scholarly journals Discrimination of Four Cinnamomum Species with Physico-Functional Properties and Chemometric Techniques: Application of PCA and MDA Models

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2871
Author(s):  
Priya Rana ◽  
Shu-Yi Liaw ◽  
Meng-Shiou Lee ◽  
Shyang-Chwen Sheu
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Principal Component ◽  
Training Set ◽  
Food Fraud ◽  
Score Plot ◽  
Food Adulteration ◽  
Clear Separation ◽  
Prediction Rate ◽  
Chemometric Techniques

Discrimination of highly valued and non-hepatotoxic Cinnamomum species (C. verum) from hepatotoxic (C. burmannii, C. loureiroi, and C. cassia) is essential for preventing food adulteration and safety problems. In this study, we developed a new method for the discrimination of four Cinnamomum species using physico-functional properties and chemometric techniques. The data were analyzed through principal component analysis (PCA) and multiclass discriminant analysis (MDA). The results showed that the cumulative variability of the first three principal components was 81.70%. The PCA score plot indicated a clear separation of the different Cinnamomum species. The training set was used to build the discriminant MDA model. The testing set was verified by this model. The prediction rate of 100% proved that the model was valid and reliable. Therefore, physico-functional properties coupled with chemometric techniques constitute a practical approach for discrimination of Cinnamomum species to prevent food fraud.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Use of near infrared spectroscopy and spectral databases to assess the quality of pharmaceutical products and aid characterization of unknown components

2019 ◽  
Vol 27 (5) ◽  
pp. 379-390
Author(s):  
Mazlina Mohd Said ◽  
Simon Gibbons ◽  
Anthony Moffat ◽  
Mire Zloh
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Score Plot ◽  
Principal Component Analysis Score ◽  
Infrared Spectral ◽  
Herbal Formulations ◽  
Spectral Database

This research was initiated as part of the fight against public health problems of rising counterfeit, substandard and poor quality medicines and herbal products. An effective screening strategy using a two-step combination approach of an incremental near infrared spectral database (step 1) followed by principal component analysis (step 2) was developed to overcome the limitations of current procedures for the identification of medicines by near infrared spectroscopy which rely on the direct comparison of the unknown spectra to spectra of reference samples or products. The near infrared spectral database consisted of almost 4000 spectra from different types of medicines acquired and stored in the database throughout the study. The spectra of the test samples (pharmaceutical and herbal formulations) were initially compared to the reference spectra of common medicines from the database using a correlation algorithm. Complementary similarity assessment of the spectra was conducted based on the observation of the principal component analysis score plot. The validation of the approach was achieved by the analysis of known counterfeit Viagra samples, as the spectra did not fully match with the spectra of samples from reliable sources and did not cluster together in the principal component analysis score plot. Pre-screening analysis of an herbal formulation (Pronoton) showed similarity with a product containing sildenafil citrate in the database. This finding supported by principal component analysis has indicated that the product was adulterated. The identification of a sildenafil analogue, hydroxythiohomosildenafil, was achieved by mass spectrometry and Nuclear Magnetic Resonance (NMR) analyses. This approach proved to be a suitable technique for quick, simple and cost-effective pre-screening of products for guiding the analysis of pharmaceutical and herbal formulations in the quest for the identification of potential adulterants.

Comparative study of the physicochemical, compositional and functional properties of eight flours obtained from different plant materials found in Colombia

2016 ◽  
Vol 22 (8) ◽  
pp. 699-707 ◽  
Author(s):  
Seneida Lopera-Cardona ◽  
Cecilia Gallardo ◽  
Jairo Umaña-Gallego ◽  
Lina María Gil
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Ananas Comosus ◽  
Whole Grain ◽  
Plant Materials ◽  
Pineapple Peel ◽  
Yellow Peas ◽  
Oyster Mushrooms

The physicochemical, compositional and functional properties of flour from green plantains ( Musa acuminata) of the large green plantain variety, oyster mushrooms ( Pleorotus ostreatus), pineapple peel ( Ananas comosus) of the ‘apple pineapple’ variety, yellow peas ( Pisum sativum), chickpeas ( Cicer arietinum), whole grain rice ( Oryza sativa), whole grain corn ( Zea mays) and whole grain white quinoa (Chenopodium quinoa) were evaluated by using one-way analysis of variance, Pearson correlations and principal component analysis chemical composition of the eight flours, statistically differed ( p < 0.05). Oyster mushroom and yellow pea flours had the greatest protein content (28.92 and 21.02%, respectively), whereas the pineapple peel, peas and corn stood out for their high contents of Fe and Zn. All flours exhibited emulsifying and foaming activities, while hydration and interfacial properties showed statistically significant negative correlations. There was a clear relationship between levels of protein and carbohydrates and gelation and syneresis phenomena in thermally treated flour suspensions. According to principal component analysis of functional, physicochemical and compositional properties, flours were classified into five groups of raw materials: (1) yellow peas, (2) chickpeas, rice, corn and quinoa, (3) green plantain, (4) pineapple peel and (5) oyster mushrooms. Results are promising to formulate mixes and composite flours for fortification and/or enrichment of food products by using different technological processes.

