Hello 

If you grind all the ten plants together that will count  as one biological replicate. And since you need at least three biological replicate groups for data analysis and statistical evaluations therefore I would recommend to split all your ten samples in three separate biological replicate groups and grind all plants with in that group together and do the estimations. you can then use these three estimations for statistical analysis etc.

HK

Hello 

If you grind all the ten plants together that will count  as one biological replicate. And since you need at least three biological replicate groups for data analysis and statistical evaluations therefore I would recommend to split all your ten samples in three separate biological replicate groups and grind all plants with in that group together and do the estimations. you can then use these three estimations for statistical analysis etc.

HK

Hello,

First, I agree with Harmeet Kaur. You have to separately assay proline content for each. It is because the data calculate correctly.

Good Luck!

Ehab MB Mahdy

Asalam alicom

i support the suggestion of Harmeet and Ehab for statistical analysis. in works i found that determination of proline in fresh plant tissue better than dry. 

good luck

Dear,

What method for proline estimation you are going to adapt?

Whether it is of Bates method of extraction in toluene...?

Thanks for your help dr. Harmeet Kaur , Ehab M. Badr Mahdy, Ahmed A Khalafallah and Ovaid Akhtar. 

Hello

The comments are clear and correct.  Separate samples are needed for analysis and statistics, otherwise it is "pseudo-replication" which is not correct.  Several plants combined in one sample which can be analysed as a replicate (and then make several such replicates)  can be useful in giving more uniform sampling.

Regards

You can also do this in two replicates (each with five plants ) or five replicates (each with two plants). Increasing replicates could produce better and more precise results, but decreasing replicates is a way of reducing time and measurement costs. You can choose one of these ways, depending on your priority.

Many thanks for you David W. Lawlor and Kazem Ghassemi-Golezani, but still confused about replicates( biological replicates)  and repeats (making replicates during the assay of a metaobolite  for the mixture of the ten plants ).  

Hello Yasser

Replication is often a problem, because of the time and cost involved as pointed out by Kazem but correct replication is essential to enable proper evaluation of the problem being considered and application of correct statistics.  So it is necessary to always have replication irrespective of time etc: if is better to do an experiment properly than to do it incorrectly and get a results which is basically of no value. A replicate is sometimes called a "repeat" but to  repeat means to do again - after a time - whereas a replicate' is don with the other replicates at the same time.

The number of replicates should ideally be determined by doing an experiment to test the methods. Note, earlier comments about using fresh plant material or dry -  drying may cause proline to accumulate in the material, so fresh is probably better but be careful to  analyse before the tissue loses water, or freeze quickly and analyse later. You could or should test this before starting the study. 

First test the analysis on a standard of proline (generally purchased from a reputable supplier) by  adding different amounts to a uniform sample of the material you will be analysing (include a blank to measure the amount of proline in the original material). Repeat the measurement several times  for each concentration. This will tell you a lot about the analysis methods, quantities etc and if you have sufficient replicates for the analysis to get an acceptable statistical error, to give say a standard error of the man of 1, 5 or 10%. 

For the experiment:  Say you want to test how many replicates are required to analyse proline in plant material grown  in field trials with a wet and dry treatment. Each treatment will have 3  replicated plots, watered independently (ideally): 3 is a number often used as it is the minimum for statistical purposes. From each plot take three plants at random.  The amount of proline in each plant from each plot is measured using the required number of replicates of the proline analysis (let's say 3).   There are 2 treatments (the effect on the plant is to be examined) X 3 replicate plots (treatment replicates) X 3 plants (`biological replicates')  X 3 `analytical replicates' that gives = 54 samples to analyse. This is quite a lot of work! But it is a fully replicated experiment which takes into account random errors in the treatment of the plants (differences between plots and the treatment), variations between plants within plots and errors in the analysis between each plant.  The data can be statistically analysed with confidence and the result are meaningful, 

It is possible to decrease the amount of work by combining, say, the three plants from 1 plot together to give a bulked sample and then using 2 replicates of the plant material from the plot. This gives 2 X 3 X 2 x 3 = 36 samples. If the analysis is very good with very small errors then replication of the analysis might be decreased to 2 so giving 24 samples, which is not so large! But decreasing replication means the experiment is not as statistically `good'.

This all sounds very complex and demanding . The reason for it is that experience over many years shows that  combining experimental  methods with statistics  give the best results and thus provide a firm basis for understanding. I hope I have not put you off but encouraged you to apply the scientific approach.

Regards

Many thanking for you my dear Dr.David W. Lawlor , You make me in the right and the clear way thank you again for your kind .

Hello Dear Yasser

The comments are clear and correct. But 1 g for each replication is high! For each replication 

To measuring of proline content in each replication, 0.3 to 0.4 g is enough. More than 0.5 g (especially in sorghum) cause to over in spectrophotometer.

Good Luck