**2. Multivariate data analysis in waste management**

The main objectives of multivariate data analysis are exploratory data analysis, classification and parameter prediction. Many different multivariate data analysis methods exist in literature. Thus the following list is not exhaustive however subdivided into the mentioned superior categories. It only concentrates on the methods applied in waste management.

Table 1 gives an overview of the existing literature in waste management on multivariate data analysis applied by several authors. It can be summarised that PCA and PLS1 are the most popular multivariate data analysis methods applied in waste management. Details are given in the following sections 2.1 and 2.2. Due to easy traceability of the parameters investigated in the different papers parameter descriptions have been taken as they were mentioned in the original.

In practice there are many software packages available which include different multivariate data analysis methods. Some software tools are: SPSS (www.spss.com\de\statistics), Canoco (www.canoco.com), The Unscrambler (www.camo.com) and the Free Software Rproject (www.cran.r-project.org).


**Table 1.** Literature review of different multivariate data analysis methods applied in waste management; PCA – Principal Component Analysis, FA – Factor Analysis, CA – Cluster Analysis, CCA – Canonical Correspondence Analysis, DA – Discriminant Analysis, SIMCA – Soft Independent Modelling of Class Analogy, MLR – Multiple Linear Regression, PLS-R – Partial Least Squares Regression, PSR – Penalised Signal Regression

#### **2.1. Pattern recognition**

16 Multivariate Analysis in Management, Engineering and the Sciences

waste management research and practice is given.

mentioned in the original.

Compost science

Municipal solid waste

Landfill research

project (www.cran.r-project.org).

**2. Multivariate data analysis in waste management** 

In this study an overview of multivariate data analysis methods and their application in

The main objectives of multivariate data analysis are exploratory data analysis, classification and parameter prediction. Many different multivariate data analysis methods exist in literature. Thus the following list is not exhaustive however subdivided into the mentioned superior categories. It only concentrates on the methods applied in waste management.

Table 1 gives an overview of the existing literature in waste management on multivariate data analysis applied by several authors. It can be summarised that PCA and PLS1 are the most popular multivariate data analysis methods applied in waste management. Details are given in the following sections 2.1 and 2.2. Due to easy traceability of the parameters investigated in the different papers parameter descriptions have been taken as they were

In practice there are many software packages available which include different multivariate data analysis methods. Some software tools are: SPSS (www.spss.com\de\statistics), Canoco (www.canoco.com), The Unscrambler (www.camo.com) and the Free Software R-

Method PCA FA CCA CA DA SIMCA MLR PLS1 PLS2 PSR

[50-55] [56] [17, 53,

76, 77]

management; PCA – Principal Component Analysis, FA – Factor Analysis, CA – Cluster Analysis, CCA – Canonical Correspondence Analysis, DA – Discriminant Analysis, SIMCA – Soft Independent Modelling of Class Analogy, MLR – Multiple Linear Regression, PLS-R – Partial Least Squares

Chapter 2.1.1 2.1.2 2.1.3 2.1.3 2.2.1 2.2.2

22, 24- 31]

[1-23] [24] [25] [1, 4,

[59-72] [65] [73, 74] [72, 75] [66, 71,

Regression, PSR – Penalised Signal Regression

Logistics [81] [82] [82] [83, 84]

**Table 1.** Literature review of different multivariate data analysis methods applied in waste

Pattern recognition Calibration

[3, 9] [8, 12] [29, 32,

33]

[78] [79, 80] [17, 61,

[2, 6, 8, 19, 21, 23, 34- 47]

57, 58]

62, 66, 71, 78]

[8, 21, 48]

[49]

#### *2.1.1. Exploratory data analysis*

Principal Componant Analysis (PCA)

PCA is mathematically defined as an orthogonal linear transformation that arranges the data to a new coordinate system in that the greatest variance by any projection of the data takes place along the first coordinate (called the first principal component), the second greatest variance along the second coordinate, and so on. Theoretically the PCA is the optimum transformation for a given data set in least square terms. That means PCA is used for dimensionality reduction of variables in a data set by retaining those characteristics of the data set that contribute most to its variance. The transformation to the new coordinate system is described by scores (T), loadings (P) and errors (E). In matrix terms, this can be written as X = T \* P + E. Fig. 1 illustrates the mathematical transformation using PCA. The matrices can be displayed graphically. The scores matrix illustrates the data structure and the loading matrix displays the influence of the different variables on the data structure.

