# Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes
## 5 Use of NIR Spectroscopy to Monitor and/or Control the Composing Process
The humic acid model findings showed an increase in the performance of the PLSR models at gather wavelengths (700-1000 nm), indicating the necessity of NIR spectra fingerprint detection in calibration model development. [PERSON] et al. [95] used FTIR spectroscopy and thermal analysis to describe compost tea liquid preparations and compare the contents to that of the waste used in their manufacture. The application of these methods to compost extracts revealed information about the molecular structure of the extracts. It allowed for the determination of which components had been extracted primarily and/or which biological and chemical changes had occurred. The humification of organic matter during composting was studied by [PERSON] et al. [96]. The content of humic and fulvic acid evaluation during 146 days of the composting of digested sewage sludge, green waste, and pine bark at a ratio of 1:1:1 was correlated with UV spectra and with NIR spectra. The statistical analyses of the results showed that NIRS presents some advantages over UV. Thus, the predictions concerning  
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline  
humic acid contents, which can characterize the maturity of compost, are more accurate with NIRS, in particular for fulvic acid and the humic acid/fulvic acid ratio. Similarly, [PERSON] [97] evaluated the applicability of NIR spectroscopy to predict the stability parameters of sewage sludge and compost derived from sludge. The results showed that developed PLS models were similarly efficient for the prediction of water-soluble carbon content in sewage sludge and sewage sludge compost samples. Furthermore, their results showed that PLS models based on NIR spectra are also very efficient for the prediction of compost age as well for the prediction of microbial reparation during the composting process expressed as CO\({}_{2}\). [PERSON] et al. [98] compared and evaluated the performance of mid-infrared spectroscopy and near-infrared spectroscopy using an integrating sphere and a fiber probe for the prediction of humic acid content in compost samples. As a result, practical considerations may be the deciding element in selecting one of these methods. While Fourier transform infrared spectroscopy (FT-IR) is an excellent laboratory technique that requires some sample preparation time, NIR is favored for online measurements and industrial process control because it is faster and does not require any extra sample preparation. NIR readings with the fiber probe produce the fastest outcomes, but due to the smaller measurement area, three samples are needed, which cannot compete with the benefit of the integrating sphere technique's bigger measurement area. [PERSON] et al. [99] studied NIR spectroscopy to analyze quantitatively the organic matter content, total nitrogen content, and carbon nitrogen ratio in compost prepared using two different composting procedures (open composting and semienclosed high-temperature composting). Spectra were gathered in the wavelength range of 350-1700 nm. The authors described the calibration procedure in detail. Firstly, 13 methods, such as smoothing, spectral enhancement, baseline correction, normalization, mean centering, differentiation, and simple mathematical transformation, were assessed to preprocess the spectral data. Secondly, the characteristic bands of the components were estimated using the competitive adaptive reweighted sampling (CARS) method, stepwise regression (SR) method, successive projection algorithm (SPA), and synergy interval partial least squares (siPLS). In the third step, PLS models were evaluated. Their results showed that various NIRS feature variable selection and spectra modification techniques enhanced the prediction ability and modeling. The NIRS method must be fitted to each element using particular spectrum pretreatment to obtain acceptable prediction precision.