David North Plant Research Centre, BSES Limited, 50 Meiers Road, Indooroopilly, Queensland 4068, Australia.
Appl Spectrosc. 2013 Oct;67(10):1160-4. doi: 10.1366/13-07003.
Breeding energy cane for cellulosic biofuel production involves manipulating various traits. An important trait to optimize is cell wall degradability as defined by enzymatic hydrolysis. We investigated the feasibility of using near-infrared spectroscopy (NIRS) combined with multivariate calibration to predict energy cane cell wall digestibility based upon fiber samples from a range of sugarcane genotypes and related species. These samples produced digestibility values ranging between 6 and 31%. To preserve the practicality of the technique, spectra obtained from crudely prepared samples were used. Various spectral pre-processing methods were tested, with the best NIRS calibration obtained from second derivative, orthogonal signal-corrected spectra. Model performance was evaluated by cross-validation and independent validation. Large differences between the performance results from the two validation approaches indicated that the model was sensitive to the choice of test data. This may be remedied by using a larger calibration training set containing diverse sample types. The best result was obtained through independent validation which produced a R(2) value of 0.86, a root mean squared error of prediction (RMSEP) of 1.59, and a ratio of prediction to deviation (RPD) of 2.7. This study has demonstrated that it is feasible and practical to use NIRS to predict energy cane cell wall digestibility.
培育用于纤维素生物燃料生产的能源甘蔗涉及到对各种特性的操纵。需要优化的一个重要特性是细胞壁的可降解性，这是通过酶水解来定义的。我们研究了使用近红外光谱（NIRS）结合多元校准来预测能源甘蔗细胞壁消化率的可行性，其基础是来自一系列甘蔗基因型和相关物种的纤维样本。这些样本的消化率值在 6%到 31%之间。为了保持该技术的实用性，我们使用了从粗制样品中获得的光谱。测试了各种光谱预处理方法，其中最好的 NIRS 校准结果来自二阶导数、正交信号校正的光谱。通过交叉验证和独立验证来评估模型性能。两种验证方法的性能结果存在较大差异，表明模型对测试数据的选择很敏感。通过使用包含多种样本类型的更大校准训练集，可能会纠正这种情况。通过独立验证得到了最佳结果，其 R(2) 值为 0.86，预测均方根误差（RMSEP）为 1.59，预测偏差比（RPD）为 2.7。本研究表明，使用 NIRS 预测能源甘蔗细胞壁消化率是可行且实用的。