**Figure 1.** Principle of the PCA (according to Esbensen [85])

PCA displays hidden structures of huge data sets. PCA is applied in different fields of waste management to find out the relevant parameters of a large parameter set. So we can see which properties of a sample are significant and important to answer a particular question. Due to the results obtained time and money can be saved in further research activities.

Many applications can be found in compost science. Zbytniewski and Buszewski [1] applied PCA to reveal the significant parameters and possible groupings of chemical parameters, absorption band ratios and NMR data. Campitelli and Ceppi [3] investigated the quality of different composts and vermicomposts. The collected data were evaluated by means of PCA to extract the significant differences between the two compost types. Gil et al. [4] used PCA to show effects of cattle manure compost applied on different soils. Termorshuizen et al. [13] carried out a PCA based on disease suppression data determined by bioassays in different compost/peat mixtures and pure composts. PCA was applied by Planquart et al. [10] to examine the interactions between nutrients and trace metals in colza (Brassica napus) when sewage sludge compost was applied to soils. LaMontagne et al. [7] applied PCA on terminal restriction fragment length polymorphisms (TRFLP) patterns of different composts to reveal their characteristics with respect to microbial communities. Malley et al. [8] recorded near infrared spectra from cattle manure during composting. The collected spectral data were

evaluated by PCA to show the relationships among samples and changes due to stockpiling and composting. Hansson et al. [6] observed the anaerobic treatment of municipal solid waste by using on-line near infrared spectroscopy. For spectral data interpretation PCA was carried out. Albrecht et al. [2] also performed a PCA for near infrared (NIR) spectra evaluation from an ongoing composting process. Smidt et al. [12] used PCA to show differences in spectral characteristics of different waste materials. Lillhonga et al. [23] used PCA to observe spectral characteristics of different composting processes. Vergnoux et al. [21] applied a PCA on NIR spectra as well as on physico-chemical and biochemical parameters to derive regularities from the data. Nicolas et al. [9] used PCA to evaluate data from an electronic nose. The correlations between the sensor of an electronic nose and chemical substances were determined by Romain et al. [11] using PCA. PCA was applied to observations of a composting process by means of analytical electrofocusing. The electrofocusing profiles were evaluated by Grigatti et al. [5]. PCA was also used by Biasioli et al. [19] to evaluate odour emissions and biofilter efficiency in composting plants using proton transfer reaction-mass spectrometry. Bianchi et al. [18] also used PCA to reduce the complex data set and to analyse the pattern of organic compounds emitted from a composting plant, a municipal solid waste landfill and ambient air. The effect of 14 different soil amendments on compost quality were evaluated using a PCA by Tognetti et al. [20]. Smidt et al. [16] applied PCA to illustrate the influence of input materials and composting operation on humification of organic matter. Böhm et al. [14] and Smidt et al. [15, 17] used PCA to illustrate spectral differences caused by different materials such as biowaste, manure, leftovers, straw and sewage sludge.

Application of Multivariate Data Analyses in Waste Management 19

groundwater. Olivero-Verbel et al. [63] investigated the relationships between physicochemical parameters and the toxicity of leachates from a municipal solid waste landfill. PCA was used to find out which parameters were responsible for their toxicity. Jean and Fruget [72] used PCA to compare landfill leachates according to their toxicity and physico-chemical parameters. Ecke et al. [71] showed an example for PCA application in landfill monitoring of data from landfill test cells, leachate and gas data. Smidt et al. [64] investigated landfill materials by means of mid infrared spectroscopy, thermal analysis and PCA. They used PCA to support data interpretation. Van Praagh et al. [70] investigated the potential impacts on leachate emissions using pretreated and untreated refuse-derived material as a cover layer on the top of a municipal solid waste landfill. To interpret leachate characteristics they used PCA. Tintner and Klug [69] used PCA to illustrate how vegetation can indicate landfill cover features. Diener et al. [67] investigated the long-term stability of steel slags used as cover construction of a municipal solid waste landfill by means of a PCA. Smidt et al. [17]

