Difference between revisions of "Team:Jiangnan China/Model"

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           <img src="https://static.igem.org/mediawiki/2018/8/84/T--jiangnan_china--home--icon-logo.png" width="36px" height="36px">
 
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         <a class="nav-link py-2 d-none d-md-inline-block" href="#"><i class="fa fa-home"></i> Home</a>
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             <a class="dropdown-item" href="https://2018.igem.org/Team:Jiangnan_China/Results">Results</a>
 
             <a class="dropdown-item" href="https://2018.igem.org/Team:Jiangnan_China/Results">Results</a>
 
             <a class="dropdown-item" href="https://2018.igem.org/Team:Jiangnan_China/Demonstrate">Demonstrate</a>
 
             <a class="dropdown-item" href="https://2018.igem.org/Team:Jiangnan_China/Demonstrate">Demonstrate</a>
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            <a class="dropdown-item" href="https://2018.igem.org/Team:Jiangnan_China/Model">Model</a>
 
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       <img src="https://static.igem.org/mediawiki/2018/b/b2/T--jiangnan_china--interlab--1.jpg" width="100%">
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       <img src="https://static.igem.org/mediawiki/2018/f/f6/T--jiangnan_china--model--1.jpg" width="100%">
 
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>Overview</strong></div>
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       &nbsp;&nbsp;&nbsp;&nbsp;In the last 4 years, the Measurement Committee has been developing a robust measurement procedure for green fluorescent protein (GFP) though the Interlab study, aiming to improve the measurement tools available to both the iGEM community and the synthetic biology community as a whole. And this year, we are delighted to join the study to know if we can reduce lab-to-lab variability in fluorescence measurements by normalizing to absolute cell count or colony-forming units (CFUs) instead of OD.
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       &nbsp;&nbsp;&nbsp;&nbsp;Directed evolution strategies have always been a common method for obtaining specific optimized functional strains, but it’s easy to obtain specific functional components, nor explore the functional mechanisms of components. Therefore, based on the data and experience of this project, we have established a model for screening specific functional components, which can also provide a reference for the preliminary exploration of functional mechanisms. The goal of this project is to build a Lactococcus lactis strain with acid and freeze resistance, in which the acid-resistant components are obtained due to this model. Below I will introduce our model with our acid-resistant component Msmk as an example:
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    <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>Materials</strong></div>
 
 
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       96-spot well plates: clear plates from Corning<br>
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       &nbsp;&nbsp;&nbsp;&nbsp;Before model, you should have a strain with corresponding capability or function. In our project, we got the anti-acid strain L.lactis by the following steps:
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       Bacteria strains: E.coli DH5α<br>
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       Test devices: obtained from the distribution kit
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      <img src="https://static.igem.org/mediawiki/2018/9/9d/T--jiangnan_china--model--2.png" width="70%" >
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      &nbsp;&nbsp;&nbsp;&nbsp;We firstly mutant our parent strain L.lactis NZ9000, and 3 key mutant strains were screened from 35,000 mutant strains under high throughput screening, namely L.lactis WH101, WH102, and WH103.
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       &nbsp;&nbsp;&nbsp;&nbsp;And then we did acid stress analysis on these three mutant strains.
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      <img src="https://static.igem.org/mediawiki/2018/a/a0/T--jiangnan_china--model--3.png" width="70%" >
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 1</strong> The survival rate of 4 strains, L.lactis NZ9000, L.lactis WH101, L.lactis WH102, L.lactis WH103. On the left it’s colony distribution of parent strain and acid-tolerant strains under a pH of 4.0 stress gradient of 10-3, and on the right it’s the survival rate of parent strain and acid tolerant strain (pH 4.0).
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 2</strong> The growth curve of 4 strains, L.lactis NZ9000, L.lactis WH101, L.lactis WH102, L.lactis WH103. A: pH 7.0, B: pH 5.0, C: pH 4.5.
 
