Multispecies analysis

On the other hand, when latent variables instead of the original variables are used in inverse calibration then powerful methods of multivariate calibration arise which are frequently used in multispecies analysis and single species analysis in multispecies systems. These so-called soft modeling methods are based, like the P-matrix, on the inverse calibration model by which the analytical values are regressed on the spectral data ... 

The reliability of multispecies analysis has to be validated according to the usual criteria selectivity, accuracy (trueness) and precision, confidence and prediction intervals and, calculated from these, multivariate critical values and limits of detection. In multivariate calibration collinearities of variables caused by correlated concentrations in calibration samples should be avoided. Therefore, the composition of the calibration mixtures should not be varied randomly but by principles of experimental design (Deming and Morgan [1993] Morgan [1991]). 

Although the relative low efficiency of Raman scattering may be seen as a distinct drawback for practical applications, it turns out that it still can be a very valuable alternative to other spectroscopic techniques. In particular, Raman spectroscopy can become extremely attractive if rapid multispecies analysis is an issue. In multispecies LIF a tuneable laser system is required, which in most cases will have to be tuned over a wide spectral range to probe all desired species sequentially. In contrast, by virtue of its non-resonant nature, only a single fixed-frequency laser is required in Raman spectroscopy. On the detection side, all the traditional means of spectral dispersion and photodetection can be applied, as was outlined in Chapter 7 for LIF. The only care to be taken is to provide a means for... 

If amperometric sensors work with constant U, the response time can be short and they can be used in real-time measurements. On the other hand, for a multispecies analysis system, it is necessary to draw the entire 1(U) curve (or to use a data acquisition system) to locate the different diffusion plateaus. Such sensors, like coulometric sensors, are more adapted for... 

Nowadays, analytical chemistry has a large variety of methods, techniques and apparatus at its disposal and is able to play its instruments with high virtuosity. Therefore, the wide range of performance which analytical chemistry can achieve is extremely varied and extends from simple binary decisions (qualitative analysis) to quantitative analysis at the ultratrace level, from structure elucidation and species identification to studies of the dynamics and the topology of multispecies systems by means of temporally and spatially high-resolving techniques. 

These properties are especially important in the design, data analysis, and interpretation of multispecies toxicity tests, field studies, and environmental risk assessment and will be discussed in the appropriate sections. This alternate approach rejects the smooth transition of effects and recognizes that ecosystems have fundamentally different properties and are expected to react unexpectedly to contaminants. 

The most important parameter is a clear identification of the specific question that the toxicity test is supposed to answer. The determination of the LC50 within a tight confidence interval will often require many fewer organisms than the determination of an effect at the low end of the dose-response curve. In multispecies toxicity tests and field studies, the inherent variability or noise of these systems requires massive data collection and reduction efforts. It is also important to determine ahead of time whether a hypothesis testing or regression approach to data analysis should be attempted. 

Since the information about past events can be kept in a variety of forms, from the dynamics of populations to the genetic sequence of mitochondria, it is necessary to be able to incorporate each of these types of data into the design and analysis of the experiment. Assumptions about recovery are invalid and tend to cloud the now-apparent dynamics of multispecies toxicity tests. The ramifications are critical to the analysis and interpretation of these tests. 

Data Analysis and Interpretation of Multispecies Toxicity Tests... 

A large number of data analysis methods have been used to examine the dynamics of these structures. The analysis techniques should be able to detect patterns, given the properties of multispecies toxicity tests described above. In order to conduct proper statistical analysis, the samples should be true replicates and in sufficient number to generate the required statistical power. The analysis techniques should be multivariate, able to detect a variety of patterns, and to perform hypothesis testing on those patterns. 

Multispecies toxicity tests come in a wide variety of types (artificial streams, generic freshwater, simulated farm ponds, ditches, experimental plots, and forests), and they share basic properties. Experimental designs should take into account the advantage of these properties to ensure an interpretable experimental result. We propose the following design parameters for experimental design, analysis, and interpretation. 

Multivariate methods are more suitable for the data analysis of multispecies toxicity tests. No one multivariate technique is always best. Given that many responses of multispecies toxicity tests are nonlinear, techniques that do not assume linear relationships may allow a more accurate interpretation of the test system. 

Landis, W.G., R.A. Matthews, and G.B. Matthews. 1997. The design and analysis of multispecies toxicity tests for pesticide registration. Ecol. Appl. 7 1111-1116. 

Matthews, G.B., R.A. Matthews, and W.G. Landis. 1995. Nonmetric clustering and association analysis Implications for the evaluation of multispecies toxicity tests and field monitoring. Environmental Toxicology and Risk Assessment, Vol. 3, ASTM 1218. J.S. Hughes, G.R. Biddinger, and E. Mones, Eds. American Society for Testing and Materials, Philadelphia, PA, pp. 79-93. 

Apparently as an independent development, A.R. Johnson (1988a) proposed the idea of using a multivariate approach to the analysis of multispecies toxicity tests. This state space analysis is based upon the common representation of complex and dynamic systems as an n-dimensional vector. In other words, the... 

The following examples demonstrate the usefulness of multivariate methods in the evaluation of field ecological data and laboratory multispecies toxicity tests. In each of the examples, several multivariate techniques were used — generally Euclidean and cosine distances (Figure 11.29), principal components, and nonmetric clustering and association analysis. 

In both studies, nonmetric clustering outperformed the metric tests, although both principal components analysis and correspondence analysis yielded some additional insight into large-scaled patterns, which was not provided by the nonmetric clustering results. However, nonmetric clustering provided information without the use of inappropriate assumptions, data transformations, or other dataset manipulations that usually accompany the use of multivariate metric statistics. The success of these studies and techniques led to the examination of community dynamics in a series of two multispecies toxicity tests. 

As a first test of the use of multivariate analysis in the interpretation of multispecies toxicity tests, the dataset used to analyze the CR microcosm experiment was presented in a blind fashion for analysis. Neither the purpose nor the experimental setup was provided for the analysis. Nonmetric clustering was used to rank variables in terms of contribution and to set clusters. 

Smith, E.P., K.W. Pontasch, and J. Cairns, Jr. 1990. Community similarity and the analysis of multispecies environmental data a unified statistical approach. Water Res., 24(4) 507-514. 

The increasing demand on multispecie chemical analysis and use of highly sophisticated computer controlled instrumentation has made significant changes in the modern laboratory. 

Figure 9.4 Risk assessment for an aquatic environment based on a probabilistic procedure into which the concept of varying sensitivity in multispecies communities is incorporated (Nendza, Volmer and Klein, 1990). Exposure and effects are determined separately from experimental or, if not available, QSAR data. Physico-chemical data and information on bioaccumulation and biotransformation are the input for computer simulations of transport and distribution processes that estimate the concentrations of a potential contaminant in a selected river scenario, using, for example, the EXAMS model (Bums, Cline and Lassiter, 1982). For the effects assessment, the log-normal sensitivity distribution is calculated from ecotoxicological data and the effective concentrations for the most sensitive species are determined. The exposure concentrations and toxicity data are then compared by analysis of variance to give a measure of risk for the environment. Modified from Nendza, Volmer and Klein (1990) with kind permission from Kluwer Academic Publishers, Dordrecht.

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