*3.1.2. Method of evaluation and results*

In a first step Mikhailov et al. [62] collected conventional data to describe landfill stability. They investigated 3 different landfills in Russia, one illegal dump, an old poorly-run dump and a modern well-run landfill. They focused on geodesic surveys to obtain the overall object properties such as size, volume and different layers. Furthermore they investigated the physical and chemical properties of the samples collected in different depths of the landfill. The physical and chemical properties include ash content, humidity, and acidity. Using the conventional collected data they carried out a PCA for each landfill site. They included the ash content, temperature, volume weight, pH, humidity and depth. The PCA for the two landfills in Bezenchuk and Kinel are presented in the study [62]. Based on the data pool Mikhailov et al. [62] could identify two important sources of waste around Bezenchuk, a poultry farm and a granary. In addition to regular domestic refuse, the agricultural and industrial wastes were disposed illegally in this dump. Kinel on the other hand is a modern, well operated landfill, in which both domestic and industrial wastes are disposed. These assumptions were confirmed by chemometric investigations based on PCA. The mentioned PCAs show clustering of the different classes. The results of the PCA of the third investigated landfill are not shown in their study. Otradny was shown to be a poorly maintained landfill. Clear separation of layers by means of the scores plot was not possible. They found out that the information by the landfill manager and the results obtained did not correspond.

### *3.1.3. Conclusion*

Mikhailov et al. [62] concluded that multivariate data analysis is an appropriate tool for ecological monitoring. They pointed out that chemometric methods provide the possibility to explore the structure of waste disposal by identification of specific areas.

#### **3.2. Partial Least Square Regression (PLS1)**

#### *3.2.1. Objective of the study*

The verification of compost quality has to be monitored consistently. However this is timeconsuming and laborious. Due to the fact that NIR is a simple, accurate and fast technique used for routine analysis Michel et al. [42] hypothesised that NIR could be used for parameter prediction. The objective of the study was to use NIR spectroscopy to determine chemical and biological properties.

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

26 Multivariate Analysis in Management, Engineering and the Sciences

The objective of the study by Mikhailov et al. [62] was to evaluate the stability of landfills based on many conventional parameters such as ash content, temperature, volume weight, pH, humidity and depth. They supposed that a multivariate approach could provide a more efficient data interpretation. Therefore they compared conventional and multivariate data

In a first step Mikhailov et al. [62] collected conventional data to describe landfill stability. They investigated 3 different landfills in Russia, one illegal dump, an old poorly-run dump and a modern well-run landfill. They focused on geodesic surveys to obtain the overall object properties such as size, volume and different layers. Furthermore they investigated the physical and chemical properties of the samples collected in different depths of the landfill. The physical and chemical properties include ash content, humidity, and acidity. Using the conventional collected data they carried out a PCA for each landfill site. They included the ash content, temperature, volume weight, pH, humidity and depth. The PCA for the two landfills in Bezenchuk and Kinel are presented in the study [62]. Based on the data pool Mikhailov et al. [62] could identify two important sources of waste around Bezenchuk, a poultry farm and a granary. In addition to regular domestic refuse, the agricultural and industrial wastes were disposed illegally in this dump. Kinel on the other hand is a modern, well operated landfill, in which both domestic and industrial wastes are disposed. These assumptions were confirmed by chemometric investigations based on PCA. The mentioned PCAs show clustering of the different classes. The results of the PCA of the third investigated landfill are not shown in their study. Otradny was shown to be a poorly maintained landfill. Clear separation of layers by means of the scores plot was not possible. They found out that

the information by the landfill manager and the results obtained did not correspond.

to explore the structure of waste disposal by identification of specific areas.

**3.2. Partial Least Square Regression (PLS1)** 

Mikhailov et al. [62] concluded that multivariate data analysis is an appropriate tool for ecological monitoring. They pointed out that chemometric methods provide the possibility

The verification of compost quality has to be monitored consistently. However this is timeconsuming and laborious. Due to the fact that NIR is a simple, accurate and fast technique used for routine analysis Michel et al. [42] hypothesised that NIR could be used for parameter prediction. The objective of the study was to use NIR spectroscopy to determine

**3.1. Principal component analysis (PCA)** 

*3.1.2. Method of evaluation and results* 

*3.1.1. Objective of the study* 

analysis methods.

*3.1.3. Conclusion* 

*3.2.1. Objective of the study* 

chemical and biological properties.

The first step was to define compost quality. Michel et al. [42] defined compost quality by C and N contents, suppression of pathogens, stability/ maturity and biological parameters, especially organic carbon (Corg), total N (Nt), C:N ratio, age, microbial biomass (Cmic), Cmic:Corg, basal respiration, enzymatic activity and suppression of plant disease. Spectroscopic data from 98 composts samples as well as the mentioned conventional parameters were collected. Fundamental relations between two matrices can be found by means of PLS1. Michel et al. [42] applied a PLS1 to express conventional parameters by spectral data. They designed for each conventional parameter a PLS1. Table 2 summarises the collected data and results obtained by Michel et al. [42]. The standard error of crossvalidation (SECV) and the coefficient of determination (r2) indicate the quality of prediction. The SECV provides information on the prediction error, r2 demonstrates the quality of correlation. Composting age and basal respiration show the highest r2. The specific enzymatic activity and the suppressive effect show the lowest r2. It should be emphasised that biological tests that are carried out with the original wet compost are more susceptible to interferences due to the heterogeneity of the material. Michel et al. [42] concluded that especially compost age and basal respiration are clearly reflected by the NIR spectrum and feature the best results. By contrast, the specific enzyme activity and suppressive effects show the worst prediction results. The assigned correlations are illustrated in the paper [42].


**Table 2.** Excerpt of table 1 and 2 by Michel et al. [42], SECV = standard error of cross-validation, r2 = the coefficient of determination

#### *3.2.3. Conclusion*

Michel et al. [42] concluded that NIR spectroscopy was a capable method to predict various chemical and biological parameters using PLS regression. They believe NIR spectroscopy to be capable of monitoring compost quality.