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       &nbsp;&nbsp;&nbsp;&nbsp;From figure 1 and figure 2, we screened out L.lactis WH101, which has remarkably 16000-fold higher survival rate than the parent strain at pH4.0 for 5h, which is the highest among the reported survival rates at the same condition.
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      &nbsp;&nbsp;&nbsp;&nbsp;Then we start our model to find key anti-acid component.
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          <tr><th>Device</th>
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          <th>Part number</th>
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          <th>Location</th>
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            <td><a text-decoration="underline">Position control </a></td>
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            <td>BBa_R0040</td>
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            <td>Well 2D</td>
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            <td><a text-decoration="underline">Negative control</a></td>
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            <td>BBa_I20270</td>
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            <td>Well 2B</td>
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            <td><a text-decoration="underline">Test Device 1</a></td>
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            <td>BBa_J364000</td>
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            <td>Well 2F</td>
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            <td><a text-decoration="underline">Test Device 2</a></td>
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            <td>BBa_J364001</td>
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            <td>Well 2H</td>
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            <td><a text-decoration="underline">Test Device 3</a></td>
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            <td>BBa_J364002</td>
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            <td>Well 2J</td>
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            <td><a text-decoration="underline">Test Device 4</a></td>
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            <td>BBa_J364007</td>
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            <td>Well 2L</td>
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            <td><a text-decoration="underline">Test Device 5</a></td>
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            <td>BBa_J364008</td>
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            <td>Well 2N</td>
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            <td><a text-decoration="underline">Test Device 6</a></td>
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            <td>BBa_J364009</td>
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            <td>Well 2P</td>
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>Protocols</strong></div>
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>1. Deferential gene expression pattern cluster analysis</strong></div>
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       <p style="text-align: center;"><a href="https://static.igem.org/mediawiki/2018/0/09/2018_InterLab_Plate_Reader_Protocol.pdf" class="btn btn-info">Here is the link </a></p>
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      <img src="https://static.igem.org/mediawiki/2018/c/cf/T--jiangnan_china--model--5.png" width="40%" >
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 3</strong> Heat map. (1) L. lactis WH101 at pH 4.0 compared with that at pH 7.0; (2) L. lactis WH101 compared with L. lactis NZ9000 at pH 7.0; (3) L. lactis NZ9000 at pH 4.0 compared with that at pH 7.0; (4) L. lactis WH101 compared with L. lactis NZ9000 at pH 4.0.
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong>Click here for a cleaner heat map:</strong><p style="text-align: center;"><a href="https://static.igem.org/mediawiki/2018/6/68/T--jiangnan_china--model--heatmap.pdf" class="btn btn-info">heat map PDF</a></p>
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      &nbsp;&nbsp;&nbsp;&nbsp;We conduct a heat map analysis of the gene expression of mutant strain L.lactis WH101 and parent strain L.lactis NZ9000 in four cases. The heat map shows the degree of up-down-regulation of each gene, and 266 deferentially expressed genes are selected in the four cases according to the P value we set. Further analysis shows that there are 61 common deferential genes (Figure 3). We preliminarily concluded that the anti-acid mechanism of mutant L.lactis WH101 is related to these 61 common differential genes.
 
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>Results</strong></div>
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>2. PCA analysis (Principle Component Analysis)</strong></div>
 
