Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy

被引:1
|
作者
M. Burak Buyukcan
Ismail Kavdir
机构
[1] Canakkale Onsekiz Mart University,Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering
关键词
FT-NIR spectroscopy; Apricot; Firmness; SSC; Color;
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学科分类号
摘要
The characteristics of internal quality attributes (firmness, soluble solids content and color values) of the Tokaloglu apricot cultivar (Prunus armeniaca L.) were predicted nondestructively using Fourier Transform-Near Infrared (FT-NIR) spectroscopy. Calibration methods were developed between the physical parameters, which were measured using standard methods, and the spectral measurements (in reflectance mode between 780 and 2500 nm) using Partial Least Squares method (PLS). Good correlations were obtained in calibration and validation procedures for Magness-Taylor (MT) maximum force, with a coefficient of determination (R2) of 0.82 (RMSEE = 4.45) in calibration and 0.80 (RMSECV = 4.68) in validation for multiple-harvest (MH) apricot group. The coefficient of determination (R2) for predicting MT slope was 0.79 (RMSEE = 0.83) in calibration and 0.77 (RMSECV = 0.88) in validation for the MH apricot group while it was 0.56 (RMSEE = 0.69) in calibration and 0.47 (RMSECV = 0.80) in validation for single-harvest (SH) apricot group. Good correlations were obtained for MT area with the coefficient of determination (R2) of 0.75 (RMSEE = 20.1) in calibration and R2 = 0.71 (RMSECV = 21) in validation for MH group. Good prediction values were obtained for soluble solids content for both applications (MH and SH) using FT-NIR spectroscopy: the best coefficient of determination was obtained for MH application with 0.77 (RMSEE = 1.45) in calibration and 0.75 (RMSECV = 1.51) in validation. Correlation values for prediction of chroma and hue were low for MH application, with R2 = 0.55 (RMSECV = 3.38) for chroma and with R2 = 0.16 (RMSECV = 0.49) for hue. The results showed that NIR spectroscopy has a good potential to predict internal quality of apricots non-destructively, however it has a limited ability to predict color features.
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页码:651 / 659
页数:8
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