each fruit was measured in the short wavelength region (700–1100 nm) in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer. Prediction equation was performed by multiple linear regression. The result showed Standard Error of Prediction (SEP) = 0.58°Bx, and Bias=0.11. The result from t-test showed that sugar content predicted by NIR was not significantly different from the value analyzed by refractometer at 95% confidence. For watercore disorder, NIR measurement was performed over the short wavelength range (700–850 nm) in the transmission mode. The first derivative spectra were correlated with internal qualities. Then principle component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to perform discrimination models. The accuracy of the PCA model was greater than the PLSDA one. The scores from PC1 were separated into two boundaries, one predicted rejected pears with 100% classification accuracy, and the other was accepted pears with 92% accuracy. The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.
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Holandino, Carla, Michelle Nonato de Oliveira Melo, Adriana Passos Oliveira, et al. "Metabolomic analysis of Viscum album L homeopathic tinctures and antitumor studies in 3D spheroid models." International Journal of High Dilution Research - ISSN 1982-6206 18, no. 02 (2021): 18. http://dx.doi.org/10.51910/ijhdr.v18i02.996.
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Background The antitumoral efficacy of aqueous Viscum album extracts is attributed to the presence of lectins and viscotoxins. However, previous studies demonstrated an antitumoral activity of European V. album ethanolic homeopathic tinctures (VAHT) prepared according to homeopathic methodology. Aims To investigate the seasonal influences (summer and winter) in the metabolomic profile of V. album ssp. homeopathic mother tinctures (VAHT) and to evaluate the antitumoral activity of some VAHT in 3D tumor spheroid models. Methodology The following VAHT were prepared by ethanolic maceration: V. album ssp. album growing on Malus domestica, Quercus sp. and Ulmus sp.; V. album ssp. austriacum from Pinus sylvestris; V. album ssp. abietis from Abies alba. Chemical analyses were performed using liquid chromatography coupled to high-resolution mass spectrometry. Data was submitted to multivariate statistical analysis using principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLSDA) in Metaboanalyst platform. The antitumor potential of VAHT (0.5% v/v) was conducted in 3D tumor spheroid models (MDA-MB-231 cell line) by MTT for 72 hours. Results and discussion The PCA analysis explained 40% of data variation and clustered VAHT samples into 3 groups, emphasizing the chemical similarity between the botanical subspecies of V. album. Some key compounds were mainly responsible for this separation: pinobankasin hexose-pentose (V. album ssp. abietis); citric acid (V. album ssp. austriacum); malic acid (V. album ssp. album). The chemical differences among summer and winter samples, detected by PLSDA, were related to the Viscum album host trees. A significant reduction of 50% and 41% (p
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Hu, Xiaowen, Lingjie Yang, Zuxin Zhang, and Yanrong Wang. "Differentiation of alfalfa and sweet clover seeds via multispectral imaging." Seed Science and Technology 48, no. 1 (2020): 83–99. http://dx.doi.org/10.15258/sst.2020.48.1.11.
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It is hard to remove sweet clover seeds from alfalfa seed lots by conventional methods, affecting the purity of seed lots and resulting losses in for alfalfa hay production as well as seed yield. However, the discrimination of sweet clover seed contaminates in alfalfa seed lots is difficult without special training. In this study, multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to separate sweet clover and alfalfa seeds. Principal component analysis (PCA), linear discrimination analysis (LDA), partial least squares discriminant analysis (PLSDA), AdaBoost and support vector machine (SVM) methods were applied to classify seeds of sweet clover and alfalfa according to their morphological features and spectral traits or a combination thereof. The results showed that an excellent classification could be achieved based on a combination of morphological features and spectral data in a tested data set. Seed classification accuracy was up to 99.58% in a validation set with the LDA model, which was better than the PLSDA (68.19%), AdaBoost (96.95%) and SVM (98.47%) models. Thus, multispectral imaging together with chemometric multivariate analysis is a promising technique to identify sweet clover seeds in alfalfa seed lots with high efficiency.
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Hark, Richard R., Chandra S. Throckmorton, Russell S. Harmon, et al. "Multianalyzer Spectroscopic Data Fusion for Soil Characterization." Applied Sciences 10, no. 23 (2020): 8723. http://dx.doi.org/10.3390/app10238723.