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    <div align="center" ><strong><font size="5" color="#5B9BD5" >Particle Standard Curve</font></strong></div>
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      &nbsp;&nbsp;&nbsp;&nbsp;We then perform PCA on the data of the 61 common deferential genes.<br>
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       &nbsp;&nbsp;&nbsp;&nbsp;Statistically, PCA is one of the most widely used data compression algorithms. In PCA, data is converted from the original coordinate system to the new coordinate system, which is determined by the data itself. When converting the coordinate system, the direction with the largest variance is taken as the direction of the coordinate axis, because the maximum variance of the data gives the most important information of the data. The first new axis selects the method with the largest variance in the original data, and the second new axis selects the direction orthogonal to the first new coordinate axis and the second largest variance. This process is repeated and the number of repetitions is the feature dimension of the original data.<br>
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      &nbsp;&nbsp;&nbsp;&nbsp;The specific code that converts the data into feature spaces that retain only the first N principal components is as follows:<br>
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       <img src="https://static.igem.org/mediawiki/2018/7/77/T--jiangnan_china--interlab--chart1.png" width="70%" >
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        from numpy import *<br><br>
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        def loadDataSet(filename,delim='\t')<br>
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    <div align="center"><strong><font size="5" color="#5B9BD5" >Particle Standard Curve (log scale)</font></strong></div>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;fr=open(filename)<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;stringArr=[line,strip().split(delim) for line in fr.readlines()]<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;datArr=[map(float.line)for line in stringArr]<br>
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      <img src="https://static.igem.org/mediawiki/2018/4/4b/T--jiangnan_china--interlab--chart2.png" width="70%" >
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return mat(datArr)<br><br>
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        def pca(dataMat,topNfeat=4096):<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;meanVals=mean(dataMat,axis=0)<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;meanRemoved=dataMat-meanVals<br>
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    <div align="center"><strong><font size="5" color="#5B9BD5" >Fluorescein Standard Curve</font></strong></div>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;covMat=cov(meanRemoved,rowvar=0)<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;eigVals,eigVects=linalg.eig(mat(conMat))<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;eigValInd=argsort(eigVals)<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;eigValInd=eigValInd[:-(topNfeat+1):-1]<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;redEigVects=eigVects[:,eigValInd]<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;lowDDataMat=meanRemoved*redEigVects<br>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;reconMat=(lowDDataMat*redEigVects.T)+meanVals<br>
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    <div align="center"><strong><font size="5" color="#5B9BD5" >Fluorescein Standard Curve (log scale)</font></strong></div>
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        &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;return lowDDataMat,reconMat<br>
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      <img src="https://static.igem.org/mediawiki/2018/f/f9/T--jiangnan_china--interlab--chart4.png" width="70%" >
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        &nbsp;&nbsp;&nbsp;&nbsp;Click here for more details about PCA:&nbsp;&nbsp;&nbsp;&nbsp;<a href="https://en.wikipedia.org/wiki/Principal_component_analysis">https://en.wikipedia.org/wiki/Principal_component_analysis</a>
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 4</strong>The proportion of each data in the dimension under the two principal components. The data here are only taken from the top 10 of 61 common differential genes.
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       &nbsp;&nbsp;&nbsp;&nbsp;PCA sorted the data after analysis, and the top 10 are showed here because 61 data are too much. The first five genes with the proportion greater than 0.2 are experimentally verified. The experimental results show that LLNZ_RS02280 (Msmk) has the best anti-acid capability. <font color="#5B9BD5">This is almost identical to the direct experimental validation from 61 common differential genes.</font><br>
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       &nbsp;&nbsp;&nbsp;&nbsp;With these two principal components, we improved the accuracy of the result to 0.98. Although LLNA_RS02280 (Msmk) is not the first in the model, it is in the top five, which shows that the model still has great reference.
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>Summery</strong></div>
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     <div align="left" style="font-family: 'spr';font-size:40px;border-bottom:2px solid #584b4f;"><strong>3. GO analysis and Pathway analysis</strong></div>
 
