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Lignin composition [syringyl/guaiacyl (S/G) ratio] and cellulose content in wood have an important bearing on pulp yield. This paper deals with the development of a calibration model for S/G ratio using five Eucalyptus species from different sites by diffuse reflectance near infrared spectroscopy. The model was constructed with 120 samples covering an S/G ratio range from 1.8 to 3.6, determined by pyrolysis-gas chromatography-mass spectrometry. The calibration plot has an R 2 value of 0.825 which was validated in E. camaldulensis, E. urophylla and E. pellita. Variation in S/G ratio was studied in more than 3000 E. camaldulensis trees across three diverse sites in southern India. The S/G ratio was lower in a low rainfall site (Mahabubnagar) compared to a higher rainfall site (Ongole). A positive correlation (R 2 = 0.72) was observed between S/G ratio and Kraft pulp yield in E. camaldulensis. Alkali consumption in Kraft pulping experiments was inversely proportional to the S/G ratio (R 2 = 0.914).

Measurement of pulpwood traits from a standing tree has considerable advantage when screening large populations for tree selection. It reduces time and also eliminates requirements of transport, powdering, and storing the sample. This study describes estimation of Kraft pulp yield (KPY) in Eucalyptus camaldulensis, E. urophylla, Leucaena leucocephala, and Casuarina junghuhniana by portable NIR spectroscopy of standing trees. Calibration models were developed for KPY estimation using portable NIR spectroscopy for the four species, along with a calibration model for syringyl/guaiacyl (S/G) ratio in E. camaldulensis. The calibration models for KPY showed R2 values ranging from 0.93 ( E. camaldulensis) to 0.83 ( L. leucocephala), and 0.95 for S/G ratio. The developed calibration models for E. camaldulensis and L. leucocephala were compared with laboratory NIR models, and a variation of <±2.0% was found between both methods. The models were validated by both external and cross validation which showed <2.0% RMSEP (root mean square error of prediction) and <2.0% RMECV (root mean square error of cross validation) in external and cross validations, respectively.

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