Pablos et al. [68] used a PCA to evaluate toxicity bioassays for biological characterisation of

Other publications focus on the process monitoring of municipal solid waste incineration residues. Ecke [50] performed PCA on leaching parameters from municipal solid waste incineration fly ash to get an overview of the mobility of metals under certain conditions. Mostbauer et al. [51] carried out PCA to observe the long-term behaviour of municipal solid

In the field of waste management logistics PCA is rarely applied. Dahlén et al [81] used PCA

FA is related to PCA but differs in its mathematical conception [86]. FA is also used to describe the variability of observed variables in terms of fewer variables called factors. That means factor analysis is a tool which reveals unobservable underlying features of a specific phenomenon by previous visible observations. The observed variables are modelled as linear combinations of the factors plus "error" terms. The information about

In waste management practice PCA is preferentially used. Differences between factor analysis and PCA are found to be small [86]. Srivastava and Ramanathan [65] investigated the groundwater quality of a landfill site in India by means of FA. They explained the observed relationship in simple terms expressed as factors. Bustamante et al. [24] used FA to identify the principal variables associated to the composting of agro-industrial wastes. Lin et

CCA is a multivariate method to explain the relationships between biological communities and their environment [87]. The method is designed to extract environmental gradients from

to display the impact of waste costs on a weight basis in a specific municipality.

interdependencies can be used to reduce the number of variables in a data set.

al. [82] used FA for selecting the best food waste recycling method.

Canonical Correspondence Analysis (CCA)

used PCA to display spectral characteristics of different landfill types.

hazardous wastes.

waste incineration (MSWI) residues.

Factor Analysis (FA)

PCA was also applied to illustrate the alteration of municipal solid waste during the biological degradation process reaching stability limits for landfilling as well as to demonstrate similarities and differences of reactor and old landfills based on thermal data [53, 66]. Scaglia and Adani [52] focused on municipal solid waste treatment. They used PCA to create a stability index for quantifying the aerobic reactivity of municipal solid waste. Abouelwafa et al. [54, 55] investigated the degradation of sludge from the effluent of a vegetable oil processing plant mixed with household waste from landfill. Abouelwafa et al. [54] applied PCA on various parameters measured during composting (e.g. pH, electrical conductivity, moisture, C/N, NH4/NO3, ash, decomposition in percent, level of polyphenols, lignin, cellulose, hemicellulose, humic acid) to find the main parameters in the decomposition and restructuring phase [54]. Abouelwafa et al. [55] extracted fulvic acids from the samples mentioned above and extended the data set used for PCA by a series of absorption band ratios resulting from of FTIR spectra.

PCA has also been used in landfill research. Mikhailov et al. [62] applied PCA for monitoring data from different landfills. They included parameters such as depth, ash content, volumetric weight, humidity, amounts of refuse in summer and winter as well as the topsoil depth of landfill sections, sewage sludge lenses and the existence of a protection system. Kylefors [61] investigated data of leachate composition using PCA. The idea was to reduce the analytical monitoring program for further investigations. Durmusoglu and Yilmaz [60] used PCA to extract the significant independent variables of the collected data of raw and pre-treated leachate. A comparable work was done by De Rosa et al. [59]. They also investigated the leachate composition of an old waste dump connected to the groundwater. Olivero-Verbel et al. [63] investigated the relationships between physicochemical parameters and the toxicity of leachates from a municipal solid waste landfill. PCA was used to find out which parameters were responsible for their toxicity. Jean and Fruget [72] used PCA to compare landfill leachates according to their toxicity and physico-chemical parameters. Ecke et al. [71] showed an example for PCA application in landfill monitoring of data from landfill test cells, leachate and gas data. Smidt et al. [64] investigated landfill materials by means of mid infrared spectroscopy, thermal analysis and PCA. They used PCA to support data interpretation. Van Praagh et al. [70] investigated the potential impacts on leachate emissions using pretreated and untreated refuse-derived material as a cover layer on the top of a municipal solid waste landfill. To interpret leachate characteristics they used PCA. Tintner and Klug [69] used PCA to illustrate how vegetation can indicate landfill cover features. Diener et al. [67] investigated the long-term stability of steel slags used as cover construction of a municipal solid waste landfill by means of a PCA. Smidt et al. [17] used PCA to display spectral characteristics of different landfill types.

