# 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
[78] used FT-NIR in a wavelength range of 1000-2500 nm and PLS modeling for the prediction of total carbon, total nitrogen, and the C/N ratio for monitoring cow manure composting. The calibration models assessed the right potassium, phosphorus, and other mineral contents of the test composts in terms of received precision. These findings suggested that NIR spectroscopy was a helpful instrument for managing compost quality. [PERSON] et al. [79] used FT-IR spectroscopy to investigate the cocomposting process of sewage waste, wood fragments, and mature compost. The aim of their study was to determine the characteristic IR absorption bands or band growth rates that correlate with the compost maturity/degradation degree, while [PERSON] et al. [80] used FT-IR to compare the quality, maturity, and nutritional levels of vermicompost and compost produced from _Eichlornia_. The potential for NIRS as a high throughput analytical method was also evaluated for the detection and quantification of microplastics in compost samples [81] and for the prediction of heavy metal content in compost [82]. Regarding the microplastic analysis in compost, the authors showed that partial least squares regression models (Figure 4), after applying a smooth first derivative of the raw data followed by normalization, could identify and quantify the extracted microplastics in a mass range of 1-10 mg. The concentrations of chosen heavy metals (Cr, As, Cd, Cu, Zn, and Pb) in compost containing pig and chicken feces were analyzed. The second derivative outperformed the log 1/R and the first derivative treatments in the research. The modified partial least squares calibration model projected a correlation between NIRS expected values and chemical analysis values better than the PLS or PCR methods. All previously presented examples show the significant potential of spectroscopy methods for monitoring process and for the analysis of compost chemical composition. The most important advantages of using spectroscopy techniques, especially NIRS, are the possibility to perform analyses in situ and online for a large number of samples per minute without interfering with the process and without the use of chemicals, and this makes this method very appealing for application on a lab scale as well as in large-scale processes. Other procedures require more time, and, in most cases, a variety of methodology must be examined, making it difficult to accurately evaluate compost quality, supporting the discovery of novel approaches for appropriate composite assessment. Spectroscopic methods are considered a rapid on-site measurement system required for the quality control of the compost production process. Some of the examples describing the application of NIR spectroscopy for monitoring the composting process are given in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Type of Composting** & **Composting Material** & \begin{tabular}{c} **Spectroscopic Method** \\ **and Chemometrics** \\ \end{tabular}  & **Specific Results** & **Reference** \\ \hline \multirow{6}{*}{In pile composting.} & \multirow{3}{*}{ \begin{tabular}{c} Compost made from \\ wheat stalks and \\ poultry waste; \\ microbial and \\ biochemical properties \\ analysis. \\ \end{tabular}  } & UV-Vis and NIR & \begin{tabular}{c} The best calibrations could be \\ developed using the 1100–2498 nm \\ segment. NIR calibration equations \\ for predicting ash, carbon, \\ thermophilic population, and fiber \\ with PLS regression. \\ \end{tabular}  & \multirow{3}{*}{[83]} \\  & \multirow{3}{*}{ \begin{tabular}{c} 120 animal manure \\ compost samples from \\ 22 provinces in China; \\ compost compositions \\ analysis (moisture, \\ volatile solid, total \\ organic carbon, total \\ nitrogen, carbon to \\ nitrogen ratio, pH, and \\ conductivity). \\ \end{tabular}  } & UV-Vis and NIR &