*3.3.1. Objective of the study*

The objective of the study by Ros et al. [27] was to find out the long-term effects of composts on soil microbial communities. Different types of compost were applied over a period of 12 years. DNA was extracted by Ros et al. [27] from differently treated soils. The microbial community was described by polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE). They used multivariate data analysis to show the differences or similarities of microbial communities using DGGE data.

Application of Multivariate Data Analyses in Waste Management 29

DGGE data, they concluded that the combined application of compost and nitrogen affected

Malley et al. [8] used a portable near infrared (NIR) spectrometer to investigate changes of biogenic waste materials during composting. The idea of this study was to observe the composting process continuously in an easy and inexpensive way using NIR spectroscopy.

First of all many spectra were collected by Malley et al. [8]. The interpretation of spectral data requires experience in spectral interpretation. To provide rapid interpretation of the measured infrared spectra Malley et al. [8] applied the classification method SIMCA. The SIMCA model allows the assignment of a new sample to a defined class. A SIMCA model is always based on the PCAs of the various defined classes. Malley et al. [8] defined 3 different classes: raw manure (M), stockpiled manure (S) and manure compost (C). In the study 2 years of composting were observed (2000 and 2001). Figure 2 by Malley et al. [8] shows the scores plot of the PCA based on the spectral data of the three different classes in the year 2001. The PCA demonstrates a clear grouping of the 3 classes manure, stockpiled manure

Malley et al. [8] illustrated the results of the SIMCA by means of a Coomans plot. In figure 3 by Malley et al. [8] they show the Coomans plot for the investigations of 2001. The vertical and horizontal lines in the Coomans plot mark the 5 % level of significance. That means that 95 % of the samples that truly belong to this group are found within the line. Due to the fact that compost lies on the opposite side of the vertical line from the raw and stockpiled samples Malley et al. [8] concluded that compost is significantly different from the other two classes. The groups of raw manure and stockpiled manure are overlapping. Thus Malley et al. [8] concluded that they did not differ significantly. Nevertheless some raw samples were different. With these results Malley et al. [8] demonstrated that spectroscopic data and multivariate data analysis, especially SIMCA provides a sensitive analysis to differentiate

Malley et al. [8] concluded that NIR spectroscopy and the multivariate data analysis method

In fact there are some statistical restrictions, which cannot be solved easily. The simple situation starts with the general linear model. This model usually has a character variable y

SIMCA can be a rapid, inexpensive method for assessing a composting process.

**4. Critical discussion of multivariate statistical methods** 

depending on one or more predictor variables x1, x2, …, xk:

soil properties regarding microbial communities much more.

*3.4.1. Objective of the study* 

and manure compost.

*3.4.3. Conclusion* 

*3.4.2. Method of evaluation and results* 

between the products of stockpiles and compost.

**3.4. Soft independent modelling of class analogy (SIMCA)** 