scholarly journals Evaluation, Effect of Whitening Agents on Physico-Chemical and Functional Properties of Sardine Surimi (Sardina pilchardus)

2021 ◽  
pp. 74-89
Author(s):  
Imen Zaghbib ◽  
Soumaya Arafa ◽  
Hassouna Mnasser
Keyword(s):  
Hydrogen Peroxide ◽  
Principal Component Analysis ◽  
Calcium Carbonate ◽  
Sodium Bicarbonate ◽  
Functional Properties ◽  
Principal Component ◽  
Textural Properties ◽  
The Other ◽  
Sardina Pilchardus ◽  
Whitening Agent

The effects of hydrogen peroxide (H2O2), sodium bicarbonate (NaHCO3) and calcium carbonate (CaCO3) treatments on the colour and textural properties of sardine surimi (Sardina pilchardus) were studied. Principal Component Analysis (PCA) was applied in order to investigate their effects and to determine the optimum whitening agents used. Addition of CaCO3 and H2O2 significantly improved whiteness of surimi in comparison to NaHCO3 treatments (p < 0.05). Some textural damage and a reduction in WHC values were observed for surimi treated withH2O2 and NaHCO3 (p < 0.05). PCA biplot showed that 1.5% CaCO3 tended to result in improved whiteness, WHC and textural properties since 1.5% CaCO3 sample sits closer to these functional properties vector lines than the other treatments. Whereas, 2.5% H2O2 had positively affected only the whiteness parameter. Results indicated that treating mince with the appropriate type and concentration of whitening agent can improve the functional properties of surimi, particularly from fish species with darker meat such as sardine. Aims: In order to improve whiteness and functional properties of sardine surimi (Sardina pilchardus), the effects of hydrogen peroxide (H2O2), sodium bicarbonate (NaHCO3) and calcium carbonate (CaCO3) treatments were studied. Principal Component Analysis (PCA) was applied in order to investigate their effects and to determine the optimum whitening agents used. Study Design: Experimental Research Design Place and Duration of Study: Research Unit “Biopreservation and Valorisation of Agro-Food Products” of the Higher Graduate School of Food Industry of Tunisia. The study was conducted in 3 months. Methodology: Sardine surimi samples were prepared with different treatments at different concentrations: calcium carbonate (CaCO3), hydrogen peroxide (H2O2) and sodium bicarbonate (NaHCO3). Proximate composition, total pigment, whiteness, water holding capacity and textural properties were investigated. Optimal levels of each whitening agent were determined using PCA. Results: Addition of CaCO3 and H2O2 significantly improved whiteness of surimi in comparison to NaHCO3 treatments (p < 0.05). Some textural damage and a reduction in WHC values were observed for surimi treated withH2O2 and NaHCO3 (p < 0.05). PCA biplot showed that 1.5% CaCO3 tended to result in improved whiteness, WHC and textural properties since 1.5% CaCO3 sample sits closer to these functional properties vector lines than the other treatments. Whereas, 2.5% H2O2 had positively affected only the whiteness parameter. Conclusion: Results indicated that treating mince with the appropriate type and concentration of whitening agent can improve the functional properties of surimi, particularly from fish species with darker meat such as sardine.

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

scholarly journals Physicochemical and Techno-Functional Characterization of Native Corn Reintroduced in the Andean Zone of Jujuy, Argentina

Proceedings ◽  
2020 ◽  
Vol 53 (1) ◽  
pp. 7
Author(s):  
María Alejandra Giménez ◽  
Cristina Noemí Segundo ◽  
Manuel Oscar Lobo ◽  
Norma Cristina Sammán
Keyword(s):  
Principal Component Analysis ◽  
Functional Properties ◽  
Food Industry ◽  
Functional Characterization ◽  
Principal Component ◽  
Component Analysis ◽  
High Variability ◽  
Pca Analysis

The chemical and techno-functional properties of nine maize races from the Andean zone of Jujuy, Argentina, in the process of reintroduction, were determined. Principal component analysis (PCA) was applied to establish the differences between them. The breeds studied showed high variability in their chemical and techno-functional properties, which would indicate that their applications in the food industry will also be differentiated. The PCA analysis allowed us to group them into four groups, and the Capia Marron and Culli races showed unique properties, mainly in the formation of gels.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Determination of the Reactivity Degree of Various Alkaline Solutions: A Chemometric Investigation

