The use of visible (VIS) and near infrared (NIR) reflectance spectroscopy to predict fibre diameter in both clean and greasy wool samples

Visible (VIS) and near infrared (NIR) reflectance spectroscopy combined with multivariate data analysis were explored to predict fibre diameter in both clean and greasy Merino wool samples. Fifty clean and 400 greasy wool samples were analysed. Samples were scanned in a large cuvette using a NIRSystems 6500 monochromator instrument by reflectance in the VIS and NIR regions (400 to 2500 nm). Partial least square (PLS) regression was used to develop a number of calibration models between the spectral and reference data. Different mathematical treatments were used during model development. Cross validation was used to assess the performance and avoid overfitting of the models. The NIR calibration models gave a coefficient of determination in calibration (R-2) > 0.90 for clean wool samples and a R-2 < 0.50 for greasy wool samples. The values for the residual predictive value, PPD, (ratio of standard deviation (s.d.) to the root mean square of the standard error of cross validation (RMSECV)) were 3 for clean and 0.6 for greasy wool samples, respectively. The results indicated that fibre diameter in greasy wool samples was poorly predicted with NIR, while clean wool showed good relationships. More research is required to improve the calibration on greasy wool samples if the technology is to be used for rapid analysis to assist in the selection of animals in breeding programmes.