Pablos et al. [68] used a PCA to evaluate toxicity bioassays for biological characterisation of hazardous wastes.

Other publications focus on the process monitoring of municipal solid waste incineration residues. Ecke [50] performed PCA on leaching parameters from municipal solid waste incineration fly ash to get an overview of the mobility of metals under certain conditions. Mostbauer et al. [51] carried out PCA to observe the long-term behaviour of municipal solid waste incineration (MSWI) residues.

In the field of waste management logistics PCA is rarely applied. Dahlén et al [81] used PCA to display the impact of waste costs on a weight basis in a specific municipality.

Factor Analysis (FA)

18 Multivariate Analysis in Management, Engineering and the Sciences

manure, leftovers, straw and sewage sludge.

absorption band ratios resulting from of FTIR spectra.

evaluated by PCA to show the relationships among samples and changes due to stockpiling and composting. Hansson et al. [6] observed the anaerobic treatment of municipal solid waste by using on-line near infrared spectroscopy. For spectral data interpretation PCA was carried out. Albrecht et al. [2] also performed a PCA for near infrared (NIR) spectra evaluation from an ongoing composting process. Smidt et al. [12] used PCA to show differences in spectral characteristics of different waste materials. Lillhonga et al. [23] used PCA to observe spectral characteristics of different composting processes. Vergnoux et al. [21] applied a PCA on NIR spectra as well as on physico-chemical and biochemical parameters to derive regularities from the data. Nicolas et al. [9] used PCA to evaluate data from an electronic nose. The correlations between the sensor of an electronic nose and chemical substances were determined by Romain et al. [11] using PCA. PCA was applied to observations of a composting process by means of analytical electrofocusing. The electrofocusing profiles were evaluated by Grigatti et al. [5]. PCA was also used by Biasioli et al. [19] to evaluate odour emissions and biofilter efficiency in composting plants using proton transfer reaction-mass spectrometry. Bianchi et al. [18] also used PCA to reduce the complex data set and to analyse the pattern of organic compounds emitted from a composting plant, a municipal solid waste landfill and ambient air. The effect of 14 different soil amendments on compost quality were evaluated using a PCA by Tognetti et al. [20]. Smidt et al. [16] applied PCA to illustrate the influence of input materials and composting operation on humification of organic matter. Böhm et al. [14] and Smidt et al. [15, 17] used PCA to illustrate spectral differences caused by different materials such as biowaste,

PCA was also applied to illustrate the alteration of municipal solid waste during the biological degradation process reaching stability limits for landfilling as well as to demonstrate similarities and differences of reactor and old landfills based on thermal data [53, 66]. Scaglia and Adani [52] focused on municipal solid waste treatment. They used PCA to create a stability index for quantifying the aerobic reactivity of municipal solid waste. Abouelwafa et al. [54, 55] investigated the degradation of sludge from the effluent of a vegetable oil processing plant mixed with household waste from landfill. Abouelwafa et al. [54] applied PCA on various parameters measured during composting (e.g. pH, electrical conductivity, moisture, C/N, NH4/NO3, ash, decomposition in percent, level of polyphenols, lignin, cellulose, hemicellulose, humic acid) to find the main parameters in the decomposition and restructuring phase [54]. Abouelwafa et al. [55] extracted fulvic acids from the samples mentioned above and extended the data set used for PCA by a series of

PCA has also been used in landfill research. Mikhailov et al. [62] applied PCA for monitoring data from different landfills. They included parameters such as depth, ash content, volumetric weight, humidity, amounts of refuse in summer and winter as well as the topsoil depth of landfill sections, sewage sludge lenses and the existence of a protection system. Kylefors [61] investigated data of leachate composition using PCA. The idea was to reduce the analytical monitoring program for further investigations. Durmusoglu and Yilmaz [60] used PCA to extract the significant independent variables of the collected data of raw and pre-treated leachate. A comparable work was done by De Rosa et al. [59]. They also investigated the leachate composition of an old waste dump connected to the FA is related to PCA but differs in its mathematical conception [86]. FA is also used to describe the variability of observed variables in terms of fewer variables called factors. That means factor analysis is a tool which reveals unobservable underlying features of a specific phenomenon by previous visible observations. The observed variables are modelled as linear combinations of the factors plus "error" terms. The information about interdependencies can be used to reduce the number of variables in a data set.