2019 ◽  
Vol 73 (12) ◽  
pp. 1361-1369 ◽  
Author(s):  
Marie Arnoult ◽  
Colin Dupuy ◽  
Maggy Colas ◽  
Julie Cornette ◽  
Ludovic Duponchel ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Statistical Model ◽  
Partial Least Squares Regression ◽  
Principal Component ◽  
Silicate Solution ◽  
Alkaline Solutions ◽  
Least Squares Regression ◽  
Chemometric Techniques

Knowledge of alkaline silicate solutions is crucial in order to optimize geopolymer properties. Geopolymers are new binders resulting from the activation of an aluminosilicate by an alkaline solution. It is well established that the solution reactivity strongly affects the geopolymerization and therefore the geopolymer working properties. As a consequence, an evaluation of the reactivity degree of alkaline silicate solutions prior synthesis is of the utmost interest. However, the determination of the solution reactivity is currently tedious, and for geopolymer commercialization, it would be necessary to find an easy way to determine it. Therefore, Raman spectroscopy, combined with chemometric techniques, is proposed as a solution to easily determine the alkaline silicate solution reactivity. To conduct this investigation, 65 silicate solutions were characterized by Raman spectroscopy, and reference values of their reactivity degree were determined. Finally, principal component analysis and partial least squares regression were performed to build a statistical model able to predict the alkaline silicate solution reactivity from Raman spectra.

scholarly journals Spectroscopic profiling variations in extracellular vesicle biochemistry in a model of myogenesis

2021 ◽  
Vol 12 ◽  
pp. 204173142110220
Author(s):  
Owen G. Davies ◽  
Stephen Powell ◽  
Jonathan JS Rickard ◽  
Michael Clancy ◽  
Pola Goldberg Oppenheimer
Keyword(s):  
Skeletal Muscle ◽  
Principal Component Analysis ◽  
Cellular Differentiation ◽  
Myogenic Differentiation ◽  
Principal Component ◽  
Cell Types ◽  
Extracellular Vesicle ◽  
Senescent Cell ◽  
Tracking Data ◽  
Clear Separation

Extracellular vesicles (EVs) hold value as accessible biomarkers for understanding cellular differentiation and related pathologies. Herein, EV biomarkers in models of skeletal muscle dormancy and differentiation have been comparatively profiled using Raman spectroscopy (RS). Significant variations in the biochemical fingerprint of EVs were detected, with an elevation in peaks associated with lipid and protein signatures during early myogenic differentiation (day 2). Principal component analysis revealed a clear separation between the spectra of EVs derived from myogenic and senescent cell types, with non-overlapping interquartile ranges and population median. Observations aligned with nanoparticle tracking data, highlighting a significant early reduction in EV concentration in senescent myoblast cultures as well as notable variations in EV morphology and diameter. As differentiation progressed physical and biochemical differences in the properties of EVs became less pronounced. This study demonstrates the applicability of RS as a high-resolution analytical method for profiling biochemical changes in EVs during early myogenesis.

scholarly journals ASSESSMENT OF EFFECT OF SHODHANA ON PHYTOCHEMICAL AND CHROMATOGRAPHICAL PROFILE OF DIFFERENT LEVELS OF CLASSICAL PROCESSED DANTI (BALIOSPERMUM MONTANUM WILLD.) ROOT

2017 ◽  
Vol 9 (3) ◽  
pp. 31
Author(s):  
Siba Prasad Rout ◽  
V. J. Shukla ◽  
Rabinarayan Acharya
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Principal Component ◽  
Additive Effect ◽  
Colour Reaction ◽  
Score Plot ◽  
Analysis Technique ◽  
Four Levels ◽  
Different Levels

Objective: Ayurveda recommends the use of Danti root after Shodhana (Processing/Purification) where the powder Pippali (Piperlongum Linn.) fruit, honey and Kusha (Desmostachya bippinata Stapf.) leaves are being used. But the additive effect of all these drugs on Danti root are yet to be explored scientifically. Principal component analysis (PCA), a multivariate data analysis technique targeting to assess the discrimination effect of psychic nut, for evaluating the additive effect, can be used to assess the effect of Shodhana on preliminary physicochemical, phytochemical parameters upon four levels of Danti (Baliospermum montanum Willd.) root.Methods: Roots of raw Danti, after proper botanical authentication, were subjected for classically recommended Shodhana procedure and four groups of Danti root like raw Danti (RD), Classical processed Danti root (CPDR), Kusha processed Danti root (KPDR), water processed Danti root (WPDR) were obtained at various levels of Danti Shodhana. Methanolic macerated extracts of all four Danti root groups were subjected for preliminary physicochemical, phytochemical and chromatographic screening. The obtained data were analyzed with the help of the Un-scrambler Camo Software for multivariate data analysis.Results: The methanolic and water extractive value of CPDR group is more than remaining sections holding lower ash value and high-intensity colour reaction during phytochemical screenings of steroid, flavonoid etc.Conclusion: Analysis of PCA technique suggests a similar trend in between RD and KPDR group while CPDR and WPDR on a different in score plot.

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