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The ability to rapidly conduct in-situ chemical analysis of multiple samples of soil and other geological materials in the field offers many advantages over a traditional approach that involves collecting samples for subsequent examination in the laboratory. This study explores the application of complementary spectroscopic analyzers and a data fusion methodology for the classification/discrimination of >100 soil samples from sites across the United States. Commercially available, handheld analyzers for X-ray fluorescence spectroscopy (XRFS), Raman spectroscopy (RS), and laser-induced breakdown spectroscopy (LIBS) were used to collect data both in the laboratory and in the field. Following a common data pre-processing protocol, principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were used to build classification models. The features generated by PLSDA were then used in a hierarchical classification approach to assess the relative advantage of information fusion, which increased classification accuracy over any of the individual sensors from 80-91% to 94% and 64-93% to 98% for the two largest sample suites. The results show that additional testing with data sets for which classification with individual analyzers is modest might provide greater insight into the limits of data fusion for improving classification accuracy.
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LIU, C., W. LIU, X. LU, et al. "Non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants using multispectral imaging and chemometric methods." Journal of Agricultural Science 154, no. 1 (2014): 1–12. http://dx.doi.org/10.1017/s0021859614001142.
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SUMMARYSoybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency.
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Lu, Yi, Changbao Yang, and Zhiguo Meng. "Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data." Remote Sensing 13, no. 7 (2021): 1280. http://dx.doi.org/10.3390/rs13071280.
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Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.
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Zhu, Hongyan, Aoife Gowen, Hailin Feng, Keping Yu, and Jun-Li Xu. "Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products." Sensors 20, no. 18 (2020): 5322. http://dx.doi.org/10.3390/s20185322.
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Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.
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Heryanto, Rudi, Yeni Herdiyeni, and Yuthika Rizqi Noviyanti. "Quality Control of Jati Belanda Leaves (Guazuma ulmifolia) using Image Analysis and Chemometrics." Jurnal Jamu Indonesia 1, no. 1 (2016): 1–9. http://dx.doi.org/10.29244/jji.v1i1.2.
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The quality of medicinal plants, such as Guazuma ulmifolia (jati belanda, JB), affects the quality of the herbal material derived from them, and can be determined using image analysis. The objective of this study is to investigate the possibility of using an image-generated spectrum and chemometrics as a method for quality control of Jati belanda leaves. Three different quality levels of JB leaves were determined, based on their harvesting time, and confirmed by total flavonoid content analysis. The images of JB samples were collected and reconstructed as a reflection spectrum using the Wiener estimation. The reconstructed spectrum had a goodness-of-fit coefficient of 0.9576 and a root-mean-square-error (RMSE) of 36.65%, compared to the experimental spectrum. Principal Component Analysis (PCA) was used to classify the JB reconstructed spectrum based on its quality. A score plot of two PCs that represented 98% variance was able to group the JB spectrum. Further analysis using Partial Least Squares-Discriminant Analysis (PLSDA) showed that the method can result in around 90% prediction success rate with external validation. This study indicates that image analysis and chemometrics could be used as quality control methods for herbal material.
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Heryanto, Rudi, Yeni Herdiyeni, and Yuthika Rizqi Noviyanti. "Quality Control of Jati Belanda Leaves (Guazuma ulmifolia) using Image Analysis and Chemometrics." Jurnal Jamu Indonesia 1, no. 1 (2016): 1–9. http://dx.doi.org/10.29244/jjidn.v1i1.30587.
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The quality of medicinal plants, such as Guazuma ulmifolia (jati belanda, JB), affects the quality of the herbal material derived from them, and can be determined using image analysis. The objective of this study is to investigate the possibility of using an image-generated spectrum and chemometrics as a method for quality control of Jati belanda leaves. Three different quality levels of JB leaves were determined, based on their harvesting time, and confirmed by total flavonoid content analysis. The images of JB samples were collected and reconstructed as a reflection spectrum using the Wiener estimation. The reconstructed spectrum had a goodness-of-fit coefficient of 0.9576 and a root-mean-square-error (RMSE) of 36.65%, compared to the experimental spectrum. Principal Component Analysis (PCA) was used to classify the JB reconstructed spectrum based on its quality. A score plot of two PCs that represented 98% variance was able to group the JB spectrum. Further analysis using Partial Least Squares-Discriminant Analysis (PLSDA) showed that the method can result in around 90% prediction success rate with external validation. This study indicates that image analysis and chemometrics could be used as quality control methods for herbal material.
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Socaciu, Carmen, Florinela Fetea, Floricuta Ranga, et al. "Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Coupled with Chemometrics, to Control the Botanical Authenticity and Quality of Cold-Pressed Functional Oils Commercialized in Romania." Applied Sciences 10, no. 23 (2020): 8695. http://dx.doi.org/10.3390/app10238695.
