To analyse the results in the easiest way possible, graphs were made from the data, and statistical tests were done. These were T-tests and ‘Analysis of Variance (ANOVA)’. They are two parametric statistical techniques which are used to test the hypothesis of the experiments. A t-test is a statistical analysis of two populations means. ANOVA is to test larger groups and to find out if there is a difference between them.
The results of the red absorption-, green absorption-, and blue absorption groups with five test series of eight measurements each were analysed with ANOVA. The result in each colour group is not significant at p < .05. Therefore, the results of each colour group with five test series and eight measurements in each series are quite homogeneous, significant errors of measurement can be excluded. With the t-test two of the results were compared with each other. At first green and red absorption were compared, followed by blue and red absorption. Both t-tests showed that the means of the two sets of data are significantly different at p <0.05. However, comparing the blue and green absorption data, the tests proved that the means of it are not significantly different at the same (p < 0.05). The test showed t to be -0.6442 whilst the two tests above were 2.145. In order to collect the data, light colour absorbance rates of red, green and blue of beetroot solutions were measured with a Colorimeter. Through the compilation of all measured absorbance rates of the three colours (see results, T1 - T15 graph), it is clearly visible that red absorbance is significantly lower over the whole temperature spectrum, compared to blue and green. The absorbance rates of blue and green increases over the whole temperature spectrum significantly. Both colours are absorbed at about the same rate at the same temperature, there is no statistically significant difference. The experiments are not flawless, though. There are many uncertainties that have to be taken into account. A measurement is usually the best estimate ± uncertainty. In these experiments uncertainties induced by measuring cylinders, cutting and weighing procedures, the freshness of the beetroot sample, and the measurement of temperature e.g. can influence the results1. There are other ways that I could have improved the way I conducted the experiments. For the first experiment, for more accurate results, I should have collected all the results in one day. This would prevent the beetroot from decaying slowly which has an impact on the data collected. Because it was not possible doing it all in a day, due to time constraints, the beetroot became older over time, which of course naturally destroyed the cells. In order to prevent this, I could have used a new beetroot every time, but this would have been a waste of the vegetable that was not used. Also, I could have placed the test tubes (containing distilled water) into the water bath first, and then put in the beetroot pieces. This would have prevented the beetroot from lying longer in water for an inexact time. The experiments and the data collected demonstrated that the integrity of cell membranes in the Beta Vulgaris was affected by a change of temperature. The greater the temperature, the more pigment leaked from the vacuoles of the Beta Vulgaris cells and the transmission decreased, supporting the hypothesis for the temperature experiment. The hypothesis for the sucrose experiment was: "Beetroot cells contain sucrose. With the increase of temperature, the cells will leak sucrose. The increase of sugar outside the cells will be detectable with Benedict's test for reducing sugars." This hypothesis was supported by the experiment. The sucrose of the Beta Vulgaris reacted with the Benedict's solution and turned the solution orange. This means there were moderate amounts of reducing sugars present. 1 EURACHEM / CITAC Guide CG 4 'Quantifying Uncertainty in Analytical Measurement'; Third Edition, 2012