Sampling frequency optimized

The objective of control is quite different. The purpose of control is to keep a process property, e.g. the composition, as close to a preset value as is technically possible and economically desirable. The deviation from the set point is caused by intentional or random fluctuations of the process condition. In order to control the fluctuating process, samples must be taken with such frequency and analyzed with such reproducibility and speed that the process condition can be reconstructed. From this reconstruction predictions can be made for the near future and control action can be optimal. Another goal can be the detection of nonrandom deviations, like drift or cyclic variations. This also sets the conditions for sampling frequency and sample size. 

Leemans described a sampling scheme based on these algorithms that considers sampling frequency, sampling time, dead time and accuracy of the method of analysis to obtain optimal information yield or maximal profit when controlling a factory. 

In general, reliable data on emission sources reduce monitoring costs because such data may provide a good basis for choosing proper sampling locations, optimal number of sampling sites and appropriate sampling frequencies. 

As with all methods and devices for quantitative analysis, the optimization of electrochemical biosensors is directed at the following features (linear) measuring range, lower limit of detection, response time and sample frequency, precision, accuracy, stability, and selectivity for the target analyte. 

A detailed study of all the factors that influence the sampling frequency complemented with a computerized procedure for optimization of continuous segmented configurations was reported by Angelova and Holy [13]. 

The intelligent design of a piezoelectric chemical sensor must consider all the trends discussed above. It can be seen that optimization of a sensor design is a complex function of analyte, analyte concentration, and sample matrix, as well as the requirements for selectivity, sampling frequency, response time, linearity of response, and sensitivity. No one design will be satisfactory for more than a few applications. 

The optimal time scale depends on the intended application. Generally, the same X variables are prominent regardless of the time scale used, but the goodness of fit of the model is heavily influenced by the sampling frequency of... 

For the purpose of fully utilizing the ultra-high performance, the instrumentation should be optimized as well. Technical challenges are seen especially in pumping and detection systems. For example, the super-high speed separations can be confirmed only by enhancing the time resolution of the detection at increased sampling frequency (Fig. 3-3). 

Capitan-Vallvey et al. [89] developed a simple, rapid, and inexpensive monoparameter flow-through sensor for the determination of saccharin. The method is based on the transient adsorption of the sweetener on Sephadex G-25 solid phase packed to a height of 20 mm in the flow cell. The optimal transient retention of the synthetic sweetener, in terms of sensitivity and sampling frequency, was obtained when pH 2.75 citric acid-sodium citrate buffer 5 x 10 M was used as a carrier at a flow rate of 1.5 mL/ min. Saccharin was determined by measuring its intrinsic absorbance at 217 nm at its residence time. The method does not need derivatization reactions and utilized a FIA monochannel manifold similar to those reported by the same investigation team as used to determine other artificial sweeteners [73-75]. 

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