*3.3.2. Method of evaluation and results*

A polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) was performed to characterize the microbial community. In Fig. 4 a DGGE fingerprint is shown. For the interpretation of such fingerprints statistical tools are necessary. DGGE data were converted into a binary system for cluster analysis (Fig. 4). As mentioned above, cluster analysis visualises the similarity between the samples in a dendrogram.

Ros et al. [27] show the cluster analysis of the DGGE profiles of 16S rDNA from the whole bacterial community. The cluster analysis illustrates the segregation of two soil groups. The clusters are caused by two different amendments. One cluster comprises the soil with compost and nitrogen application, the second cluster represents the soil with amendment of different composts (compost + nitrogen as mineral fertiliser).


**Figure 4.** DGGE fingerprint and an example of a binary DGGE data matrix

#### *3.3.3. Conclusion*

Ros et al. [27] concluded that the differences between soils with compost with additional nitrogen fertiliser, and the second cluster comprising compost, control and mineral fertiliser soils are stronger than the influence of the different compost types. Furthermore they hypothesised that a certain microbial community inherent to the different composts is irrelevant after 12 years of compost application. Based on the cluster analyses of the PCR-

DGGE data, they concluded that the combined application of compost and nitrogen affected soil properties regarding microbial communities much more.

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

#### *3.4.1. Objective of the study*

28 Multivariate Analysis in Management, Engineering and the Sciences

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

A polymerase chain reaction coupled with denaturing gradient gel electrophoresis (PCR-DGGE) was performed to characterize the microbial community. In Fig. 4 a DGGE fingerprint is shown. For the interpretation of such fingerprints statistical tools are necessary. DGGE data were converted into a binary system for cluster analysis (Fig. 4). As mentioned above, cluster analysis visualises the similarity between the samples in a

Ros et al. [27] show the cluster analysis of the DGGE profiles of 16S rDNA from the whole bacterial community. The cluster analysis illustrates the segregation of two soil groups. The clusters are caused by two different amendments. One cluster comprises the soil with compost and nitrogen application, the second cluster represents the soil with amendment of

1 2 3 4 5 6 7 8 9 10 11 12 … 1 2 3 4 5 6 7 8 9 10 11 12

80 0 0 0 0 0 0 0 1 1 0 0 0 GC 0 0 0 0 0 0 0 1 1 0 0 1 OWC 0 1 1 0 0 1 1 1 1 0 0 0 MC 0 1 1 0 0 1 1 1 1 0 0 0 SSC 0 1 1 0 0 1 1 1 1 0 0 0 Control 0 1 1 0 0 1 1 1 1 0 0 0 GC+80 0 1 0 0 0 1 1 1 1 0 1 1 OWC+80 0 1 1 0 0 1 1 0 1 0 0 0 MC+80 0 1 1 0 1 1 1 1 1 0 0 0 SSC+80 0 1 1 0 0 1 1 1 1 0 0 0

Ros et al. [27] concluded that the differences between soils with compost with additional nitrogen fertiliser, and the second cluster comprising compost, control and mineral fertiliser soils are stronger than the influence of the different compost types. Furthermore they hypothesised that a certain microbial community inherent to the different composts is irrelevant after 12 years of compost application. Based on the cluster analyses of the PCR-

differences or similarities of microbial communities using DGGE data.

different composts (compost + nitrogen as mineral fertiliser).

**Figure 4.** DGGE fingerprint and an example of a binary DGGE data matrix

**. . . . . . .** 

**3.3. Cluster analysis (CA)** 

*3.3.1. Objective of the study* 

dendrogram.

*3.3.3. Conclusion* 

*3.3.2. Method of evaluation and results* 

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.

#### *3.4.2. Method of evaluation and results*

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 and manure compost.

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 between the products of stockpiles and compost.

#### *3.4.3. Conclusion*

Malley et al. [8] concluded that NIR spectroscopy and the multivariate data analysis method SIMCA can be a rapid, inexpensive method for assessing a composting process.