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Attenuated total reflectance-Fourier transform infrared ppectroscopy (ATR-FTIR) proved to be a reliable, rapid, and easy-to-use technique to evaluate vegetable oils quality and authenticity. The spectral range of the middle infrared region (MIR) of FTIR spectra, from 4000 to 600 cm−1, has been commonly used to fingerprint specific functional groups of lipids and their modified forms induced by oxidation of thermal treatment. The applicability of FTIR-MIR spectroscopy in assessing oil fingerprinting and quality parameters is crucially dependent on the chemometric methods, including calibrations with authentic samples. We report here the evaluation of seven types of cold-pressed functional oils (sunflower, pumpkin, hempseed, soybean, walnut, linseed, sea buckthorn) produced in Romania, provided directly from small enterprises (as genuine, process-controlled authentic samples) comparative to commercialized samples. Concomitantly, olive oils of similar claimed quality were investigated. The ATR-FTIR-MIR data were complemented by UV–Vis spectral fingerprints and multivariate analysis using Unscrambler X.10.4 and Metaboanalyst 4.0 software (e.g., PCA, PLSDA, cluster analysis, heatmap, Random forest analysis) and ANOVA post-hoc analysis using Fischer’s least significant difference. The integration of spectral and chemometric analysis proved to offer valuable criteria for their botanical group recognition, individual authenticity, and quality, easy to be applied for large cohorts of commercialized oils.
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Popa, Ramona Maria, Florinela Fetea, and Carmen Socaciu. "ATR-FTIR-MIR Spectrometry and Pattern Recognition of Bioactive Volatiles in Oily versus Microencapsulated Food Supplements: Authenticity, Quality, and Stability." Molecules 26, no. 16 (2021): 4837. http://dx.doi.org/10.3390/molecules26164837.
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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.
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Surmacki, Jakub, Beata Brozek-Pluska, Radzislaw Kordek, and Halina Abramczyk. "The lipid-reactive oxygen species phenotype of breast cancer. Raman spectroscopy and mapping, PCA and PLSDA for invasive ductal carcinoma and invasive lobular carcinoma. Molecular tumorigenic mechanisms beyond Warburg effect." Analyst 140, no. 7 (2015): 2121–33. http://dx.doi.org/10.1039/c4an01876a.
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Ghebremedhin, Meron, Rae Heitkamp, Shubha Yesupriya, Bradford Clay, and Nicole J. Crane. "Accurate and Rapid Differentiation of Acinetobacter baumannii Strains by Raman Spectroscopy: a Comparative Study." Journal of Clinical Microbiology 55, no. 8 (2017): 2480–90. http://dx.doi.org/10.1128/jcm.01744-16.
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ABSTRACT In recent years, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) has become the standard for routine bacterial species identification due to its rapidity and low costs for consumables compared to those of traditional DNA-based methods. However, it has been observed that strains of some bacterial species, such as Acinetobacter baumannii strains, cannot be reliably identified using mass spectrometry (MS). Raman spectroscopy is a rapid technique, as fast as MALDI-TOF, and has been shown to accurately identify bacterial strains and species. In this study, we compared hierarchical clustering results for MS, genomic, and antimicrobial susceptibility test data to hierarchical clustering results from Raman spectroscopic data for 31 A. baumannii clinical isolates labeled according to their pulsed-field gel electrophoresis data for strain differentiation. In addition to performing hierarchical cluster analysis (HCA), multiple chemometric methods of analysis, including principal-component analysis (PCA) and partial least-squares discriminant analysis (PLSDA), were performed on the MS and Raman spectral data, along with a variety of spectral preprocessing techniques for best discriminative results. Finally, simple HCA algorithms were performed on all of the data sets to explore the relationships between, and natural groupings of, the strains and to compare results for the four data sets. To obtain numerical comparison values of the clustering results, the external cluster evaluation criteria of the Rand index of the HCA dendrograms were calculated. With a Rand index value of 0.88, Raman spectroscopy outperformed the other techniques, including MS (with a Rand index value of 0.58).
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Owusu-Sarfo, K., V. Asiago, N. Gowda, et al. "1H NMR–based metabolic profiling of serum for the detection of pancreatic cancer." Journal of Clinical Oncology 29, no. 4_suppl (2011): 193. http://dx.doi.org/10.1200/jco.2011.29.4_suppl.193.
