Quality Estimation of Agave Tequilana Leaf for Bioethanol Production

Abstract

Agave tequilana is a potential biofuel crop, for which the characters of juice total soluble sugar content (TSS), dry matter content (DM), cellulose, hemicellulose and lignin content are quality criteria. Spectra of leaves were obtained using a hand-held silicon photodiode array (Si PDA)-based spectrometer with a wavelength range of 300–1100 nm and an InGaAs-based Fourier transform near infrared (FT-NIR) spectrometer with a wavelength range of 1100–2500 nm. Fresh leaves were harvested at different maturity stages, in different seasons and from two locations in Queensland during 2012–2014. Partial least square regression models were developed for DM and TSS of fresh leaf, and for cellulose, hemicellulose and lignin of dried material, with models tested on populations of independent samples collected in different years, seasons and locations. Prediction statistics for DM of fresh leaf using the Si PDA spectrometer (729–975 nm) were r² = 0.49–0.87 and root mean square error of prediction (RMSEP) = 2.36–1.44%, while with the use of the FT-NIR spectrometer, the prediction statistics were r² = 0.53–0.66 and RMSEP = 2.63–2.18% (across different years, seasons and locations). Prediction statistics for TSS in fresh leaf using the Si PDA spectrometer (729–975 nm) were r² = 0.53–0.69 and RMSEP = 1.70–1.91%, with poorer results obtained using the FT-NIR spectrometer (r² = 0.33–0.56; RMSEP = 1.88–2.45%). With increased sample diversity in the calibration set, NIR technology is recommended for estimation of DM and TSS in fresh Agave leaves. FT-NIR-based prediction of cellulose, hemicellulose or lignin of independent sets (of different years or cultivars) was unsatisfactory, with r² < 0.75 and bias >10% of mean. These results may be improved with increased sample range, and attention to laboratory (reference method) error. However, leaf cellulose and hemicellulose content may be more easily estimated through correlation to leaf DM level (R² of 0.77 across all sampling events).