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       &nbsp;&nbsp;&nbsp;&nbsp;We studied for a time before doing interlab, and spent one-two day preparing experimental materials, such as preparing E. coli DH5α and several culture media. After full preparation, we started the experiment. In the first experiment, our antibiotics failed, causing too much bacteria to colonize the plate.The second time we had a problem with the competent cells, so the bacteria did not grow after the plate was painted.The third time, we reprepared the competent cells and replaced the antibiotics, and the experiment went well.
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      <img src="https://static.igem.org/mediawiki/2018/4/4b/T--jiangnan_china--model--model8.png" width="80%" >
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       &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 5</strong> GO analysis.
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      <img src="https://static.igem.org/mediawiki/2018/8/8f/T--jiangnan_china--model--model9.png" width="80%" >
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      &nbsp;&nbsp;&nbsp;&nbsp;<strong >Figure 6</strong> Pathway analysis.
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      &nbsp;&nbsp;&nbsp;&nbsp;Now that the acid-resistant component is obtained, it is not enough for scientific research. We also need to explore the mechanism of acid resistance. This model attempts to analyze the mechanisms through GO analysis and pathway analysis. Here we analyze the data of 266 deferentially expressed genes. GO analysis shows that deferentially expressed genes are mainly involved in catalytic activity, binding activity in molecular function and metabolic process, cellular process in biological process. And pathway analysis shows that deferentially expressed genes are mainly involved in amino acid biosynthesis, metabolic pathways, fatty acid metabolism and carbon metabolism.  
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      &nbsp;&nbsp;&nbsp;&nbsp;This is a rough direction judgment of the mechanism and helps us to further analyze. In the analysis of the acid resistance mechanism, it may not be more substantial than the information in a large number of documents, but it is still a good reference. When analyzing a new component with fewer references, the two analyses will make a big difference.
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      &nbsp;&nbsp;&nbsp;&nbsp;In general, the establishment of our model uses a statistical approach, in which the PCA dimensionality reduction algorithm is the point. The results of the model are basically consistent with the LLNZ-RS02280 obtained from the experimental results, and the accuracy is very high. Therefore, the model can be used to identify the key genes of the target mutant and to explore its possible mechanism. However, there is a small amount of data loss in the process of PCA dimension reduction, so the accuracy is not 100%. The results also need to be verified by experiments with several genes with higher ratios.
 
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       &nbsp;&nbsp;&nbsp;&nbsp;We studied for a time before doing interlab, and spent one-two day preparing experimental materials, such as preparing E. coli DH5α and several culture media. After full preparation, we started the experiment. In the first experiment, our antibiotics failed, causing too much bacteria to colonize the plate.The second time we had a problem with the competent cells, so the bacteria did not grow after the plate was painted.The third time, we reprepared the competent cells and replaced the antibiotics, and the experiment went well.
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       &nbsp;&nbsp;&nbsp;&nbsp;1.  da Silva Sauthier, Maria Celeste; da Silva, Erik Galvao Paranhos; da Silva Santos, Bruna Rosa; Silva, Emmanuelle Ferreira Requiao; da Cruz Caldas, Jamile; Cavalcante Minho, Lucas Almir; Dos Santos, Ana Maria Pinto; Dos Santos, Walter Nei Lopes. Screening of Mangifera indica L. functional content using PCA and neural networks (ANN). Food chemistry. 10.1016/j.foodchem.2018.01.129
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       &nbsp;&nbsp;&nbsp;&nbsp;2.Silva, Emanuela Dos Santos; da Silva, Erik Galvao Paranhos; Silva, Danielen Dos Santos; Novaes, Cleber Galvao; Amorim, Fabio Alan Carqueija; Dos Santos, Marcio Jose Silva; Bezerra, Marcos Almeida. Evaluation of macro and micronutrient elements content from soft drinks using principal component analysis and Kohonen self-organizing maps. Food chemistry. 10.1016/j.foodchem.2018.06.021
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      <font size="5">1.</font> The final concentration of chloramphenicol is not clearly defined in the experimental protocol. Although we have made the experiment proceed smoothly based on experience, we believe that accurate antibiotic dosage requirements are still necessary;
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      <font size="5">2.</font> Regarding the use of the plate reader: although there are some descriptions in the experimental protocol that explain the experimental design and a simple explanation of the plate reader, we used the plate reader incorrectly at the beginning of the experiment, leading to a invalid data. So we still expect more instructions on the setup and use of the plate reader;
 
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      <font size="5">3.</font> Regarding the preservation of competent cells: we have transferred the competent cells position between buildings due to the change of the laboratory position, which took about 10 minutes. And in the transformation experiment, the competent cells stayed outside for too long. These improper storage of competent cells made the bacteria grow very unsatisfactorily after plating. Therefore, the preservation of competent cells is very important.
 