In waste management practice PCA is preferentially used. Differences between factor analysis and PCA are found to be small [86]. Srivastava and Ramanathan [65] investigated the groundwater quality of a landfill site in India by means of FA. They explained the observed relationship in simple terms expressed as factors. Bustamante et al. [24] used FA to identify the principal variables associated to the composting of agro-industrial wastes. Lin et al. [82] used FA for selecting the best food waste recycling method.

Canonical Correspondence Analysis (CCA)

CCA is a multivariate method to explain the relationships between biological communities and their environment [87]. The method is designed to extract environmental gradients from

ecological data sets. By means of the gradients an ordination diagram describing and visualising the diverse habitat preferences of taxa is calculated.

Application of Multivariate Data Analyses in Waste Management 21

extractable organic matter during cattle manure composting. Gil et al. [4] displayed dendrograms to illustrate the similarities or differences by application of cattle manure compost to different soils. Bustamante et al. [24] studied physico-chemical, chemical and microbiological parameters of different composts. The evaluation of the composts was

Lin et al. [82] applied a CA for the selection of optimal recycling methods for food waste.

A stepwise cluster analysis (SCA) was used to describe the nonlinear relationships among state variables and microbial activities of composts by Sun et al. [29]. Sun et al. [30] developed a genetic algorithm aided stepwise cluster analysis (GASCA) to describe the relationships between selected state variables and the C/N ratio in food waste composting.

Furthermore CA has often been used to evaluate microbiological data, especially in compost science [25-28, 31]. Innerebner et al. [26] and Ros et al. [27, 28] used CA to identify related samples and similar groups of microorganisms. Franke-Whittle et al. [25] used CA to show the similarities of Denaturing Gradient Gel Electrophoresis (DGGE) data of three different compost types with proceeding compost maturity. Xiao et al. [31] used a hierarchical cluster analysis of DGGE data to estimate the succession of bacterial communities during the active

Tesar et al. [75] applied CA to spectral data to illustrate the effect of in-situ aeration of a landfill. Jean and Fruget [72] used CA to compare landfill leachates on the basis of their

All supervised methods are classifications. Classification can be considered as a predictive method where the response is a category variable. Different classification methods exist. There are types of "hard" and "soft" modelling. Hard modelling means that a nonrelocatable line between the defined groups exists. One object can only belong to one group. Soft modelling allows an overlapping of the defined classes. An object can belong to both groups [88]. With regard to waste management practice two different classification methods

DA is a classification method of hard modelling. Campitelli and Ceppi [3] carried out a DA to distinguish between compost and vermicompost on the basis of parameters such as total organic carbon (TOC), germination index (GI), pH, total nitrogen (TN), and water soluble carbon (WSC). Nicolas et al. [9] performed a DA to classify data of an electric nose according to defined exceeded levels of odour. Ecke et al. [71] investigated samples from three different landfill sites by the biochemical methane potential and used DA for data evaluation. Huber-Humer et al. [77] applied DA to determine methane oxidation efficiency of different materials based on chemical and physical variables. Smidt et al. [66, 76] used DA to differentiate the infrared spectral [76] and thermal patterns [66] of municipal solid waste

conducted by a hierarchical cluster analysis [24].

composting process.

are described in detail.

Discriminant analysis (DA)

toxicity and physico-chemical parameters.

*2.1.3. Supervised pattern recognition* 

CCA is sometimes used in waste management if, for example, microbial communities or vegetation surveys are analysed. CCA was applied by Franke-Whittle et al. [25] and El-Sheikh et al. [73]. Franke-Whittle et al. [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes. El-Sheikh et al. [73] investigated the ten-year primary succession on a newly created landfill at a lagoon of the Mediterranean Sea. Vegetation surveys where the basis for CCA. Kim et al. [74] applied CCA to investigate the vegetation and the soil of a not properly maintained landfill to suggest restoration alternatives by comparing the vegetation of the landfill to the nearby forests.

