scholarly journals Determination of Stevioside and Rebaudioside A in Stevia rebaudiana Bertoni Leaves Using near Infrared Spectroscopy and Multivariate Data Analysis

2018 ◽  
Vol 18 (4) ◽  
pp. 664 ◽  
Author(s):  
Yohanes Martono ◽  
Suryasatriya Trihandaru ◽  
Ferdy Semuel Rondonuwu
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Cross Validation ◽  
Multivariate Data ◽  
Stevia Rebaudiana ◽  
Partial Least Square ◽  
Steviol Glycosides ◽  
Rebaudioside A ◽  
Wavenumber Region

Rebaudioside A and stevioside are abundant steviol glycoside contained in Stevia rebaudiana leaves. These components are widely used as a natural sweetener. The objective of this study was to develop rapid determination method of stevioside, and rebaudioside A in S. rebaudiana leaves using near infrared trans-reflectance spectroscopy (NIRS) combined with multivariate analysis. The reference method used was HPLC. A prediction model was developed using partial least square (PLS) regression. Calibration parameters were calculated based on a calibration set of various stevioside, rebaudioside A from 23 samples. Performance of PLS model was assessed in term of optimum determination coefficient (R2), and minimum root mean square error of cross-validation (RMSEV). Validation of PLS model was performed using cross-validation and leave one out calibration of PLS component. Rebaudioside A has well PLS model in wavenumber region of 4100–5100 cm-1, and stevioside determination using difference wavenumber region of 4760-5016 cm-1. PLS model for total (sum of stevioside and rebaudioside A content) was exploited in wavenumber region of 4568-4928 cm-1. NIRS in combination with multivariate data analysis of PLSR can be applied as a rapid method for determining rebaudioside A and the total amount of steviol glycosides in S. rebaudiana leaves.

Classification of Stevia rebaudiana Using Near Infrared Spectroscopy and Multivariate Data Analysis

2017 ◽  
Vol 901 ◽  
pp. 103-109 ◽  
Author(s):  
Yohanes Martono ◽  
Ferdy S. Rondonuwu ◽  
Suryasatriya Trihandaru
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Geographical Area ◽  
Stevia Rebaudiana ◽  
Classification Method ◽  
Rebaudioside A

Stevia rebaudiana leaf contains stevioside and rebaudioside A as main diterpene glycosides. These compounds are used as natural sweetener and potentially as drug candidate of diabetes type 2. Rapid and nondestructive method for S. rebaudiana leaves (n = 23) classification based on geographical area and main diterpene glycosides content was carried out using near infrared spectroscopy combined with multivariate data analysis. Linear discriminant analysis (LDA) was applied to discriminate S. rebaudiana leaves based on geographical area. Principal component analysis (PCA) was established to classify S. rebaudiana leaves based on main diterpene glycosides content. HPLC analysis was used as reference data to divide PCA result into groups. LDA model correctly classified 95% of the S. rebaudiana leaves based on geographical area. PCA model correctly classified 95% and 90% of S. rebaudiana leaves based on rebaudioside A and stevioside content, respectively. The classification method using near infrared spectroscopy combined with multivariate data analysis demonstrate potential use of the classification method established as quality control technique of S. rebaudiana leaves.

scholarly journals Monitoring the Processing of Dry Fermented Sausages with a Portable NIRS Device

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1294
Author(s):  
Alberto González-Mohino ◽  
Trinidad Pérez-Palacios ◽  
Teresa Antequera ◽  
Jorge Ruiz-Carrascal ◽  
Lary Souza Olegario ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Least Square ◽  
K Nearest Neighbor ◽  
Fermented Sausage ◽  
Ripening Process ◽  
Dry Fermented Sausage

This work studies the ability of a MicroNIR (VIAVI, Santa Rosa, CA) device to monitor the dry fermented sausage process with the use of multivariate data analysis. Thirty sausages were made and subjected to dry fermentation, which was divided into four main stages. Physicochemical (weight lost, pH, moisture content, water activity, color, hardness, and thiobarbiruric reactive substances analysis) and sensory (quantitative descriptive analysis) characterizations of samples on different steps of the ripening process were performed. Near-infrared (NIR) spectra (950–1650 nm) were taken throughout the process at three points of the samples. Physicochemical data were explored by distance to K-Nearest Neighbor (K-NN) cluster analysis, while NIR spectra were studied by partial least square–discriminant analysis; before these models, Principal Component Analysis (PCA) was performed in both databases. The results of multivariate data analysis showed the ability to monitor and classify the different stages of ripening process (mainly the fermentation and drying steps). This study showed that a portable NIR device (MicroNIR) is a nondestructive, simple, noninvasive, fast, and cost-effective tool with the ability to monitor the dry fermented sausage processing and to classify samples as a function of the stage, constituting a feasible decision method for sausages to progress to the following processing stage.

scholarly journals Characterization of α-Glucosidase Inhibitors from Psychotria malayana Jack Leaves Extract Using LC-MS-Based Multivariate Data Analysis and In-Silico Molecular Docking

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5885
Author(s):  
Tanzina Sharmin Nipun ◽  
Alfi Khatib ◽  
Zalikha Ibrahim ◽  
Qamar Uddin Ahmed ◽  
Irna Elina Redzwan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Data Analysis ◽  
Binding Affinity ◽  
Bioactive Compounds ◽  
In Silico ◽  
Multivariate Data Analysis ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Protein Data Bank Code ◽  
Glucosidase Inhibitors

