scholarly journals Geographical origin identification of teas using UV-VIS spectroscopy

2021 ◽  
Vol 265 ◽  
pp. 05013
Author(s):  
Thi Hue Tran ◽  
Quoc Toan Tran ◽  
Thi Thao Ta ◽  
Si Hung Le
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Ann Model ◽  
Multivariate Statistical ◽  
Identification Rate ◽  
Tea Quality ◽  
Alternative Approach ◽  
Tea Infusions ◽  
Vis Spectroscopy ◽  
Artificial Neural Network Ann

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

scholarly journals Classification of Wines from Five Spanish Origin Denominations by Aromatic Compound Analysis

2010 ◽  
Vol 93 (6) ◽  
pp. 1916-1922 ◽  
Author(s):  
Cecilia Sáenz ◽  
Trinidad Cedráenzn ◽  
Susana Cabredo
Keyword(s):  
Discriminant Analysis ◽  
Solid Phase ◽  
Principal Component ◽  
Decanoic Acid ◽  
Multivariate Statistical ◽  
Ethyl Hexanoate ◽  
La Mancha ◽  
Diethyl Succinate ◽  
Ethyl Octanoate

Abstract Wine is a complex matrix in which aroma compounds play an important role in the characterization of the flavor pattern of a given wine. Twelve volatile compounds were determined in 244 samples of Spanish red wines from different denominations of origin: Rioja, Navarra, Valdepeas, La Mancha, and Cariena. The samples were analyzed by GC using headspace solid-phase microextraction. The concentration (mg/mL) intervals obtained were 3-methyl-butyl acetate (3.9 to 116), 3-methyl-1-butanol (93 to 724), ethyl hexanoate (0.8 to 39), 1-hexanol (0.3 to 6.7), ethyl octanoate (1.4 to 41), diethyl succinate (0.2 to 13), 2-phenyl ethyl acetate (0 to 5.3), hexanoic acid (0 to 8.3), geraniol (0 to 3.0), 2-phenylethanol (1.5 to 56), octanoic acid (0 to 20), and decanoic acid (0 to 3.3). Wines were classified by multivariate statistical methods: principal component analysis, and lineal discriminant analysis. A correct differentiation among wines according to their origin was obtained by lineal discriminant analysis.

An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

2015 ◽  
Vol 24 (09) ◽  
pp. 1550139
Author(s):  
Debashis Saikia ◽  
Diganta Kumar Sarma ◽  
P. K. Boruah ◽  
Utpal Sarma
Keyword(s):  
Process Condition ◽  
Correlation Study ◽  
Sensor Nodes ◽  
Quality Data ◽  
Inlet Temperature ◽  
Ann Model ◽  
Classification Rate ◽  
Tea Quality ◽  
Artificial Neural Network Ann ◽  
The Impact

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.

scholarly journals Application of Spectroscopic UV-Vis and FT-IR Screening Techniques Coupled with Multivariate Statistical Analysis for Red Wine Authentication: Varietal and Vintage Year Discrimination

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4166 ◽  
Author(s):  
Elisabeta-Irina Geană ◽  
Corina Teodora Ciucure ◽  
Constantin Apetrei ◽  
Victoria Artem
Keyword(s):  
Discriminant Analysis ◽  
Principal Component ◽  
Total Variance ◽  
Red Wines ◽  
Linear Discriminant ◽  
Vis Spectroscopy ◽  
Ft Ir ◽  
Wine Authentication ◽  
Analytical Approaches

One of the most important issues in the wine sector and prevention of adulterations of wines are discrimination of grape varieties, geographical origin of wine, and year of vintage. In this experimental research study, UV-Vis and FT-IR spectroscopic screening analytical approaches together with chemometric pattern recognition techniques were applied and compared in addressing two wine authentication problems: discrimination of (i) varietal and (ii) year of vintage of red wines produced in the same oenological region. UV-Vis and FT-IR spectra of red wines were registered for all the samples and the principal features related to chemical composition of the samples were identified. Furthermore, for the discrimination and classification of red wines a multivariate data analysis was developed. Spectral UV-Vis and FT-IR data were reduced to a small number of principal components (PCs) using principal component analysis (PCA) and then partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were performed in order to develop qualitative classification and regression models. The first three PCs used to build the models explained 89% of the total variance in the case of UV-Vis data and 98% of the total variance for FR-IR data. PLS-DA results show that acceptable linear regression fits were observed for the varietal classification of wines based on FT-IR data. According to the obtained LDA classification rates, it can be affirmed that UV-Vis spectroscopy works better than FT-IR spectroscopy for the discrimination of red wines according to the grape variety, while classification of wines according to year of vintage was better for the LDA based FT-IR data model. A clear discrimination of aged wines (over six years) was observed. The proposed methodologies can be used as accessible tools for the wine identity assurance without the need for costly and laborious chemical analysis, which makes them more accessible to many laboratories.

