Visible and Near-Infrared Calibrations for Quality Assessment of Fresh Phase I and II Mushroom (Agaricus Bisporus) Compost

Abstract

Previous studies have shown that visible and near-infrared spectra (Vis-NIR) of dry and milled compost can be used for generating partial least squares (PLS) calibrations of phase II compost parameters including ammonia, nitrogen dry matter (NDM), dry matter (DM), pH, conductivity, carbon, microbial population, and potential productivity. The objective of this study was to develop robust calibrations for some of the key parameters from the spectra of fresh phase I and II composts. Samples of substrates from six commercial production yards were obtained during winter and summer months of 2000-2004 to monitor changes in quality and were analyzed for the test factors. Vis-NIR reflectance measurements of fresh samples (740) were made over the range of 400-2500 nm. After mathematical pretreatments, PLS calibrations of the key parameters were developed using the NIR (1100-2500 nm) and visible and NIR (400-2500 nm) regions and subsequently validated using an independent sample set of 123 phase I and II samples obtained during 2004-2005. The phase I and II standard errors of laboratory measurements of ammonia, pH, conductivity, DM, NDM, and ash were lower than the standard error of predictions of the same parameters, respectively, by the best NIR or Vis-NIR models. The degree of precision for some of the calibrations, especially ammonia, NDM, and DM, is suitable for composters to monitor changes in quality parameters during production. The laboratory measurement errors for phase I samples were greater than those of the phase II samples, except for ash, due to a higher degree of heterogeneity in the substrate. The calibrations, especially for pH, conductivity, and ash, need to be improved with new sample sets. A major advantage of NIR spectroscopy is the ability to assess substrate quality for a range of target parameters simultaneously, within a few hours of receiving the samples. The main drawbacks are the expensive instrumentation, expertise, and training necessary for operating the spectrometer and a dedicated chemometrician required for maintaining the equations compared to the reference methods.

Application of Multivariate Data Analyses in Waste Management

“…Furthermore they determined humic acid contents by near infrared spectroscopy [41]. Sharma et al [47] developed prediction models for conventional compost parameters, especially ammonia, pH, conductivity, dry matter, nitrogen and ash using NIR and Vis-NIR spectroscopy. Lillhonga et al [23] used PLS-R for compost parameter prediction based on NIR spectra.…”

Quality Assessment of Mushroom Casing Soil Using Visible and near Infrared Spectroscopy

“…all 221 samples of peat and casing were scanned. the scanning protocol that was adopted for this investigation was as described by Sharma et al, 13 with slight modifications to suit the material. Vis-nIr spectra of samples were obtained in the reflectance mode, over a wavelength of 400-2498 nm at 2 nm intervals, set at a band-pass value of 10 nm, with a wavelength accuracy of 0.5 nm.…”

Visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) for rapid measurement of organic matter in compost

“…The history of the technology with compost analysis, however, is more limited, though studies have proven its applicability in assessing microbial populations, nitrogen content, carbon content, pH, and compost salinity (Ben-Dor et al, 1997;Malley et al, 2005;Sharma et al, 2005). Sharma et al (2005) obtained r² values of 0.852 for the correlation between predicted and measured values for quantification of the percentage of ash.…”

“…Studies utilizing VisNIR DRS in the fields of agriculture or waste management have shown the technology's ability to identify organic and inorganic soil carbon for soil characterization (Brown et al, 2006;Morgan et al, 2009), clay mineralogic composition (Waiser et al, 2007), and in quantifying soil environmental contaminants, such as hydrocarbons (Chakraborty et al, 2010). The history of the technology with compost analysis, however, is more limited, though studies have proven its applicability in assessing microbial populations, nitrogen content, carbon content, pH, and compost salinity (Ben-Dor et al, 1997;Malley et al, 2005;Sharma et al, 2005). Sharma et al (2005) obtained r² values of 0.852 for the correlation between predicted and measured values for quantification of the percentage of ash.…”

“…Ben-Dor et al (1997) were focused on monitoring the material's composition and spectral changes throughout the composting process instead of analyzing the material once it had reached a finished state. Malley et al (2005) obtained useful calibration data for total carbon, organic carbon, total nitrogen, carbon:nitrogen, sulfur, potassium, and pH, and Sharma et al (2005) for pH, salinity, and percentage of ash. However, their studies were confined by studying only milled and dried samples from specific feedstocks and composting methods.…”