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Revision as of 13:52, 12 October 2018


    Directed evolution strategies have always been a common method for obtaining specific optimized functional strains, but it’s easy to obtain specific functional components, nor explore the functional mechanisms of components. Therefore, based on the data and experience of this project, we have established a model for screening specific functional components, which can also provide a reference for the preliminary exploration of functional mechanisms. The goal of this project is to build a Lactococcus lactis strain with acid and freeze resistance, in which the acid-resistant components are obtained due to this model. Below I will introduce our model with our acid-resistant component Msmk as an example:

    Before model, you should have a strain with corresponding capability or function. In our project, we got the anti-acid strain L.lactis by the following steps:

    We firstly mutant our parent strain L.lactis NZ9000, and 3 key mutant strains were screened from 35,000 mutant strains under high throughput screening, namely L.lactis WH101, WH102, and WH103.
    And then we did acid stress analysis on these three mutant strains.

    Figure 1 The survival rate of 4 strains, L.lactis NZ9000, L.lactis WH101, L.lactis WH102, L.lactis WH103. On the left it’s colony distribution of parent strain and acid-tolerant strains under a pH of 4.0 stress gradient of 10-3, and on the right it’s the survival rate of parent strain and acid tolerant strain (pH 4.0).

    Figure 2 The growth curve of 4 strains, L.lactis NZ9000, L.lactis WH101, L.lactis WH102, L.lactis WH103. A: pH 7.0, B: pH 5.0, C: pH 4.5.

    From figure 1 and figure 2, we screened out L.lactis WH101, which has remarkably 16000-fold higher survival rate than the parent strain at pH4.0 for 5h, which is the highest among the reported survival rates at the same condition.
    Then we start our model to find key anti-acid component.
1. Deferential gene expression pattern cluster analysis

    Figure 3 Heat map. (1) L. lactis WH101 at pH 4.0 compared with that at pH 7.0; (2) L. lactis WH101 compared with L. lactis NZ9000 at pH 7.0; (3) L. lactis NZ9000 at pH 4.0 compared with that at pH 7.0; (4) L. lactis WH101 compared with L. lactis NZ9000 at pH 4.0.

    Click here for a cleaner heat map:

heat map PDF


    We conduct a heat map analysis of the gene expression of mutant strain L.lactis WH101 and parent strain L.lactis NZ9000 in four cases. The heat map shows the degree of up-down-regulation of each gene, and 266 deferentially expressed genes are selected in the four cases according to the P value we set. Further analysis shows that there are 61 common deferential genes (Figure 3). We preliminarily concluded that the anti-acid mechanism of mutant L.lactis WH101 is related to these 61 common differential genes.
2. PCA analysis (Principle Component Analysis)


    We then perform PCA on the data of the 61 common deferential genes.
    Statistically, PCA is one of the most widely used data compression algorithms. In PCA, data is converted from the original coordinate system to the new coordinate system, which is determined by the data itself. When converting the coordinate system, the direction with the largest variance is taken as the direction of the coordinate axis, because the maximum variance of the data gives the most important information of the data. The first new axis selects the method with the largest variance in the original data, and the second new axis selects the direction orthogonal to the first new coordinate axis and the second largest variance. This process is repeated and the number of repetitions is the feature dimension of the original data.
    The specific code that converts the data into feature spaces that retain only the first N principal components is as follows:

from numpy import *

def loadDataSet(filename,delim='\t')
        fr=open(filename)
        stringArr=[line,strip().split(delim) for line in fr.readlines()]
        datArr=[map(float.line)for line in stringArr]
        return mat(datArr)

def pca(dataMat,topNfeat=4096):
        meanVals=mean(dataMat,axis=0)
        meanRemoved=dataMat-meanVals
        covMat=cov(meanRemoved,rowvar=0)
        eigVals,eigVects=linalg.eig(mat(conMat))
        eigValInd=argsort(eigVals)
        eigValInd=eigValInd[:-(topNfeat+1):-1]
        redEigVects=eigVects[:,eigValInd]
        lowDDataMat=meanRemoved*redEigVects
        reconMat=(lowDDataMat*redEigVects.T)+meanVals
        return lowDDataMat,reconMat
    Click here for more details about PCA:    https://en.wikipedia.org/wiki/Principal_component_analysis

    Figure 4The proportion of each data in the dimension under the two principal components. The data here are only taken from the top 10 of 61 common differential genes.

    PCA sorted the data after analysis, and the top 10 are showed here because 61 data are too much. The first five genes with the proportion greater than 0.2 are experimentally verified. The experimental results show that LLNZ_RS02280 (Msmk) has the best anti-acid capability. This is almost identical to the direct experimental validation from 61 common differential genes.
    With these two principal components, we improved the accuracy of the result to 0.98. Although LLNA_RS02280 (Msmk) is not the first in the model, it is in the top five, which shows that the model still has great reference.
3. GO analysis and Pathway analysis

    Figure 5 GO analysis.

    Figure 6 Pathway analysis.

    Now that the acid-resistant component is obtained, it is not enough for scientific research. We also need to explore the mechanism of acid resistance. This model attempts to analyze the mechanisms through GO analysis and pathway analysis. Here we analyze the data of 266 deferentially expressed genes. GO analysis shows that deferentially expressed genes are mainly involved in catalytic activity, binding activity in molecular function and metabolic process, cellular process in biological process. And pathway analysis shows that deferentially expressed genes are mainly involved in amino acid biosynthesis, metabolic pathways, fatty acid metabolism and carbon metabolism.

    This is a rough direction judgment of the mechanism and helps us to further analyze. In the analysis of the acid resistance mechanism, it may not be more substantial than the information in a large number of documents, but it is still a good reference. When analyzing a new component with fewer references, the two analyses will make a big difference.

    In general, the establishment of our model uses a statistical approach, in which the PCA dimensionality reduction algorithm is the point. The results of the model are basically consistent with the LLNZ-RS02280 obtained from the experimental results, and the accuracy is very high. Therefore, the model can be used to identify the key genes of the target mutant and to explore its possible mechanism. However, there is a small amount of data loss in the process of PCA dimension reduction, so the accuracy is not 100%. The results also need to be verified by experiments with several genes with higher ratios.

Reference :

    1. da Silva Sauthier, Maria Celeste; da Silva, Erik Galvao Paranhos; da Silva Santos, Bruna Rosa; Silva, Emmanuelle Ferreira Requiao; da Cruz Caldas, Jamile; Cavalcante Minho, Lucas Almir; Dos Santos, Ana Maria Pinto; Dos Santos, Walter Nei Lopes. Screening of Mangifera indica L. functional content using PCA and neural networks (ANN). Food chemistry. 10.1016/j.foodchem.2018.01.129
    2.Silva, Emanuela Dos Santos; da Silva, Erik Galvao Paranhos; Silva, Danielen Dos Santos; Novaes, Cleber Galvao; Amorim, Fabio Alan Carqueija; Dos Santos, Marcio Jose Silva; Bezerra, Marcos Almeida. Evaluation of macro and micronutrient elements content from soft drinks using principal component analysis and Kohonen self-organizing maps. Food chemistry. 10.1016/j.foodchem.2018.06.021

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