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193 Background: Pancreatic cancer (PC) is one of the leading causes of cancer deaths with a 5-yr mortality rate of 95%, and the lack of a suitable early detection method contributes to its poor prognosis. Metabolomics, the analysis of the metabolic profiles in biological samples such as serum and urine is emerging as an important tool to complement other “omic” techniques. In an effort to identify potential biomarkers for PC, we analyzed serum from PC patients (pts) focusing on altered metabolic profiles using 1H nuclear magnetic resonance (NMR). Methods: The metabolite profiles from serum samples consisting of 55 PC pts and 32 healthy controls were analyzed using NMR combined with advanced supervised and unsupervised multivariate statistical methods such as partial least squares discriminant analysis (PLSDA) and principal component analysis (PCA). A number of metabolite markers selected based on p values and logistic regression rank the importance of each potential marker. Statistically significant metabolites between cancer and controls were used to build a prediction model. Results: Based on multivariate logistic regression analysis of 20 targeted metabolites, 10 metabolite markers were selected from the variable selection process and used to build a regression model with high accuracy (AUROC >0.99), a sensitivity of 95% and specificity of 95% using a training set of samples. When the model was tested on an independent set of patient samples, it yielded a sensitivity of 95% and a specificity of 100% (AUROC >0.98). Box and whisker plots for individual markers verified the high performance of all 10 markers. Conclusions: The high sensitivity of the metabolic profile that distinguishes PC pts from controls indicates the potential utility of 1H NMR metabolic profiling for the early detection of PC. The investigation has identified perturbations in several pathways such as glycolysis and amino acid metabolism, highlighting their contribution to disease onset. This study demonstrates the potential of metabolite profiling as an important tool toward detecting PC development. Future studies will involve metabolite validation on high risk pts, and additional mass spectrometry based metabolic discovery efforts. No significant financial relationships to disclose.
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Le, Van-Tuyen, Samuel Bertrand, Thibaut Robiou du Pont, et al. "Untargeted Metabolomics Approach for the Discovery of Environment-Related Pyran-2-Ones Chemodiversity in a Marine-Sourced Penicillium restrictum." Marine Drugs 19, no. 7 (2021): 378. http://dx.doi.org/10.3390/md19070378.
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Very little is known about chemical interactions between fungi and their mollusc host within marine environments. Here, we investigated the metabolome of a Penicillium restrictum MMS417 strain isolated from the blue mussel Mytilus edulis collected on the Loire estuary, France. Following the OSMAC approach with the use of 14 culture media, the effect of salinity and of a mussel-derived medium on the metabolic expression were analysed using HPLC-UV/DAD-HRMS/MS. An untargeted metabolomics study was performed using principal component analysis (PCA), orthogonal projection to latent structure discriminant analysis (O-PLSDA) and molecular networking (MN). It highlighted some compounds belonging to sterols, macrolides and pyran-2-ones, which were specifically induced in marine conditions. In particular, a high chemical diversity of pyran-2-ones was found to be related to the presence of mussel extract in the culture medium. Mass spectrometry (MS)- and UV-guided purification resulted in the isolation of five new natural fungal pyran-2-one derivatives—5,6-dihydro-6S-hydroxymethyl-4-methoxy-2H-pyran-2-one (1), (6S, 1’R, 2’S)-LL-P880β (3), 5,6-dihydro-4-methoxy-6S-(1’S, 2’S-dihydroxy pent-3’(E)-enyl)-2H-pyran-2-one (4), 4-methoxy-6-(1’R, 2’S-dihydroxy pent-3’(E)-enyl)-2H-pyran-2-one (6) and 4-methoxy-2H-pyran-2-one (7)—together with the known (6S, 1’S, 2’S)-LL-P880β (2), (1’R, 2’S)-LL-P880γ (5), 5,6-dihydro-4-methoxy-2H-pyran-2-one (8), (6S, 1’S, 2’R)-LL-P880β (9), (6S, 1’S)-pestalotin (10), 1’R-dehydropestalotin (11) and 6-pentyl-4-methoxy-2H-pyran-2-one (12) from the mussel-derived culture medium extract. The structures of 1-12 were determined by 1D- and 2D-MMR experiments as well as high-resolution tandem MS, ECD and DP4 calculations. Some of these compounds were evaluated for their cytotoxic, antibacterial, antileishmanial and in-silico PTP1B inhibitory activities. These results illustrate the utility in using host-derived media for the discovery of new natural products.
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Wang, Fang, Bin Jia, Xiangwen Song, et al. "Rapid Identification of Peucedanum praeruptorum Dunn and Its Adulterants by Hand-Held Near-Infrared Spectroscopy." Journal of AOAC INTERNATIONAL, December 25, 2021. http://dx.doi.org/10.1093/jaoacint/qsab160.
