1. College of Life Sciences，China Jiliang University，Hangzhou 310018，China
2. Institute of Food Science，Zhejiang Academy of Agricultural Sciences，Key Laboratory of Post-Harvest Handing of Fruits，Ministry of Agriculture，Key Laboratory of Fruits and Vegetables Postharvest and Processing Technology Research of Zhejiang Province，Hangzhou 310021，China
Abstract：To improve the development and utilization of blueberry pomace, the test measured the feasibility of near-infrared spectroscopy for the determination of anthocyanins in blueberry pomace of the three species which includes Northland, Bluebeauty No.1 and Brightwell. We gathered the near-infrared spectroscopy data of three blueberry pomaces through DA7200 and eliminated 1, 4 and 8 abnormal samples of Northland, Bluebeauty No. 1 and Brightwell respectively by principal component analysis-Mahalanobis distance. The K-S was used to divide the sample set into correction set (686 samples) and verification set (171 samples). Normalization, standardized normal variate (SNV), multivariate scattering correction (MSC), Norris first derivative (NFD), Norris second derivative (NSD), SG convolution first derivative (SGCFD), SG convolution second derivative (SGCSD), Savitzky-Golay (SG) convolution smoothing and orthogonal signal correction preprocess were performed on the sample set respectively, and the full spectrum PLS model was built accordingly. Preprocess methods with sequential combinations of MSC, SGCSD, SG convolution smoothing and orthogonal signal correction were compared. The results showed that the optimal preprocess method in the full spectrum PLS model was orthogonal signal correction+SGCSD+SG convolution smoothing, with R2c as 0.940 0, R2p as 0.886 7, RMSEC as 0.722 5, RMSECV as 0.246 2, RMSEP as 1.005, RPD as 2.970 8. Wavelength filtering algorithms SPA and CARS were used to screen the pre-processed spectral data. Then PLS regression model was established and the ability to predict anthocyanins in blueberry pomace was quantitatively analyzed. In the screening of wavelength variables for all pretreatment methods, both SPA and CARS algorithms can effectively screen out the wavelength variables, but the wavelength variables screened by SPA algorithm cannot be used to build PLS regression model, while the wavelength variables screened by CARS algorithm can. The data showed that the optimal combination of CARS-PLS was orthogonal signal correction+MSC+SG convolution smoothing+ SGCSD, with several selected 25 wavelengths. Compared with the original spectrum, its R2c increased from 0.900 8 to 0.940 3, R2p rose from 0.881 8 to 0.885 7, RMSEC decreased from 0.929 1 to 0.720 9, RMSECV dropped from 0.317 6 to 0.245 6, RMSEP changed from 1.021 8 to 1.004 9, and RPD was raised from 2.908 8 to 2.957 5. In the measurement of anthocyanin content in blueberry pomace by near infrared spectroscopy, the orthogonal signal correction has strong denoising effect, while CARS algorithm has the advantages of the simplified model, good applicability and high prediction accuracy. The result indicated that near-infrared spectroscopy could be used to determine anthocyanin content in blueberry pomace of three different varieties, and it can provide a fast and large sample size detection method for blueberry pomace quality classification.
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