The phrase "amount of sample necessary" is correct and usable in written English.
It can be used in contexts where you are discussing the quantity of a sample required for a specific purpose, such as in scientific research or testing.
Example: "To ensure accurate results, we need to determine the amount of sample necessary for the experiment."
Alternatives: "quantity of sample required" or "sample size needed".
To reduce the amount of sample necessary for the analysis, sequential extraction has also been proposed [ 6].
Another challenge is that the amount of sample necessary to perform these analyses is often significant and can be difficult to obtain in pediatric or severely ill patients.
The percentages of DPPH· scavenged (RSa %) by test samples were calculated as: The radical scavenging activity fifty (RSa50) corresponding to the amount of sample necessary to decrease by 50% the amount of free radical DPPH was determined by plotting the scavenging activity against the logarithm of sample concentration [ 16].
The probit values were plotted against the logarithmic values of concentrations of the test samples and a linear regression curve was established in order to calculate the EC50 (μg/ml), which is the amount of sample necessary to decrease by 50% the absorbance of DPPH [ 11].
On the other hand, the experimental design based on the primary cultures substantially decreased the available amount of samples necessary for powerful statistics.
In particular, we are optimistic about the promise of the paradynamics approach as an effective tool to reduce the cost of high-level QM/MM calculations by using a mostly automated parameter refinement procedure to limit the amount of sampling necessary on the high-level surface.
The amount of sample material necessary at present is ∼1.5 mg of silica and the reproducibility achieved for natural samples is better than ±0.15‰.
However, even for specialized sampling techniques, a large amount of samples is necessary to cover the relevant ranges of various input parameters.
By randomly excluding an increasing amount of GEO data sets and array samples from the training data, we investigated the minimum number of samples necessary to robustly build the probe effect model in TileProbe.
This new approach significantly reduced variation among samples and reduced the amount of necessary sample used in the assay.
Among them, the hydrolysis of proteins is responsible for most analysis errors and a precise technique for handling small amounts of samples is necessary.
