[使用便携式近红外分析仪测定苹果中的酸度和维生素C]

[Determination of acidity and vitamin C in apples using portable NIR analyzer].

作者信息

Yang Fan, Li Ya-Ting, Gu Xuan, Ma Jiang, Fan Xing, Wang Xiao-Xuan, Zhang Zhuo-Yong

机构信息

Department of Chemistry, Capital Normal University, Beijing 100048, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Sep;31(9):2386-9.

PMID:22097832

Abstract

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (R(c)) obtained by the acidity model was 0.999 4, and for prediction samples (R(p)) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, R(c) was 0.989 1, R(p) was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.

摘要

基于便携式近红外分析仪的近红外（NIR）光谱技术，结合核等距映射算法和广义回归神经网络（GRNN），已被应用于建立六种苹果样品酸度和维生素C预测的定量模型。所得结果表明，采用核等距映射算法的模型拟合度和预测准确性令人满意。酸度模型获得的校准样品实际值与预测值之间的相关性（R(c)）为0.999 4，预测样品的相关性（R(p)）为0.979 9。预测集的均方根误差（RMSEP）为0.055 8。对于维生素C模型，R(c)为0.989 1，R(p)为0.927 2，RMSEP为4.043 1。结果证明，便携式近红外分析仪可作为测定苹果酸度和维生素C的可行工具。