#### *2.1.2. Unsupervised pattern recognition*

Cluster analysis (CA)

Clustering is the classification of objects into groups called clusters. Objects from the same cluster are more similar to one another than objects from different clusters. The difference of clusters is based on measured distances without any unit. Cluster analysis can be illustrated graphically in a dendrogram as shown in Fig. 2. The samples 2, 3 and 5 are clustered due to the high degree of similarity as well as the samples 1 and 4. The two clusters show little similarity.

**Figure 2.** Example of a cluster analysis visualised by a dendrogram

CA was applied in compost science by Zybtniewskie and Buszewski [1]. They applied CA to conventional compost parameters and NMR data to find out the grouping depending on the composting time. He et al. [56] used a hierarchical cluster analysis to show the similarities and differences of UV-Vis and fluorescence spectra of water extractable organic matter, originating from municipal solid waste that had been subjected to different composting times. A hierarchical cluster analysis was also used by He et al. [22] to investigate waterextractable organic matter during cattle manure composting. Gil et al. [4] displayed dendrograms to illustrate the similarities or differences by application of cattle manure compost to different soils. Bustamante et al. [24] studied physico-chemical, chemical and microbiological parameters of different composts. The evaluation of the composts was conducted by a hierarchical cluster analysis [24].

Lin et al. [82] applied a CA for the selection of optimal recycling methods for food waste.

A stepwise cluster analysis (SCA) was used to describe the nonlinear relationships among state variables and microbial activities of composts by Sun et al. [29]. Sun et al. [30] developed a genetic algorithm aided stepwise cluster analysis (GASCA) to describe the relationships between selected state variables and the C/N ratio in food waste composting.

Furthermore CA has often been used to evaluate microbiological data, especially in compost science [25-28, 31]. Innerebner et al. [26] and Ros et al. [27, 28] used CA to identify related samples and similar groups of microorganisms. Franke-Whittle et al. [25] used CA to show the similarities of Denaturing Gradient Gel Electrophoresis (DGGE) data of three different compost types with proceeding compost maturity. Xiao et al. [31] used a hierarchical cluster analysis of DGGE data to estimate the succession of bacterial communities during the active composting process.

Tesar et al. [75] applied CA to spectral data to illustrate the effect of in-situ aeration of a landfill. Jean and Fruget [72] used CA to compare landfill leachates on the basis of their toxicity and physico-chemical parameters.

### *2.1.3. Supervised pattern recognition*

20 Multivariate Analysis in Management, Engineering and the Sciences

*2.1.2. Unsupervised pattern recognition* 

Cluster analysis (CA)

forests.

similarity.

visualising the diverse habitat preferences of taxa is calculated.

**Figure 2.** Example of a cluster analysis visualised by a dendrogram

ecological data sets. By means of the gradients an ordination diagram describing and

CCA is sometimes used in waste management if, for example, microbial communities or vegetation surveys are analysed. CCA was applied by Franke-Whittle et al. [25] and El-Sheikh et al. [73]. Franke-Whittle et al. [25] applied CCA to illustrate the similarities in microbial communities of three different composting processes. El-Sheikh et al. [73] investigated the ten-year primary succession on a newly created landfill at a lagoon of the Mediterranean Sea. Vegetation surveys where the basis for CCA. Kim et al. [74] applied CCA to investigate the vegetation and the soil of a not properly maintained landfill to suggest restoration alternatives by comparing the vegetation of the landfill to the nearby

Clustering is the classification of objects into groups called clusters. Objects from the same cluster are more similar to one another than objects from different clusters. The difference of clusters is based on measured distances without any unit. Cluster analysis can be illustrated graphically in a dendrogram as shown in Fig. 2. The samples 2, 3 and 5 are clustered due to the high degree of similarity as well as the samples 1 and 4. The two clusters show little

CA was applied in compost science by Zybtniewskie and Buszewski [1]. They applied CA to conventional compost parameters and NMR data to find out the grouping depending on the composting time. He et al. [56] used a hierarchical cluster analysis to show the similarities and differences of UV-Vis and fluorescence spectra of water extractable organic matter, originating from municipal solid waste that had been subjected to different composting times. A hierarchical cluster analysis was also used by He et al. [22] to investigate waterAll supervised methods are classifications. Classification can be considered as a predictive method where the response is a category variable. Different classification methods exist. There are types of "hard" and "soft" modelling. Hard modelling means that a nonrelocatable line between the defined groups exists. One object can only belong to one group. Soft modelling allows an overlapping of the defined classes. An object can belong to both groups [88]. With regard to waste management practice two different classification methods are described in detail.

