Charles Hurburgh

Near Infrared Spectroscopy (NIRS) analysis at the single seed level is a useful tool for breeders, farmers, feeding facilities, and food companies according to current researches. As a non-destructive technique, NIRS allows for the selection and classification of seeds according to specific traits and attributes without alteration of their properties. Critical aspects in using NIRS for single seed analysis such as reference method, sample morphology, and spectrometer suitability are discussed in this review. A summary of current applications of NIRS technologies at single seed level is also presented.

ABSTRACT Soil testing requires the analysis of large numbers of samples in the laboratory that is often time consuming and expensive Mid-infrared spectroscopy (mid-IR) and near infrared (NIR) spectroscopy are fast, non-destructive and inexpensive analytical methods that have been used for soil analysis, in the laboratory and in the field, to reduce the need for measurements using complex chemical/physical analyses A comparison of the use of spectral pretreatment as well as the implementation of linear and non-linear regression methods was performed This study presents an overview of the use of infrared spectroscopy for the prediction of five physical (sand, silt and clay) and chemical (total carbon and total nitrogen) soil parameters with near and mid-infrared units in bench top and field set-ups Even though no significant differences existed among pretreatment methods, models using second derivatives performed better. The implementation of partial least squares (PLS), least squares support vector machines (LS-SVM) and locally weighted regression (LWR) for the development of the calibration models showed that the LS-SVM did not out-perform linear methods for most components while LWR that creates simpler models performed well The present results tend to show that soil models are quite sensitive to the complexity of the model The ability of LWR to select only the appropriate samples did help in the development of robust models. Results also proved that field units performed as well as bench-top instruments This was true for both near infrared and mid-infrared technology. Finally, analysis of field moist samples was not as satisfactory as using dried-ground samples regardless of the chemometrics methods applied.

ABSTRACT The modification of frequency components (Fourier coefficients and wavelet detail component) of near infrared spectra for the optimisation of calibration and standardisation processes was investigated. High-frequency components were smoothed and approximated to remove components most likely to represent noise and background information. Savitzky-Golay smoothing and signal correction were used for that purpose. Frequency modification methods were used in addition to wavelength domain processing techniques. Whole soybean protein and oil calibrations were developed on four instruments with their own calibration set (two Foss Infratecs and two Bruins OmegAnalyzerGs). A validation strategy with two sample sets of known and of new variability was implemented. Frequency modification methods showed improvements of the prediction precision in calibration (relative predictive determinant (RPD) increased from 8.57 to 9.25 for protein and from 7.01 to 7.28 for oil with Fourier coefficients-based smoothing for Infratec 124103501 Frequency based pre-processing methods were also successful when transferring prediction models in intra and inter-brand situations (RPD of the secondary unit of 9.21 compared to original RPD of 8.45 in intra-brand for Foss network for protein; RPD of secondary unit of 9.33 compared to original RPD of 8.74 for inter-brand scenario with Foss Infratec 1241 master of Bruins units for protein). The smoothing of Fourier coefficients showed the best results. Prediction accuracies were not modified by the frequency-based modifications, except in the inter-brand scenario. An appropriate pre-processing limited the need for other standardisation methods except in inter-brand situations where a bias correction should be implemented. Frequency-based pre-treatment methods tend to specialise the calibration set to optimise predictions. This may not be suitable when the variability of the future samples is not included in the calibration set (i.e. yearly variability of agricultural products).

... reduction of near infrared spectral data using global and local implementations of principal component analysis for neural network calibrations Igor V. Kovalenko," Glen R. Rippke and Charles R. Hurburgh Department ofAgricultural and Biosystems Engineering, iov;a State ...