Raman Spectroscopic Discrimination of Cell Response to Chemical and Physical Inactivation

2007 ◽  
Vol 61 (8) ◽  
pp. 812-823 ◽  
Author(s):  
Maria Fernanda Escoriza ◽  
Jeanne M. Van Briesen ◽  
Shona Stewart ◽  
John Maier
Keyword(s):  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Species Differentiation ◽  
Multivariate Statistical ◽  
Classification Rate ◽  
Dna And Rna ◽  
Spectral Bands ◽  
Viable Cells ◽  
Standard Plate

Raman spectroscopy was applied to study Escherichia coli and Staphylococcus epidermidis cells that were inactivated by different chemicals and stress conditions including starvation and high temperature. E. coli cells exposed to starvation conditions over several days lost viability at the same rate that spectral bands assigned to DNA and RNA bases decreased in intensity. Band intensities correlate with standard plate counts with R2 = 0.99 and R2 = 0.97, respectively. Principal components analysis and discriminant analysis multivariate statistical techniques were used to evaluate the spectral data collected. Significant changes were observed in the spectra of treated cells in comparison with their respective controls (samples without treatment). As a result, there was a significant differentiation between viable and non-viable cells (treated and non-treated cells) in the first and second principal component plots for all the treatments. Discriminant analysis was used along with PCA to estimate a classification rate based on viability status of the cells. Non-viable cells were differentiated from viable cells with classification rates that ranged between 60 and 90% for specific treatments (i.e., EDTA-treated cells versus control cells). The classification rate obtained considering all the treatments (non-viable cells) and controls (viable cells) at the same time for each of the species studied was 86%. The classification rate based on species differentiation when all the spectra (viable and non-viable) were used was 87%. These results suggest that Raman spectroscopy is a powerful tool that can be used to evaluate viability and to study metabolic changes in microorganisms. It is a robust method for bacterial identification even when high spectral variations are introduced.

scholarly journals Optimization of Ash Yield from Bicomposite Biomass (Terminalia catappa and Chrysophyllum albidium) Seed Barks with Additive upon Combustion

2021 ◽  
pp. 26-38
Author(s):  
Ebenezer Olujimi, Dada ◽  
Uriel Olamilekan, Awe-Obe ◽  
Kamoru Olufemi, Oladosu ◽  
Abass Olanrewaju, Alade ◽  
Tinuade Jolaade, Afolabi
Keyword(s):  
Optimal Design ◽  
Principal Component ◽  
Volatile Matter ◽  
Proximate Analysis ◽  
Quadratic Model ◽  
Coefficient Of Determination ◽  
Dihydrogen Phosphate ◽  
Ammonium Dihydrogen Phosphate ◽  
Ann Model ◽  
Artificial Neural Network Ann

The ash yield from the combustion of a mixture of Africa star apple and tropical almond seeds shells (biocomposite biomass) with ammonium dihydrogen phosphate as an additive in a furnace was optimized using I-Optimal Design under the Combined Methodology of the Design Expert Software. The data obtained were analysed statistically using Analysis of Variance (ANOVA), Artificial Neural Network (ANN) for the prediction of ash yield and Principal Component Analysis (PCA) to determine the coefficient of determination (R²) between variables. Proximate analysis was used to evaluate Moisture Content (MC), Fixed Carbon Content (FCC), and Volatile Matter (VM) values while the Higher Heating Value (HHV) of the mixtures that gave the highest and lowest ash yields was evaluated numerically. The optimum conditions of process variables for the compositions of tropical almond, African star apple, and ammonium dihydrogen phosphate, as well as the temperature, were 30%, 60%, 10% and 704 oC, respectively leading to a minimum ash yield of 24.8%. The mathematical models for the ash using the I-optimal design indicate a good fit to the Quadratic model with a R² of 0.9999. The ANN model agreed significantly with the experimental results with an R² of 0.9939.  The VM, FCC, MC, AC and HHV of the highest ash yield were 11.00%, 2.34%, 3.20%, 33.80% and 4487.747 , respectively. The study established the suitability of optimisation tool to develop solid fuel mixtures for possible use in grate furnaces and its efficiencies.