Psychotria malayana Jack has traditionally been used to treat diabetes. Despite its potential, the scientific proof in relation to this plant is still lacking. Thus, the present study aimed to investigate the α-glucosidase inhibitors in P.malayana leaf extracts using a metabolomics approach and to elucidate the ligand–protein interactions through in silico techniques. The plant leaves were extracted with methanol and water at five various ratios (100, 75, 50, 25 and 0% v/v; water–methanol). Each extract was tested for α-glucosidase inhibition, followed by analysis using liquid chromatography tandem to mass spectrometry. The data were further subjected to multivariate data analysis by means of an orthogonal partial least square in order to correlate the chemical profile and the bioactivity. The loading plots revealed that the m/z signals correspond to the activity of α-glucosidase inhibitors, which led to the identification of three putative bioactive compounds, namely 5′-hydroxymethyl-1′-(1, 2, 3, 9-tetrahydro-pyrrolo (2, 1-b) quinazolin-1-yl)-heptan-1′-one (1), α-terpinyl-β-glucoside (2), and machaeridiol-A (3). Molecular docking of the identified inhibitors was performed using Auto Dock Vina software against the crystal structure of Saccharomyces cerevisiae isomaltase (Protein Data Bank code: 3A4A). Four hydrogen bonds were detected in the docked complex, involving several residues, namely ASP352, ARG213, ARG442, GLU277, GLN279, HIE280, and GLU411. Compound 1, 2, and 3 showed binding affinity values of −8.3, −7.6, and −10.0 kcal/mol, respectively, which indicate the good binding ability of the compounds towards the enzyme when compared to that of quercetin, a known α-glucosidase inhibitor. The three identified compounds that showed potential binding affinity towards the enzymatic protein in molecular docking interactions could be the bioactive compounds associated with the traditional use of this plant.

scholarly journals Discrimination of Cultivated Regions of Soybeans (Glycine max) Based on Multivariate Data Analysis of Volatile Metabolite Profiles

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 763
Author(s):  
So-Yeon Kim ◽  
So Young Kim ◽  
Sang Mi Lee ◽  
Do Yup Lee ◽  
Byeung Kon Shin ◽  
...  
Keyword(s):  
Glycine Max ◽  
Data Analysis ◽  
Volatile Compounds ◽  
Multivariate Data Analysis ◽  
Butyl Acetate ◽  
Solid Phase ◽  
Multivariate Data ◽  
Partial Least Square ◽  
Ethyl Hexanoate ◽  
Score Plot

Soybean (Glycine max) is a major crop cultivated in various regions and consumed globally. The formation of volatile compounds in soybeans is influenced by the cultivar as well as environmental factors, such as the climate and soil in the cultivation areas. This study used gas chromatography-mass spectrometry (GC-MS) combined by headspace solid-phase microextraction (HS-SPME) to analyze the volatile compounds of soybeans cultivated in Korea, China, and North America. The multivariate data analysis of partial least square-discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA) were then applied to GC-MS data sets. The soybeans could be clearly discriminated according to their geographical origins on the PLS-DA score plot. In particular, 25 volatile compounds, including terpenes (limonene, myrcene), esters (ethyl hexanoate, butyl butanoate, butyl prop-2-enoate, butyl acetate, butyl propanoate), aldehydes (nonanal, heptanal, (E)-hex-2-enal, (E)-hept-2-enal, acetaldehyde) were main contributors to the discrimination of soybeans cultivated in China from those cultivated in other regions in the PLS-DA score plot. On the other hand, 15 volatile compounds, such as 2-ethylhexan-1-ol, 2,5-dimethylhexan-2-ol, octanal, and heptanal, were related to Korean soybeans located on the negative PLS 2 axis, whereas 12 volatile compounds, such as oct-1-en-3-ol, heptan-4-ol, butyl butanoate, and butyl acetate, were responsible for North American soybeans. However, the multivariate statistical analysis (PLS-DA) was not able to clearly distinguish soybeans cultivated in Korea, except for those from the Gyeonggi and Kyeongsangbuk provinces.

scholarly journals Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

Metals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 1017 ◽  
Author(s):  
Walter Díaz ◽  
Carlos Toro ◽  
Eduardo Balladares ◽  
Victor Parra ◽  
Pablo Coelho ◽  
...  
Keyword(s):  
Data Analysis ◽  
Spectral Data ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Iron Sulfide ◽  
Multivariate Data ◽  
Copper Production ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Drop Tube

The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes.

Fungal decay of spruce and beech wood assessed by near-infrared spectroscopy in combination with uni- and multivariate data analysis

Holzforschung ◽  
2007 ◽  
Vol 61 (6) ◽  
pp. 680-687 ◽  
Author(s):  
Karin Fackler ◽  
Manfred Schwanninger ◽  
Cornelia Gradinger ◽  
Ewald Srebotnik ◽  
Barbara Hinterstoisser ◽  
...  
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Lignin Content ◽  
Beech Wood ◽  
Multivariate Data ◽  
Principal Component ◽  
Brown Rot ◽  
White Rot ◽  
Least Squares Regression

Abstract Wood is colonised and degraded by a variety of micro-organisms, the most efficient ones are wood-rotting basidiomycetes. Microbial decay processes cause damage to wooden constructions, but also have great potential as biotechnological tools to change the properties of wood surfaces and of sound wood. Standard methods to evaluate changes in infected wood, e.g., EN350-1 1994, are time-consuming. Rapid FT-NIR spectroscopic methods are also suitable for this purpose. In this paper, degradation experiments on surfaces of spruce (Picea abies L. Karst) and beech (Fagus silvatica L.) were carried out with white rot basidiomycetes or the ascomycete Hypoxylon fragiforme. Experiments with brown rot or soft rot caused by Chaetomium globosum were also performed. FT-NIR spectra collected from the degraded wood were subjected to principal component analysis. The lignin content and mass loss of the specimens were estimated based on univariate or multivariate data analysis (partial least squares regression).

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