Near Infrared Reflectance Calibration Optimisation to Predict Lignocellulosic Compounds in Sugarcane Samples with Coarse Particle Size

Abstract

Frequent variations in spectral intensity due to particle size and/or of particle size distribution are observed in plant products processed in powder form and scanned with near infrared reflectance (NIR). In this study, two grinders, with differences in time consumption, practicality and providing homogenates with different particle size range and distribution, were tested to evaluate their effects on NIR spectra. Optimisation of NIR calibration was necessary before predicting lignocellulosic compounds in sugarcane ( Saccharum spp.) samples with coarse particle size to supply a pre-existing ecophysiological growth model. Sixty samples from three varieties, grown in four contrasting pedoclimatic areas and from five anatomical parts were scanned and then analysed by biochemical fractionation. Different calibration methods, resulting in a combination of multiple linear regressions (MLR) applied to three calibration sets (fine, coarse and mixed particle sizes) treated with six data pretreatments—first derivative (D), second derivative (D2), multiplicative scatter correction (MSC), standard normal variate and detrend (SNVD), standard normal variate and detrend successively followed by first derivative (SNVD-D) or second derivative (SNVD-D2)—were investigated. The best NIR model statistical values were obtained by calibration developed on a mixed calibration set treated by SNVD-D2. Results confirmed that NIR spectroscopy could be an accurate and efficient method to predict lignocellulosic compounds in different botanical parts of sugarcane samples when used as input to an ecophysiological growth model.