Performing Analyses and Evaluating Results Obtained

In order to evaluate results obtained from different calculation runs, a number of standardized reports were integrated into the decision support tool. The reports can be clustered into the three categories production network structure, cost structure and investment/restructuring activities (cf. Fig. 49). Since reports become very complex if they contain data from several scenarios, comparative analyses of different scenarios are performed via data downloads into MS Excel pivot spreadsheets. 

These results obtained from the analyses of industrial blends proved that the identification of the constituents of the different surfactant blends in the FIA-MS and MS-MS mode can be performed successfully in a time-saving manner only using the product ion scan carried out in mixture analysis mode. The applicability of positive ionisation either using FIA-MS for screening and MS-MS for the identification of these surfactants was evaluated after the blends examined before were mixed resulting in a complex surfactant mixture (cf. Fig. 2.5.7(a)). Identification of selected mixture constituents known to belong to the different blends used for mixture composition was performed by applying the whole spectrum of analytical techniques provided by MS-MS such as product ion, parent ion and/or neutral loss scans. 

Duplicate analyses were performed on a representative selection of all the actual samples collected. Different types of foods and cooked meals were analyzed by two different techniques, that is, ICP-AES and ICP-MS. In order to evaluate the accuracy of the adopted instrumental procedure correlation analysis with an LR method was carried out on the data. For the comparison R2, slope b and intercept a were evaluated, which in a theoretical model take on the values R2 = 1 (maximum accordance), b = 1 (absence of additive or multiplicative effects), and a = 0 (absence of bias). Typical results obtained are those illustrated for A1 (R2 = 0.94, a =-0.92, b= 1.02) and Mn (R2 = 0.83, a =-0.13, b = 0.92) in Figures 10.1 and 10.2, respectively. 

Upon arrival, the sample should be logged in the laboratory record book indicating time of arrival, sample temperature, and pH. The sample should in all cases be analyzed for residual chlorine and, if present, oxidized with sodium thiosulfate before it is employed for toxicity evaluation. A portion of the sample should also be removed for alkalinity and hardness analyses. Often it is necessary to coarse filter the sample to remove floe or suspended debris before testing however, this practice may reduce the sample s toxicity. The remainder of the sample should be kept at 4°C for a period not exceeding 72 h after initial sample collection. It is desirable to employ two separate 24 h composite samples for performing a 96 h acute larval fathead minnow test. This would allow renewal after 48 h exposure. In the 7 day tests with Ceriodaphnia and Pimephales, three separate 24 h composite samples should be employed for daily renewal of the various exposure solutions. Toxicity data summary sheets should include daily routine physico-chemical measurements and sample information. It is essential that good laboratory practices be used in all aspects of sample collection, treatment, and analysis to obtain quality and defensible results. 

A principal point in this scheme is that accuracy characteristics should be estimated before an analytical procedure is regularly used and should be the characteristics of any future result obtained by application of the procedure under specified conditions. Measurements of this type are most commonly performed (by technicians, not measurement scientists) in control engineering and are sometimes called technical measurements . It is such measurements that are usually referred to as routine in chemical analysis. In fact, the problems of evaluation of routine analyses faced by chemists are treated more generally in the technical measurements theory [15]. 

The technical evaluation may also lead to the comparison of the results obtained from different methods. It will allow participants to extract information by comparing and possibly discussing their performance and method with other participants applying similar procedures, i.e. it may allow to discover biases in methods. If several enriched materials have been prepared and analysed the organiser may produce Youden plots where trends and systematic errors can appear [10-12]. Such more elaborated data presentations have to be issued with sufficient explanations to avoid misunderstanding and wrong conclusions. More advanced data treatment require the application of suitable robust statistics which have to be carefully chosen to arrive at sound scientific conclusions. Their meaning should always be explained and documented. 

Quantitative analyses are obtained by densitometry. The TLC scaimer used for this is linked to a personal computer (PC) and is controlled by an evaluation program. The PC performs the calculation of the result, supports the protocol, and provides an archive of all the parameters of the equipment and evaluation program, the raw data and the numerical and graphical results. 

