*3.1.1. Objective of the study*

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 analysis methods.

Application of Multivariate Data Analyses in Waste Management 27

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].

n Mean Range Outliers

Age [d] 98 183.6 82.0 - 268.0 6 16.7 0.82 Corg content [%] 97 26.0 16.4 - 41.5 5 2.32 0.77 Nt content [%] 97 1.4 1.0 - 2.1 4 0.11 0.67 C:N ratio 97 18.2 12.2 - 29.1 4 1.51 0.71 Cmic [μg g-1] 98 4986 774 - 8587 5 954 0.68 Cmic:Corg [mgCmicgCorg-1] 97 18.6 4.0 - 29.4 4 4.00 0.63 Basal respiration [μg C g-1 d-1] 47 574.8 252.0 - 966.0 2 49.2 0.88 qCO2 [μgCO2-C mg Cmic-1 d-1] 47 9.7 4.2 - 17.1 1 1.98 0.83

Suppression 5‰ (rating) [%] 98 57.3 8.0 - 101.0 2 19.3 0.71 Suppression 5‰ (fresh weight) [%] 98 59.1 14.0 - 103.0 3 18.7 0.47 **Table 2.** Excerpt of table 1 and 2 by Michel et al. [42], SECV = standard error of cross-validation, r2 = the

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

removed

98 517.9 256.0 - 879.0 5 74.7 0.75

98 118.7 48.6 - 370.9 6 48.6 0.49

SECV r2

*3.2.2. Method of evaluation and results* 

Hydrolysis of fluorescein diacetate

Specific enzyme activity [μgFDA

be capable of monitoring compost quality.

(FDA-HR) [μg g-1h-1]

coefficient of determination

*3.2.3. Conclusion* 

mgCmic-1h-1]