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Abstract Background Peucedanum praeruptorum Dunn (PPD) is a traditional Chinese medical herb of high medical and economic value. However, PPD is often adulterated by inexpensive plants. Objective In order to establish an integrated and straightforward methodology to identify adulterated PPD products, hand-held near-infrared spectroscopy (NIRS) combined with chemical pattern recognition techniques was employed. Method The standard normal variate (SNV) was used to preprocess the original near-infrared spectra. Principal component analysis (PCA), linear discriminant analysis (LDA), and partial least-squares regression analysis (PLSDA) were used to construct the recognition models. Results PCA analysis could not correctly distinguish PPD from non-PPD. However, based on absorbance in the spectral region of 1405–2442 nm and SNV pretreatment, the accuracy of the LDA model was above 90% at identifying genuine PPD. Compared with the LDA method, the PLSDA model is more stable and reliable, and its model prediction accuracy was 93.4%. Conclusion The combination of NIRS and chemometric methods based on a hand-held near-infrared spectrometer is an efficient, nondestructive, and reliable method for validating traditional Chinese medicine PPD. Highlights The advanced method based on a hand-held near-infrared spectrometer can be used for rapid identification and quality evaluation of PPD in the field, medicinal material markets, and points of sale.
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Wang, Fang, Bin Jia, Jun Dai, et al. "Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy." Journal of Near Infrared Spectroscopy, April 25, 2022, 096703352210783. http://dx.doi.org/10.1177/09670335221078354.
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Because of the similar appearance and properties of different quality grades of the product, super Dendrobium huoshanense could be easily adulterated with first-grade D. huoshanense and second-grade D. huoshanense products, thereby affecting its clinical application and causing market distortion. In this study, a combination of hand-held near infrared spectroscopy and chemometrics was used to classify different grades of D. huoshanense. The standard normal variate was employed to preprocess the original near infrared spectra, following which linear analysis models (principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and a non-linear support vector machine (SVM) model, were utilized to establish the identification models. The results showed that PCA analysis could not identify the three grades of D. huoshanense, and the LDA analysis could distinguish the second-grade from the other two grades. The PLSDA model resulted in prediction accuracies for the calibration cross-validation, and test sets of 91.83%, 83.58%, and 84.29%, respectively. Unfortunately, the super and first-grade D. huoshanense were not identified by the linear analysis model. Further analysis was performed with a non-linear model, where SVM was used to analyze all grades of D. huoshanense. The recognition rate of thel training set and validation set were 88% and 84%, respectively. All in all, the use of a hand-held near infrared spectrometer combined with chemometrics could identify the quality grade of D. huoshanense samples on-site in real-time, and provide a simple, fast, and reliable method for the quality control of the traditional Chinese medicine herb of D. huoshanense.
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Ruiz-Perez, Daniel, Haibin Guan, Purnima Madhivanan, Kalai Mathee, and Giri Narasimhan. "So you think you can PLS-DA?" BMC Bioinformatics 21, S1 (2020). http://dx.doi.org/10.1186/s12859-019-3310-7.
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Abstract Background Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA). Results We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at http://biorg.cs.fiu.edu/plsda Conclusions Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models.
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Siregar, Yusraini Dian Inayati, Rudy Heryanto, Nur Lela, and Tri Heny Lestari. "Karakterisasi Karbon Aktif Asal Tumbuhan dan Tulang Hewan Menggunakan FTIR dan Analisis Kemometrika." Jurnal Kimia VALENSI, November 30, 2015, 103–16. http://dx.doi.org/10.15408/jkv.v0i0.3146.
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Activated carbon is widely used as an adsorbent in gas purification, refining pulp, and also for the purification of food products, among others, oil purification, refining cane sugar, beet sugar, corn sugar, eliminate the taste and odor of drinking water. Carbon active can be derived from plant and animal bone. This study aims to analyze the differences in spectral profile of activated carbon from plants and animal bones by using FTIR. The data combined with the results of FTIR analysis chemometrics to classify and categorize the data, so it is clear where the activated carbon from plants and animal bones. FTIR analysis methods combined with chemometrics analysis through modeling PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) is able to distinguish between activated carbon derived from plants (coconut shell) and animal bones (beef and pork). PCA with total diversity of 89% were able to classify the samples of activated carbon plant and animal bones. PLSDA models successfully predicted the test sample is based on a sample group of activated carbon raw material. Manufacture of activated carbon predictive models with PLS calibration generates R2, R2 predictions, RMSEC, and RMSEP respectively by 0.9787389, 0.9662152, 0.0687364 and 0.0928362. The results showed that FTIR spectra and can be used to distinguish chemometrics activated carbon derived from plant and animal bonesDOI :http://dx.doi.org/10.15408/jkv.v0i0.3146.