Near infrared reflectance spectroscopy (NIRS) to predict biological parameters of maize silage: effects of particle comminution, oven drying temperature and the presence of residual moisture

Comparative near Infrared (NIR) spectroscopy calibrations performance of dried and undried forage on dry and wet matter bases

The application of Near Infrared (NIR) spectroscopy for analyzing wet feed directly on farms is increasingly recognized for its role in supporting harvest-time decisions and refining the precision of animal feeding practices. This study aims to evaluate the accuracy of NIR spectroscopy calibrations for both undried, unprocessed samples and dried, ground samples. Additionally, it investigates the influence of the bases of reference data (wet vs. dry basis) on the predictive capabilities of the NIR analysis. The study utilized 492 Corn Whole Plant (CWP) and 405 High Moisture Corn (HMC) samples, sourced from various farms across Italy. Spectral data were acquired from both undried, unground and dried, ground samples using laboratory bench NIR instruments, covering a spectral range of 1100 to 2498 nm. The reference chemical composition of these samples was analyzed and presented in two formats: on a wet matter basis and on a dry matter basis. The study revealed that calibrations based on undried samples generally exhibited lower predictive accuracy for most traits, with the exception of Dry Matter (DM). Notably, the decline in predictive performance was more pronounced in highly moist products like CWP, where the average error increased by 60-70%. Conversely, this reduction in accuracy was relatively contained (10-15%) in drier samples such as HMC. The Standard Error of Cross-Validation (SECV) values for DMres, Ash, CP, and EE were notably low, at 0.39, 0.30, 0.29, 0.21% for CWP and 0.49, 0.14, 0.25, 0.14% for HMC, respectively. These results align with previous studies, indicating the reliability of NIR spectroscopy in diverse moisture contexts. The study attributes this variance to the interference caused by water in 'as is' samples, where the spectral features predominantly reflect water content, thereby obscuring the spectral signatures of other nutrients. In terms of calibration development strategies, the study concludes that there is no significant difference in predictive performance between undried calibrations based on either 'dry matter' or 'as is' basis. This finding emphasizes the potential of NIR spectroscopy in diverse moisture contexts, although with varying degrees of accuracy contingent upon the moisture content of the analyzed samples. Overall, this research provides valuable insights into the calibration strategies of NIR spectroscopy and its practical applications in agricultural settings, particularly for on-farm forage analysis.

Near-infrared spectroscopy (NIRS) for a real time monitoring of the biogas process

In this research project Near-infrared spectroscopy (NIRS) was applied to monitor the content of specific process parameters in anaerobic digestion. A laboratory scaled biogas digester was constantly fed every four hours with maize- and grass silage to keep a base load with an organic loading rate (OLR) of 2.5 kg oDM/m³ ∗ d. Daily impact loads with shredded wheat up to an OLR of 8 kg oDM/m³ ∗ d were added in order to generate peaks at the parameters tested. The developed calibration models are capable to show changes in process parameters like volatile fatty acids (VFA), propionic acid, total inorganic carbon (TIC) and the ratio of the volatile fatty acids to the carbonate buffer (VFA/TIC). Based on the calibration of the models for VFA and TIC, the values could be predicted with an R² of 0.94 and 0.97, respectively. Moreover, the residual prediction deviations were 4.0 and 6.0 for VFA and TIC, respectively.

Use of near infrared spectroscopy in online-monitoring of feeding substrate quality in anaerobic digestion

In order to keep the anaerobic process stably and uniformly producing biogas it needs to be supplied with either an even amount of substrate of stable quality or varying amounts according to variations in quality. Feeding amounts are usually adjusted manually as a reaction to changing rates of biogas production. Continuous information about the actual substrate quality is not available and feedstuff analyses are costly. Aim of this study was to assess the feasibility of near infrared spectroscopic (NIRS) online monitoring of substrate quality in order to find ways towards more exact control of biogas plant feeding. A NIRS sensor system was designed, constructed and calibrated for continuous monitoring of (RMSECV in brackets) dry matter (DM) (0.75%fresh matter (FM)), volatile solids (0.74%FM), crude fat (0.09%FM), crude protein (0.22%FM), crude fiber (1.50%DM) and nitrogen-free extracts (0.93%FM) of maize silage.