Discriminant analysis (DA)

DA is a classification method of hard modelling. Campitelli and Ceppi [3] carried out a DA to distinguish between compost and vermicompost on the basis of parameters such as total organic carbon (TOC), germination index (GI), pH, total nitrogen (TN), and water soluble carbon (WSC). Nicolas et al. [9] performed a DA to classify data of an electric nose according to defined exceeded levels of odour. Ecke et al. [71] investigated samples from three different landfill sites by the biochemical methane potential and used DA for data evaluation. Huber-Humer et al. [77] applied DA to determine methane oxidation efficiency of different materials based on chemical and physical variables. Smidt et al. [66, 76] used DA to differentiate the infrared spectral [76] and thermal patterns [66] of municipal solid waste

incinerator (MSWI) bottom ash before and after CO2 uptake. A DA on the CO2 ion current recorded during combustion was applied to illustrate the effect of CO2 treatment of MSWI bottom ash [66]. DA was also used to illustrate the spectral characteristics of leachate from landfill simulation reactors under aerobic and anaerobic conditions [17].

Application of Multivariate Data Analyses in Waste Management 23

MLR to predict the factors associated with medical waste generation at hospitals. Sun et al. [29] used MLR to predict mesophilic and thermopilic bacteria in food waste composts. Suehara and Yano [33] applied MLR to predict conventional compost parameters by NIR

PLS-R is used to find out the fundamental relations between two matrices. PLS-R is a bilinear modelling method. The main idea behind it is to calculate the principal components of the X and the Y matrix separately (external correlation) and to develop a regression model between the scores of the principal components (inner correlation). The concept of PLS-R is

PLS1 is often used to predict time consuming or expensive parameters using an alternative analytical method. Modern analytical tools such as spectroscopic, chromatographic and thermo analytical methods generate data with inherent information on different parameters. With the development of an evaluated prediction model conventional analytical methods

J

Loadings P

\* +

I

I

1

+

PLS model

Errors F

Errors E

> external correlation

J

Many authors have developed such prediction models in compost science. Zvomuya et al. [44] predicted phosphorus availability in soils, amended with composted and noncomposted cattle manure by means of cumulative phosphorus analysis. Fujiwara and

Loadings S

1

can be replaced by easier and/ or faster handling and robust methods.

A

A

U = B \* T

A

\*

Scores T

A

Scores U

**Figure 3.** Principles of PLS-R (according to Esbensen [85])

PCA

inner correlation

PCA

I

I

spectral data.

PLS1

demonstrated in Fig. 3.

J

Data matrix X

I

1

Data matrix Y

I

*2.2.2. Partial Least Squares Regression (PLS-R)* 

Soft independent modelling of class analogy (SIMCA)

SIMCA is a special method of soft modelling recommended by Wold in the 1970s [88]. Objects can belong to one of the defined class, to both classes or to none. Whether SIMCA can be applied on the data set depends on the question to be answered. According to Brereton [88] it is often legitimate in chemistry that an object belongs to more than one class For example a compound may have an ester and an alkene group which are both reflected by an infrared spectrum. Thus they fit in both classes. In natural science it is allowed in most cases for an object to be in line with more than one class simultaneously.

Contrarily in other cases an object can belong only to one class and the application of SIMCA is inappropriate. Brereton [88] gives a good example where the concept of SIMCA is not applicable: A banknote is either forged or not. In many cases there is only one true answer. For such problems SIMCA is not the adequate method.

In compost science Malley et al. [8] and Smidt et al. [12] carried out a SIMCA. Malley et al. [8] classified different decomposition stages of manures by means of near infrared spectroscopy and SIMCA. Smidt et al. [12] carried out a SIMCA to classify different waste materials such as biowaste compost, mechanically-biologically pretreated waste and landfill materials based on their spectroscopic pattern. Smidt et al. [78] used the SIMCA model developed by Smidt et al. [12] to identify different landfill types such as reactor landfill and industrial landfill samples.
