利用近红外反射光谱法快速评估栗子品质

Rapid evaluation of the quality of chestnuts using near-infrared reflectance spectroscopy.

作者信息

Hu Jiaqi, Ma Xiaochen, Liu Lingling, Wu Yanwen, Ouyang Jie

机构信息

Department of Food Science and Engineering, College of Biological Sciences and Technology, Beijing Key Laboratory of Forest Food Process and Safety, Beijing Forestry University, Beijing 100083, China.

Beijing Center for Physical and Chemical Analysis, Beijing Food Safety Analysis and Testing Engineering Research Center, Beijing 100089, China.

出版信息

Food Chem. 2017 Sep 15;231:141-147. doi: 10.1016/j.foodchem.2017.03.127. Epub 2017 Mar 23.

DOI:10.1016/j.foodchem.2017.03.127

PMID:28449990

Abstract

Near-infrared (NIR) diffuse reflectance spectroscopy was used to evaluate the quality of fresh chestnuts, which can be affected by mildew, water, and levels of water-soluble sugars. The NIR spectra were determined and then modeling was performed including principal component analysis - discriminant analysis (PCA-DA), soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), and partial least squares (PLS) methods. LDA model was better than PCA-DA model for the discrimination of normal and mildewed chestnuts, and the accuracy rates of calibration and validation were 100% and 96.37%, respectively. Normal and mildewed chestnuts were easily distinguished by the SIMCA classification and showed only 4.7% overlap. A PLS model was established to determine the water and water-soluble sugars in chestnuts. The R of calibration and validation were all higher than 0.9, while the root mean square errors (RMSE) were all lower than 0.05, indicating that the established models were successful.

摘要

近红外（NIR）漫反射光谱法用于评估新鲜栗子的品质，其品质会受到霉菌、水分和水溶性糖含量的影响。测定了近红外光谱，然后进行建模，包括主成分分析-判别分析（PCA-DA）、类相关软独立建模（SIMCA）、线性判别分析（LDA）和偏最小二乘法（PLS）。LDA模型在区分正常栗子和发霉栗子方面优于PCA-DA模型，校准和验证的准确率分别为100%和96.37%。通过SIMCA分类可以轻松区分正常栗子和发霉栗子，重叠率仅为4.7%。建立了一个PLS模型来测定栗子中的水分和水溶性糖。校准和验证的R值均高于0.9，而均方根误差（RMSE）均低于0.05，表明所建立的模型是成功的。