scholarly journals GC-MS Based Metabolite Profiling to Monitor Ripening-Specific Metabolites in Pineapple (Ananas comosus)

Metabolites ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 134 ◽  
Author(s):  
Muhammad Maulana Malikul Ikram ◽  
Sobir Ridwani ◽  
Sastia Prama Putri ◽  
Eiichiro Fukusaki
Keyword(s):  
Metabolite Profiling ◽  
Principal Component ◽  
Ananas Comosus ◽  
Production Volume ◽  
Ripening Process ◽  
Projection To Latent Structures ◽  
Ripening Stages ◽  
Early Ripening ◽  
Latent Structures ◽  
Time Of Harvest

Pineapple is one of the most cultivated tropical, non-climacteric fruits in the world due to its high market value and production volume. Since non-climacteric fruits do not ripen after harvest, the ripening stage at the time of harvest is an important factor that determines sensory quality and shelf life. The objective of this research was to investigate metabolite changes in the pineapple ripening process by metabolite profiling approach. Pineapple (Queen variety) samples from Indonesia were subjected to GC-MS analysis. A total of 56, 47, and 54 metabolites were annotated from the crown, flesh, and peel parts, respectively. From the principal component analysis (PCA) plot, separation of samples based on ripening stages from C0–C2 (early ripening stages) and C3–C4 (late ripening stages) was observed for flesh and peel parts, whereas no clear separation was seen for the crown part. Furthermore, orthogonal projection to latent structures (OPLS) analysis suggested metabolites that were associated with the ripening stages in flesh and peel parts of pineapple. This study indicated potentially important metabolites that are correlated to the ripening of pineapple that would provide a basis for further study on pineapple ripening process.

Comprehensive fault detection and isolation method applied to a recovery boiler

TAPPI Journal ◽  
2016 ◽  
Vol 15 (5) ◽  
pp. 323-328
Author(s):  
MOHAMED EL KOUJOK ◽  
MOULOUD AMAZOUZ ◽  
BRUNO POULIN
Keyword(s):  
Industrial Process ◽  
Principal Component ◽  
Real Data ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Pollutant Emission ◽  
Biomass Boiler ◽  
Projection To Latent Structures ◽  
Productivity Losses ◽  
Latent Structures

Early and accurate detection and isolation of industrial process faults are crucial to avoiding abnormal situations that cause productivity losses. Principal component analysis and reconstruction-based contribution (PCA-RBC) is a popular method used for such tasks. Unfortunately, this method does not guarantee correct fault isolation in cases where the faulty variables contribute little or do not contribute at all to the main principal components of the PCA model. This is the case, for example, of some pollutant emission levels that do not affect the global performance of a biomass boiler, but that should be maintained below certain thresholds. This paper proposes to adapt the PCA-RBC method to cope with such limitations. The idea is first to classify the data onto normal and abnormal conditions according to a selected parameter threshold, and then to build a PCA model using the normal dataset. The RBC approach is applied on the abnormal dataset to identify the variables that mostly contribute to the faulty situations. The proposed method is successfully demonstrated using real data from an industrial case. It is noted that an attempt to develop an accurate predictive model of the selected parameter using projection to latent structures (PLS) was unsuccessful.

scholarly journals Калибровка температуры по нормированным спектрам АП-конверсионной флуоресценции германатных стекол и стеклокерамик, активированных ионами эрбия и иттербия

2021 ◽  
Vol 129 (3) ◽  
pp. 350
Author(s):  
В.А. Асеев ◽  
Д.А. Борисевич ◽  
М.А. Ходасевич ◽  
Н.К. Кузьменко ◽  
Ю.К. Федоров
Keyword(s):  
Fluorescence Spectra ◽  
Glass Ceramics ◽  
Principal Component ◽  
Temperature Sensors ◽  
Quality Parameters ◽  
Multivariate Model ◽  
Calibration Model ◽  
Green Fluorescence ◽  
Projection To Latent Structures ◽  
Latent Structures

To select erbium and ytterbium doped germanate glasses and glass ceramics, which are most suitable as sensitive elements of fluorescent temperature sensors, a multivariate model of temperature calibration has been developed based on principal component analysis, cluster analysis and interval projection to latent structures of up-conversion green fluorescence spectra. The calibration model used 95 spectral variables for the GeO2-Na2O-Yb2O3-MgO-La2O3-Er2O3 glass-ceramic is characterized by the best quality parameters: the root-mean-square error is 0.37 K, the residual prediction deviation for the test subset is greater than 102, and the relative error does not exceed 0.20%.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

scholarly journals Formulation and Sensory Analysis of Cocktails Based on Ginger Oleoresin (Zingiber officinale Roscoe), Pineapple Juice (Ananas comosus L. MERR) and Bissap Concentrate (Hibiscus sabdariffa)

2021 ◽  
pp. 36-47
Author(s):  
Adama Coulibaly ◽  
Pierre Ezoua ◽  
Ysidor N’guessan Konan ◽  
Souleymane Doukoure ◽  
Daouda Sidibe ◽  
...  
Keyword(s):  
Descriptive Analysis ◽  
Principal Component ◽  
Zingiber Officinale ◽  
Agricultural Products ◽  
Ananas Comosus ◽  
Hibiscus Sabdariffa ◽  
Pineapple Juice ◽  
Natural Substances ◽  
Zingiber Officinale Roscoe ◽  
Central Plan

