Chemometric Techniques - Assignment Example

Question 1) Enter the data into a spreadsheet and rearrange the equation as a linear equation between k and T. Produce a layout of your data in the spreadsheet by transforming the appropriate variable(s) to enable you answer the questions below. [8 marks] Question2) Plot the appropriate regression graph. [4 marks] Question 3) Determine the equation of the linear equation for the transformed variables. [4 marks] K=20.38t +254.2 Question 4) Use the deduced linear equation to calculate the value of A.[3 marks] Basing on the equation above, we are required to replace the known values to get the unknown R is the gas constant 8.314 J/mol K. At t 25, K=20.38x25+254.2 =763.7 At t 77, k=20.38x77+254.2 =1823.46 Therefore at this stage we need to get E first using the two k and t values 763.7=A.exp(E/8.314(1/25) 1823.46= A.exp(E/8.314(1/77) A=1823.46/ exp(E/8.314(1/77) A=763.7/ exp(E/8.314(1/25) 763.7/ exp(E/8.314(1/25)= 1823.46/ exp(E/8.314(1/77) =324.636 Question 4) Use the deduced linear equation to calculate the value of E.[3 marks] 763.7=A.exp(E/8.314(1/25) 1823.46= A.exp(E/8.314(1/77) E=535.634 Question 5) Determine (k) if T is 102oC. [3 marks] At t 102, K=20.38x102+254.2 =2332.96 Excersice 2) Question 1 Identify two aims and objectives of the publication you have selected and comment on them. [8 marks] Objective 1: Rapid AS and AH differentiation coupled with principal analysis without relying on the morphology of the plants. In China, the rhizomes and the roots of ‘‘Asarum heterotropoides Fr. Schmidtvar. mandshuricum (Maxim.) Kitag.’’ (AH) as well as of ‘‘Asarum sieboldii Miq.’’ (AS) are known to be the 2 major species which are mostly used as Asari RadixetRhizoma (Cai and Li, 2001). They are known to have the difference in pharmacological activities (Xu et al,2012).They are also known to be similar in terms of morphology, hence normally classified by experts or herb farmers. This process needs a long term experience and it is based on the judgment that is subjective. Furthermore, they tend to be fragile as well as very hard to be differentiated in cases when the aerial parts are missing. In regards to this, it is important to develop a method that could be able to differentiate this plants and the method which is not based on the plant appearance (Fan et al, 2006). Objective 2: Differentiation of AS and AH by their volatile constituents through the use of headspace GC–MS, chemo- metrics and electronic nose methods. There are previous methods that are used in the differentiation of AS and AH.They include gas chromatography coupled to mass spectrometry and relies on essential oils and X-ray fluorescence analysis (Su, 1999). In relation to these procedures, in the course of the isolation of the essential oils through either solvent extraction or hydro-distillation, it is seen to be tedious and there is a high consumption of a large volume of both the solvent and the sample (such as 50–100 g). Hence, at this point, it is important to have solvent-free, a simple and rapid sample pretreatment method like headspace sampling. Such technique has also the advantage whereby fewer samples is required (normally less than 1g) and the capacity of being automated through a commercialized sampling system Question2) Identify one analytical technique used in this publication and provide the basic theory of this technique including the type of data it generates. [8 marks] 'Headspace' G-C Head space is a gas space in a chromatography vial located above sample. The component of the volatile sample usually diffuse into the gas phase and later if forms headspace gas. Therefore, headspace analytical technique involves the analyzing the components that are present in that gas.  Headspace G-C is the most appropriate in the analysis of volatiles that are very light in the given sample which can be partitioned efficiently into the headspace gas volume from the solid or liquid matrix sample. Higher boiling semi-volatiles and volatiles are known not to be detected in such a technique because in the gas headspace volume, they do have a low partition. It is also known that the headspace gas analysis is automated for sample screening and quality control. Such is achieved by modern instrumentation, whereby, there is preparation of highly reproducible samples in an accurate and efficient way (Adams, 2000). Sample matrices that are complex and hard to analyze directly, might need sample preparation or extraction and they are seen to be ideal for headspace because they might be put directly in a vial either no or little preparation. In this case money and time is saved.  