Experimental and numerical studies for estimating coefficient of discharge of side compound weir

2019 ◽  
Vol 46 (10) ◽  
pp. 887-895
Author(s):  
Mujib Ahmad Ansari ◽  
Ajmal Hussain ◽  
Ali Shariq ◽  
Fakre Alam
Keyword(s):  
Network Architecture ◽  
Ann Model ◽  
Regression Modelling ◽  
Numerical Studies ◽  
Alternative Approach ◽  
Diversion Structure ◽  
Side Walls ◽  
Flow Through ◽  
Artificial Neural Network Ann ◽  
Coefficient Of Discharge

A sharp-crested side compound weir is a flow diversion structure provided on one or both side walls of a channel to divert water from the main channel. Compound sharp-crested weirs are widely used in irrigation, hydraulics, and environmental engineering. This article presents results of experimental and numerical studies conducted on sharp-crested side compound weirs in open channels. Owing to the complex mechanism of flow through a side compound weir it is difficult to establish a regression model to accurately predict the coefficient of discharge (Cd). In this study, an alternative approach to the conventional regression modelling in the form of artificial neural network (ANN) has been used to predict the values of Cd. A network architecture with trained values of connection weights and biases is recommended to predict Cd. The input to ANN model consists of grouped parameters pertaining to the ratio of weighted crest height to the length of the side compound weir ([Formula: see text]), the ratio of upstream depth to length of the side compound weir (Y1/L), and upstream Froude number (F1). The results of the ANN model applied herein were found to be superior to those obtained through regression modelling by previous researchers. The sensitivity analysis of the ANN model shows that [Formula: see text] is the most important parameter for the estimation of Cd; followed by Y1/L and F1.

scholarly journals Evaluation of Geographic Origin of Torrontés Wines by Chemometrics

2013 ◽  
Vol 2 (5) ◽  
pp. 48 ◽  
Author(s):  
Silvana Mariela Azcarate ◽  
Miguel Angel Cantarelli ◽  
Eduardo Jorge Marchevsky ◽  
José Manuel Camiña
Keyword(s):  
Discriminant Analysis ◽  
Geographical Origin ◽  
Uv Spectroscopy ◽  
Geographic Origin ◽  
Principal Component ◽  
San Juan ◽  
Electromagnetic Spectrum ◽  
Linear Discriminant ◽  
Rio Negro ◽  
Vis Spectroscopy

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

scholarly journals Nondestructive identification of tea (Camellia sinensis L.) varieties using FT-NIR spectroscopy and pattern recognition

2008 ◽  
Vol 26 (No. 5) ◽  
pp. 360-367 ◽  
Author(s):  
Q. Chen ◽  
J. Zhao ◽  
M. Liu ◽  
J. Cai
Keyword(s):  
Pattern Recognition ◽  
Camellia Sinensis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Ann Model ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Non Invasive ◽  
Artificial Neural Network Ann

Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea (<I>Camellia sinensis</I> L.) varieties is crucial to the tea quality control. Fourier Transform Near-Infrared (FT-NIR) spectroscopy coupled with the pattern recognition was used to identify individual tea varieties as a rapid and non-invasive analytical tool in this work. Seven varieties of Chinese tea were studied in the experiment. Linear Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis (PCA). The number of principal components factors (PCs) was optimised in the constructing model. The experimental results showed that the performance of ANN model was better than LDA models. The optimal ANN model was achieved when four PCs were used, identification rates being all 100% in the training and prediction sets. The overall results demonstrated that FT-NIR spectroscopy technology with ANN pattern recognition method can be successfully applied as a rapid method to identify tea varieties.

Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose

2011 ◽  
Vol 239-242 ◽  
pp. 2096-2100 ◽  
Author(s):  
Hong Mei Zhang ◽  
Ming Xun Chang ◽  
Yong Chang Yu ◽  
Hui Tian ◽  
Yu Qing He ◽  
...  
Keyword(s):  
Neural Network ◽  
Green Tea ◽  
Electronic Nose ◽  
Principal Component ◽  
Correct Identification ◽  
Tea Quality ◽  
Testing Data ◽  
Artificial Neural Network Ann ◽  
Correct Discrimination

In this work, the capacity of an electronic nose (E-nose, PEN2) to classify tea quality grades is investigated. Three tea groups with different quality grades were harvested at different times. Principal component analysis (PCA) and artificial neural network (ANN) were applied to identify the different tea samples. PCA provided perfect classification of tea quality grades. In the analysis of age, six groups of XinyangMaojian green tea were distinguished completely by PCA. The results of ANN analysis gave a high percentage of correct discrimination of green tea samples. The correct identification rates of the training and testing data were 98.6% and 83%, respectively, for three grades of green tea samples harvested in 2009. The correct identification rates of the training and testing data were 100% and 87.8%, respectively, for three grades of green tea samples harvested in 2010. In the analysis of age, the correct discrimination percentages for six groups of XinyangMaojian green tea were 99.4% and 88.9% for training and testing data, respectively. These results indicate that the electronic nose could be successfully used for the detection of teas of different quality grades and ages.

scholarly journals Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245525
Author(s):  
Junzhao Liu ◽  
Dong Zhang ◽  
Qiuju Tang ◽  
Hongbin Xu ◽  
Shanheng Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Temporal Analysis ◽  
Statistical Techniques ◽  
Complex Data ◽  
Monitoring Networks ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

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