Abstract

Composting is one of the most desirable techniques for reducing waste volume. To make good compost, the correct proportions of the elements carbon and nitrogen (30: 1 ratio) are important. In this paper, carbon quantification of green grass tissue using near infrared (NIR) technology was studied. Separate studies were conducted for the short-wavelength region (SWR = 700–1100 nm, a range that includes part of the visible spectrum) and long-wavelength region (LWR = 1100–2500 nm). Several spectral pretreatments (such as SNV, derivatives etc.) were implemented to optimise the stepwise multiple linear regression (SMLR) and partial least squares (PLS) calibrations. PLS analysis was conducted for all pretreatments. Results showed that the 2^nd derivative of standard normal variate (SNV) pretreatment for the LWR and the SNV pretreatment for the SWR gave the best predictions. To simplify the PLS models, a weight index (WI), was defined as the absolute value of product between the regression vector from PLS analysis and the average spectrum. A simple PLS calibration was developed using selected peak wavelengths of regression vector with a minimum WI. The simple PLS models gave better results than the full PLS calibrations. According to this analysis, the C–H stretching of the first overtone at 1860 nm and the C–H stretching of the third overtone at 874 nm were the key bands for the SWR and LWR, respectively. SMLR analysis was performed on the same spectral data used in the PLS analysis. SMLR calibrations were developed using the key band chosen in PLS analysis. Although the performance of the calibrations were not as good as the PLS calibrations, the SMLR model produced acceptable calibrations for both the SWR and LWR. The simple fact that NIR technology can be used to determine both carbon and nitrogen very quickly makes it an ideal technology for monitoring material going into a composting operation.

© 2003 NIR Publications

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