Aims: The aim of this study is to formulate cocktails based on ginger oleoresin, pineapple juice and bissap concentrate in order to contribute to the development of these agricultural products and improve the income of producers. Study Design: The biological material consists of pineapple juice, ginger oleoresin and bissap concentrate. The oleoresin and the bissap concentrate were supplied respectively by Gazignaire (France) and the Water Chemistry and Natural Substances Laboratory. Place and Duration of Study: The cocktails were formulated and then subjected to sensory analyzes, from July to October 2018, at the Biochemistry and Food Sciences laboratory at Félix Houphouët-Boigny University. Methodology: The cocktails were formulated through a composite central plan having as variables the proportions of the pineapple juice, the bissap concentrate and the ethanol composing the cocktail. Thus 15 cocktail formulations were developed, the sensory characteristics of which were estimated. Results: Hedonic analysis of the formulations indicates acceptance of 12 of them by more than 50% of tasters.In addition, 5 formulations F4; F6; F12; F13 and F15 are preferred in proportions varying between 62% and 77%.The descriptive analysis of these 5 formulations indicates that only the pineapple flavor makes it possible to distinguish them and the F13 formulation is less provided with them.However, these formulations according to their flavor, aroma and texture are classified into 3 groups according to a principal component analysis.Which could offer consumers more choice. Conclusion: Commercial production of these cocktails could improve the availability of ginger, bissap and pineapple year-round and help improve the income of producers.

scholarly journals Tissue-based metabolomics profiling reveals metabolic signatures and major metabolic pathways of gastric cancer

2021 ◽  
Author(s):  
Yaqin Wang ◽  
Wenchao Chen ◽  
Kun Li ◽  
Gang Wu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Predictive Ability ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pentose Phosphate ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Dodecanoic Acid ◽  
Orthogonal Projections ◽  
Latent Structures

Abstract Purpose This study was aimed to screen differential metabolites between gastric cancer (GC) and paracancerous (PC) tissues and find new biomarkers of GC. Methods GC (n = 28) and matched PC (n = 28) tissues were collected and LC-MS/MS analyses were performed to detect metabolites of GC and PC tissues in positive and negative models. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA) were conducted to describe distribution of origin data and general separation and estimate the robustness and the predictive ability of our mode. Differential metabolites were screened based on criterion of variables with p value < 0.05 and VIP (variable importance in the projection) > 1.0. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic power of differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to search for metabolite pathways and MetaboAnalyst was used for pathway enrichment analysis. Results Several metabolites were significantly changed in GC group compared with PC group. Thirteen metabolites with high VIP were chose and among which 1-methylnicotinamide, dodecanoic acid and sphinganine possessed high AUC values (AUC > 0.8) indicating an excellent discriminatory ability on GC. Pathways such as pentose phosphate pathway and histidine metabolism were focused based on differential metabolites demonstrating their effects on progress of GC. Conclusions In conclusion, we investigated the tissue-based metabolomics profile of GC and several differential metabolites and signaling pathways were focused. Further study is needed to verify those results.

scholarly journals Projection to latent structures with orthogonal constraints for metabolomics data

2018 ◽  
Vol 32 (5) ◽  
pp. e3047 ◽  
Author(s):  
Matteo Stocchero ◽  
Samantha Riccadonna ◽  
Pietro Franceschi
Keyword(s):  
Metabolomics Data ◽  
Projection To Latent Structures ◽  
Latent Structures

scholarly journals Analysis of Metabolite Profiling in Human Endothelial Cells after Plasma Jet Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yanjie Yang ◽  
Dehui Xu ◽  
Ning Ning ◽  
Yujing Xu
Keyword(s):  
Endothelial Cells ◽  
Plasma Treatment ◽  
Metabolite Profiling ◽  
Atmospheric Plasma ◽  
Human Endothelial Cells ◽  
Metabolic Pathway Analysis ◽  
Orthogonal Projections ◽  
Blood Capillaries ◽  
Latent Structures ◽  
Tof Ms

Cold atmospheric plasma (CAP) is a novel technology, which has been widely applied in biomedicine, especially in wound healing, dermatological treatment, hemostasis, and cancer treatment. In most cases, CAP treatment will interact with innumerable blood capillaries. Therefore, it is important and necessary to understand the effects of CAP treatment on endothelial cell metabolism. In this study, the metabolite profiling of plasma treatment on endothelial cells was measured by gas chromatography tandem time-of-flight mass spectrometry (GC-TOF-MS). We found that 695 signals (metabolites) were detected by GC-TOF-MS and then evaluated using orthogonal projections to latent structures discriminant analysis (OPLS-DA). All the differential metabolites were listed, and proline and xanthosine were the two of the most downregulated metabolites by plasma treatment. By comprehensive metabolic pathway analysis with the KEGG pathway, we showed that alanine, aspartate, glutamate, and purine metabolism pathways were the most significantly suppressed after gas plasma treatment in human endothelial cells. Our finding gives an overall picture of the metabolic pathways affected by plasma treatment in endothelial cells.

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.

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