Abstract: Biomass resources, including crop straw and livestock manure, can usually serve as advantageous raw materials to produce organic fertilizer. The utilization of these resources can be achieved in aerobic composting technology. Currently, trough composting is the main large-scale composting technology in China, due to its large processing capacity, low investment cost, and short composting cycle. As a new type of composting technology, membrane-covered composting refers to a semi-permeable membrane to cover the surface of the fermentation trough. Much attention has gained due to its high efficiency, adaptability, energy saving, easy operation, and reduction of greenhouse gas. However, the composting is normally associated with the complex physical and chemical changes under the action of microorganisms, particularly when affected by some process parameters, such as temperature, moisture content (MC), organic matter content (OM), and carbon-nitrogen (C/N) ratio. Specifically, the sample complexity varied in different technologies during composting process. It is necessary to rapidly detect the processing parameters in real time during the whole composting process, in order to fully optimize composting process for the composting quality. Near infrared spectroscopy (NIRS) can serve as a promising analytical technology in this case. However, most studies focused on a specific model for a certain composting technology. Since a general model suitable for different composting technologies was built using partial least squares (PLS) method, it is inevitable to bring some problems, such as the number increase of latent variables, model overfitting, and low prediction accuracy. Local PLS algorithm can be expected to save calculation time and improve the accuracy of the models. This study aims to dynamic analyze composting parameters in real-time for various composting technologies using FT-NIR spectroscopy combined with Local PLS method. Dairy manure and corn stalks were used as raw materials for the large-scale trough and membrane-covered aerobic composting. 100 samples were collected for each composting technology. The key physicochemical parameters were analyzed, such as MC, OM, and C/N ratio, during the composting process. A FT-NIR spectrometer was used to obtain the infrared spectra of samples. Local PLS algorithm was used to establish the universal rapid measurement models of processing parameters during the whole composting process in two composting techniques. The results showed that: 1) The changes of key parameters in the whole composting process varied greatly in an individual trough or membrane-covered composting, indicating significant variation in the processing (P<0.05); 2) The established Local PLS model demonstrated, excellent prediction for the MC with the R2P value of 0.95, RPD value of 4.47, and RSD value of 3.37%, as well approximate quantitative prediction for the OM and C/N ratio with the R2P value of 0.74 and 0.77, RPD value above 1.5, and RSD less than 10%. NIR-prediction has also a good agreement with the measured in the change trends during the composting processing. The proposed algorithm can provide a promising potential to the real-time dynamic analysis of key parameters in the large-scale trough and membrane-covered composting process.