As described in Section 3.2, interlaboratory studies are organized in such a way that several laboratories analyse a common material which is distributed by a central laboratory responsible for the data collection and evaluation. When laboratories participate in an interlaboratory study, different sample pretreatment methods and techniques of separation and final determination are compared and discussed, as well as the performance of these laboratories. If the results of such an intercomparison are in good and statistical agreement, the collaboratively obtained value is likely to be the best approximation of the truth. 

A number of laboratory tests to determine LOD, linearity and repeatability of MIMS instruments applied to the analysis of VOCs in water were performed. Data were comparable with those obtained by the classical method of VOC analysis in water (P T/GC/MS and USEPA Method 8260B). Four MIMS instruments were tested over an extensive period of time to evaluate their on-site performance in unmanned locations. Results were remarkable the instruments worked unchecked for long periods producing a total of more than 45.000 analyses and VOC amounts were quantified automatically and sent to a remote control room where non-expert personnel could understand the results readily. 

The previous chapters of this book have discussed the many activities which laboratories undertake to help ensure the quality of the analytical results that are produced. There are many aspects of quality assurance and quality control that analysts carry out on a day-to-day basis to help them produce reliable results. Control charts are used to monitor method performance and identify when problems have arisen, and Certified Reference Materials are used to evaluate any bias in the results produced. These activities are sometimes referred to as internal quality control (IQC). In addition to all of these activities, it is extremely useful for laboratories to obtain an independent check of their performance and to be able to compare their performance with that of other laboratories carrying out similar types of analyses. This is achieved by taking part in interlaboratory studies. There are two main types of interlaboratory studies, namely proficiency testing (PT) schemes and collaborative studies (also known as collaborative trials). 

Realistic predichons of study results based on simulations can be made only with realistic simulation models. Three types of models are necessary to mimic real study observations system (drug-disease) models, covariate distribution models, and study execution models. Often, these models can be developed from previous data sets or obtained from literature on compounds with similar indications or mechanisms of action. To closely mimic the case of intended studies for which simulations are performed, the values of the model parameters (both structural and statistical elements) and the design used in the simulation of a proposed trial may be different from those that were originally derived from an analysis of previous data or other literature. Therefore, before using models, their appropriateness as simulation tools must be evaluated to ensure that they capture observed data reasonably well [19-21]. However, in some circumstances, it is not feasible to develop simulation models from prior data or by extrapolation from similar dmgs. In these circumstances, what-if scenarios or sensitivity analyses can be performed to evaluate the impact of the model uncertainty and the study design on the trial outcome [22, 23]. 

Those entrusted with the development of propellant systems frequently have attempted to gain insight from new analyses, only to find the fundamental conceptual results of the analyses obscured in non-understandable detail. Correspondingly, many books which have appeared report and analyze the performance of chemical propellants (9 through 13). Yet rarely is an attempt made to explain why the performance results of the analyses are oriented as they are. This monograph is an attempt to state clearly the important concepts of propellant evaluation and to answer the important question of why Why is one propellant better than another, why does the point of maximum specific impulse shift towards stoichiometric mixture ratio as the chamber pressure is increased, why doesn t one obtain equilibrium chamber product concentrations with certain monopropellants, etc ... 

In the initial analysis we performed, the first two methods described above were compared with the sodium carbonate sodium dioxide flux method and a very close similarity in the results for more of the elements analysed was found. It is felt, however, that the values reported in this study are only valid for comparative purposes. They should not be considered as absolute values. Comparison of the three methods is still under study and it is not possible yet to give a final evaluation of them. The use of a rock standard in a destructive method still under study does not seem wise, considering the problems faced in obtaining these standards in Mexico. It is possible to find some elements that give high readings due to interference or emission from the matrix, and to find some others, which may be absorbed in the precipitate, that give abnormally low results. 

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