Headspace GC is usually used in the analysis of semi-volatile and volatile organics in gas, solid and liquid samples. In regards to the data generated, the volatile constituents can be identified through the comparison of mass spectra with the mass spectrum library (NIST 08 library).The spectra can be confirmed through comparing of the calculated R I with the use of n-alkanes mixture .The relative contents of volatile constituents can be calculated by area normalization method (vanGenderen et al, 1999) Question 3) Name two Chemometric techniques used in this report and sate what they were used for in this paper [6 marks] Chemometric techniques used Cluster analysis (CA) Principal component analysis (PCA) The reason why they were used is because, first, in regards to the chemometrics analysis, the averages of the data generated from the GC–MS analysis and electronic nose were analyzed by these techniques of analysis that include principle component technique and cluster analysis (CA). PCA is method that is unsupervised and has the capacity of reducing the multidimensional data into coordinates that are orthogonal and this is on the basis of maximum variance through linear projection. Through the application of this PCA, data can be transformed either into 3D coordinates or two- dimensional (2D). In regards to the 3D and 2D plots of PCA, samples that have with patterns are seen to be situated together and the differences that exist between the groups can be visualized easily (Feng et al, 2011).In this current research, PCA was employed in order to come up with the first 2 major components generated from the data of electronic nose as well as visualize the information that existed in the given data. CA is a data analysis tool that is exploratory and it is applied in the quantifying of the similarities that exists among the given samples. Its major task involves sample sorting into clusters whereby the level of relatedness is strong between the entities of same cluster as well as weak between entities of different clusters (Yuan et al, 2009). The visualization of the relationship that exists among the cluster can be through dendro- gram. In this study, there was use of Hierarchical cluster analysis to evaluate the probable grouping of analyzed samples through headspace GC–MS. Similar method in this case was applied as an amalgamation rule (Yuan et al, 2009), and the application of the squared Euclidean distance was done in order to measure the proximity that existed between the given samples. Question 4) Principle component analysis It is well known that, the multi-dimensional data are collected in a real data matrix table, whereby the rows are related to the cases and the columns are related to the variables. Principal component analysis is traditionally carried out on either on the symmetric Correlation or symmetric Covariance matrix. Such given matrices are known to be calculated on the basis of data matrix. The covariance matrix is known to consist of scaled cross products and sums of squares. A correlation matrix is seen to be similar to covariance matrix only that the 1st variables, such as the columns, are seen to be standardized. It is required to standardize the data first in cases where there is a big difference in the variables’ variances or in cases where there is a difference in the measurement units of the variables. The data can be standardized in the real table through the selection of Standardize columns (Perumalsamyet al, 2009). In oder to carry out the analysis, the data matrix Tabel Of Real in the objects’ list is selected then the PCA is chosen. Such leads to a new PCA object in the objects’ list. At this point a scree plot of eigen values can now be made. This is done in order to come up with an indication related to the significance of each eigen value. The real contribution related to each eigen value as well as "explained variance" might as well be queried. In this study research, PCA was employed in order to come up with the first 2 major components generated from the data of electronic nose as well as visualize the information that existed in the given data (Dragonieri et al,2007). Question 5) Identify the main data structure used for the chemometric analysis you selected in (d). (Hint: identify number of rows and columns including headings etc.). [8 marks] The volatile components with their corresponding contents in Asarum sieboldii (AS) and Asarum heterotropoides var. mandshuricum (AH) were structured as follows. on column section, there was number, retention time(min),constituents, identification method and relative contents. The row section had the list of the components. From the table, there were nine constituents that were selected for cluster analysis and they were indicated in bold. It was important to standardize the data first in cases where there was a big difference in the variables’ variances or in cases where there is a difference in the measurement units of the variables. The data can be standardized in the real table through the selection of Standardize columns. The visualization of the relationship that exists among the cluster can be through dendro- gram. In this study, there was use of Hierarchical cluster analysis to evaluate the probable grouping of analyzed samples through headspace GC–MS. Similar method in this case was applied as an amalgamation rule. Bibliography Cai, S.Q, Li, J. (2001). Species systematization and quality evaluation of commonly used Chinese tradtional drugs, Beijing medical university press, Beijing, PR China,. Xu, Y.G.,C.Cao,M.Y.Shang,Y.M.Jiang,X.Wang,C.L.Li,J.Ye,S.Q.Cai. 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Fan G.,W.Botang,C.Footim.(2006).Chemical characterization of essential oil in Rhi-zoma asarum from different sources using GC–MS with resolution improved by data processing techniques,Flavour and Fragrance Journal 21; 549–555 Read More