Application of OSC in Sugar Content Evaluation of Chestnut Based on near Infrared Spectroscopy
Year: 2014
Type: article
Abstract: It is important in chestnut industry to evaluate the sugar content of nuts since sugar content is one of parameters for classifying the fruit to different productions. Previous work had proved the near infrared (NIR) spectroscopy could be used to measuring the sugar content in intact and peeled chestnut nondestructively; however, the performance of the predictive model would need more improvement. In this work, the orthogonal signal correction (OSC) algorithm was employed to optimize the predictive models. The results shown that, for the peeled chestnut sample, OSC could increase the correlation coefficient (R2) of validation set from 0.8649 to 0.8961while decrease the root mean and square error of prediction from 0.739 to 0.626. For the intact chestnut sample, this algorithm did not improve the model performance. The results indicated that the OSC had potential to optimized the prediction accuracy of sugar content in chestnut based on near infrared spectroscopy.
Source: Applied Mechanics and Materials
Authors: Jie Liu, Xiao Yu Li, Wei Wang, Jun Zhang, Wei Zhou
Institution: Huazhong Agricultural University
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W2024926924