2009
DOI: 10.1016/j.scitotenv.2008.12.033
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Monitoring of the evolution of an industrial compost and prediction of some compost properties by NIR spectroscopy

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Cited by 72 publications
(49 citation statements)
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References 62 publications
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“…On the other hand, from day zero, there was an increase in temperature reaching 39.6 ºC at 15 days of composting, demonstrating the growth and development of mesophilic microorganisms in the biomass. Bacteria, fungi, and mesophilic actinomycetes, which are dominant in the first three days of composting, feed on readily available organic matter components such as sugars, amino acids, proteins and nucleic acids, rapidly causing temperature rise due to the release of heat by part of the microbial metabolism (VERGNOUX et al, 2009). However, the persistence of these microorganisms in the period suggests that the colonization of the biomass by the thermophilic microbiota may have been impaired by the moisture content (62.8%) of the substrate mixture.…”
Section: Resultsmentioning
confidence: 99%
“…On the other hand, from day zero, there was an increase in temperature reaching 39.6 ºC at 15 days of composting, demonstrating the growth and development of mesophilic microorganisms in the biomass. Bacteria, fungi, and mesophilic actinomycetes, which are dominant in the first three days of composting, feed on readily available organic matter components such as sugars, amino acids, proteins and nucleic acids, rapidly causing temperature rise due to the release of heat by part of the microbial metabolism (VERGNOUX et al, 2009). However, the persistence of these microorganisms in the period suggests that the colonization of the biomass by the thermophilic microbiota may have been impaired by the moisture content (62.8%) of the substrate mixture.…”
Section: Resultsmentioning
confidence: 99%
“…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.…”
Section: Pattern Recognition Calibrationmentioning
confidence: 99%
“…Galvez-Sola et al [45] used PLS1 to predict different compost quality parameters such as pH, electric conductivity, total organic matter, total organic carbon, total N, C/N ratio as well as nutrients contents (N, P, K) and potentially pollutant element concentrations (Fe, Cu, Mn and Zn) from near infrared spectra. Vergnoux et al [21] applied a PLS1 to predict physico-chemical and biochemical parameters from NIR spectra. Physico-chemical parameters comprised age, organic carbon, organic nitrogen, C/N, total N, fulvic acids (FA), humic acids (HA) and HA/FA.…”
Section: Partial Least Squares Regression (Pls-r)mentioning
confidence: 99%
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“…Tracking and interpreting the processing stages between start and endpoint is however difficult and fraught with uncertainty [26][27][28][29] even though general requirements and priorities regarding composting principles have emerged [30][31][32]. Of particular interest are practical methods that would allow analyzing the state of fragmentation of polysaccharides undergoing biodegradation.…”
Section: Introductionmentioning
confidence: 99%