State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China.
J Agric Food Chem. 2013 Jan 23;61(3):540-6. doi: 10.1021/jf305272s. Epub 2013 Jan 11.
Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.
近红外光谱（NIRS）校准方法被开发用于区分中国山楂（Crataegus pinnatifida Bge. var. major）果实的三个地理来源，并用于估计总糖、总酸、总酚含量和总抗氧化活性。主成分分析（PCA）用于根据果实的地理来源进行区分。三种模式识别方法，线性判别分析、偏最小二乘判别分析和反向传播人工神经网络，被应用于对这些样本进行分类和比较。此外，基于一阶导数近红外光谱、偏最小二乘回归、反向传播人工神经网络和最小二乘支持向量机的三种多元校正模型，被构建用于对四个分析物（总糖、总酸、总酚含量和总抗氧化活性）进行定量分析，并通过预测数据集进行验证。
