Optimized prediction of sugar content in 'Snow' pear using near-infrared diffuse reflectance spectroscopy combined with chemometrics.

In near-infrared spectroscopy applications, the original spectra often contain redundant information, which will seriously affect the performance of chemometric models. Therefore, preprocessing, effective wavelengths selection, and appropriate regression models are essential. The objective of this study was to optimize the nondestructive determination multivariate calibration model of sugar content in 'Snow' pears, using near-infrared diffuse reflectance spectroscopy combined with chemometrics. All data (sugar content reference values and spectra data) from three measuring positions (P1, P2, and P3, marked around the pear's equator at angular distances of approximately 120°) were divided into four grouped datasets, namely Set-1 (P1), Set-2 (P2), Set-3 (P3), and Set-4 (average of the three measuring positions). All subsequent optimized processes were performed based on each grouped dataset. First, different preprocessing methods were tested and an optimal method was determined. Then, synergy interval partial least squares and synergy interval partial least squares-competitive adaptive reweighted sampling were applied to select effective regions and effective wavelengths from all wavelengths, respectively, and partial least squares regression models were established and analyzed. In addition, support vector regression models were also established for comparative study. After comprehensive analysis of prediction accuracy and model complexity, the partial least squares regression model based on the 16 selected effective wavelengths for Set-4 was optimal, with the correlation coefficient for prediction, root-mean-square error of prediction, and residual predictive deviation of 0.9701, 0.2311, and 4.12, respectively. The results indicated that with these optimized processes, the multivariate calibration model of sugar content in 'Snow' pears was effectively optimized for each dataset. In addition, it is concluded that partial least squares regression was superior to support vector regression in this study, although some other researches had found different results in related fields. [ABSTRACT FROM AUTHOR]