Application of Band Optimization of Near-infrared Spectra for Quantitative Detection of Proteins in Northeastern Pine Nuts
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Abstract:
Near-infrared (NIR) spectroscopy was performed to develop a fast, nondestructive, and simple method to test the quality of Northeastern pine nuts. Using shelled and deshelled pine nuts, quantitative analysis models of proteins in the nuts were established using partial least squares (PLS) and the models were optimized by derivation, multiplicative scatter correction (MSC), standard normal variate (SNV), and vector normalization pretreatment. Backward interval partial least squares (BiPLS) and elimination of uninformative variables (UVE) were used to select characteristic bands to establish PLS protein prediction models with full wavelength and characteristic bands. The results showed that the models established after preprocessing with vector normalization and SNV exhibited optimal performance for deshelled and shelled pine nuts, respectively. The models were optimized by band selection and the optimum screening result was presented using BiPLS. The correlation coefficients (RC) of calibration subset of the protein models for deshelled and shelled pine nuts were 0.9056 and 0.9383, respectively. The root-mean-square error (RMSE) values of the validation subset were 0.6670 and 0.5761, respectively. Therefore, after optimization, the model prediction performance was improved, thus providing a reference point for online testing of proteins in deshelled and shelled pine nuts.
