Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The maize cob biomass is one of important biomass crops in Thailand. Nowadays, the use of the biomass as renewable resource is increasing, especially residue agriculture waste. As we know that the biomass properties impact combustion, in order to achieve boiler efficiency, its energy characteristics of biomass was required immediately before burning. This work uses the FT-near infrared spectroscopy to estimate gross calorific value (GCV) of maize cob as the rapid method. Each sample was scanned using diffuse reflectance mode at a wavenumber range between 12500-3600 cm-1. The scanning was done with a resolution of 8 cm-1 and completed 32 scans per sample, then averaged to be one spectrum. The results showed that this technique could decrease a processing time to 1-2 minutes per sample to determine GCV whereas alternatively the current method used a processing time of 25-30 minutes per sample. The capacity of the model gave root mean square error of cross validation (RMSECV) of 91.1 Jg-1, which was low. Hence, the model was acceptable and cloud be used for screening.

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Analysis of Energy Traits of Populus spp. Clones by Near-Infrared Spectroscopy

Journal of Biobased Materials and Bioenergy ◽

10.1166/jbmb.2007.1989 ◽

2007 ◽

Vol 1 (1) ◽

pp. 155-162 ◽

Cited By ~ 5

Author(s):

Melchor C. Maranan ◽

Marie-Pierre G. Laborie

Keyword(s):

Infrared Spectroscopy ◽

Specific Gravity ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Hybrid Poplar ◽

Calorific Value ◽

Validation Dataset ◽

Hybrid Poplars ◽

Lower Accuracy ◽

Calibration Models

The application of near-infrared spectroscopy (NIRS) and multivariate analysis for determining the calorific value and specific gravity of Populus spp. clones was assessed. Projection to latent structure (PLS) models of calorific value and specific gravity were developed from NIR original spectra and also from the first and second spectral derivatives. The best calibration models were built from the NIR second spectral derivative with good calibration statistics for both calorific value (r = 0 97, RMSEC = 0 05 kJ/g) and specific gravity (r = 0 98; RMSEC = 0 005). The calibration models from the NIR first spectral derivative were also good for specific gravity (r = 0 92, RMSEC = 0 010) and moderate for calorific value (r = 0 82; RMSEC = 0 11 kJ/g). When evaluated on a validation dataset, the models from the NIR first spectral derivative performed best for both specific gravity (r = 0 84; RMSEP = 0 021) and calorific value (r = 0 81; RMSEP = 0 13 kJ/g). In both cases, the standard errors of prediction (SEP) obtained from the NIRS calibration models were less than twice those of the corresponding laboratory measurement. The NIRS models were therefore useful for quickly determining calorific value and specific gravity of hybrid poplars but with a lower accuracy than the corresponding laboratory measurements. The study also helped delineate parentage as a factor of choice for manipulating wood specific gravity and thus biomass yield in hybrid poplars. On the other hand, calorific value was uniform within the population evaluated, indicating that little improvement in calorific value can be expected from selecting for it in hybrid poplar programs.

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