3.4.4. Repeatability of Py–GC-method
Two samples were analysed multiple times using the Py–GC method, a S. obliquus sample and a Chlorella vulgaris sample. The Scenedesmus sample exhibited 36.1% protein, 28.3% carbohydrates and 16.9% lipid using the traditional analysis techniques; the Chlorella sample exhibited 53.1% protein, 23.6% carbohydrates and 15.3% lipid. The samples were analysed by Py–GC in triplicate. The MS intensity results are plotted in Fig. 6 along with the trend lines derived from the experiments in previous sections. It can be seen that the majority of replicate data points are very close together. The only data points where repeat analysis deviates significantly are for the protein levels of Chlorella determined by the conventional method to be 53%. Using the equation derived for protein content in Section 3.4.1 (y = 162659x − - 646452), the protein content is calculated as 74.3, 82.9 and 75.04% respectively. This equates to a standard deviation of 4.7 and deviation of the mean 77.4 ± 2.2. This deviation is low, showing the high repeatability of the analysis. However, the estimation of the protein content using the equation is significantly higher (by 22%) than that measured by the Lowry method. The improved regression line using the data for Chlorella samples only, in Fig. 3b (y = 128889× x + 1035755), results in an average of 84% protein, still around 23% too high. However, this is the worst case scenario observed in the current work. For example the calculated lipid content using the Py–GC method agrees within 2% of the Folch method's observed value. The deviation of repeats for carbohydrates and lipid for Chlorella are calculated as 1.3 and 0.3 respectively.
3.4.4. Repeatability of Py–GC-methodTwo samples were analysed multiple times using the Py–GC method, a S. obliquus sample and a Chlorella vulgaris sample. The Scenedesmus sample exhibited 36.1% protein, 28.3% carbohydrates and 16.9% lipid using the traditional analysis techniques; the Chlorella sample exhibited 53.1% protein, 23.6% carbohydrates and 15.3% lipid. The samples were analysed by Py–GC in triplicate. The MS intensity results are plotted in Fig. 6 along with the trend lines derived from the experiments in previous sections. It can be seen that the majority of replicate data points are very close together. The only data points where repeat analysis deviates significantly are for the protein levels of Chlorella determined by the conventional method to be 53%. Using the equation derived for protein content in Section 3.4.1 (y = 162659x − - 646452), the protein content is calculated as 74.3, 82.9 and 75.04% respectively. This equates to a standard deviation of 4.7 and deviation of the mean 77.4 ± 2.2. This deviation is low, showing the high repeatability of the analysis. However, the estimation of the protein content using the equation is significantly higher (by 22%) than that measured by the Lowry method. The improved regression line using the data for Chlorella samples only, in Fig. 3b (y = 128889× x + 1035755), results in an average of 84% protein, still around 23% too high. However, this is the worst case scenario observed in the current work. For example the calculated lipid content using the Py–GC method agrees within 2% of the Folch method's observed value. The deviation of repeats for carbohydrates and lipid for Chlorella are calculated as 1.3 and 0.3 respectively.
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