A new extended method for separating, e.g., scattering from absorbance in spectroscopic measurements, extended inverted signal correction (EISC), is presented and compared to multiplicative signal correction (MSC) and existing modifications of this. EISC preprocessing is applied to near-infrared transmittance (NIT) spectra of single wheat kernels with the aim of improving the multivariate calibration for protein content by partial least-squares regression (PLSR). The primary justification of the EISC method is to facilitate removal of spectral artifacts and interferences that are uncorrelated to target analyte concentration. In this study, EISC is applied in a general form, including additive terms, multiplicative terms, wavelength dependency of the light scatter coefficient, and simple polynomial terms. It is compared to conventional MSC and derivative methods for spectral preprocessing. Performance of the EISC was found to be comparable to a more complex dual-transformation model obtained by first calculating the second derivative NIT spectra followed by MSC. The calibration model based on EISC preprocessing performed better than models based on the raw data, second derivatives, MSC, and MSC followed by second derivatives.

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Cereal Chem. 80(3):274-280The development of nondestructive screening methods for single seed protein, vitreousness, density, and hardness index has been studied for single kernels of European wheat. A single kernel procedure was applied involving, image analysis, near-infrared transmittance (NIT) spectroscopy, laboratory density determination, single kernel characterization system (SKCS), and finally Kjeldahl protein determination on the crushed single kernels. Single kernel NIT spectroscopy showed excellent ability to determine protein content, and some ability for determination of single kernel vitreousness. Nondestructive determination of single kernel density, either based on NIT spectroscopy or based on image analysis and kernel weight, needs to be further improved for practical use. The use of SKCS hardness index as a true single kernel hardness reference in a NIT prediction model resulted in a poor predictability. However, by applying an averaging approach, in which single seed replicate measurements are mathematically simulated, a very good NIT prediction model was achieved. This suggests that the single seed NIT spectra contain hardness information, but that a single seed hardness method with higher accuracy is needed to achieve a good NIT prediction model for single kernel hardness.

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