关键词：
Competitive adaptive reweighted sampling (CARS);Honey adulteration;Near-infrared spectroscopy;Partial least squares linear discriminant analysis (PLS-LDA);Partial least squares regression (PLSR)
摘要：
Near-infrared spectroscopy (NIR) was used for qualitative and quantitative detection of honey adulterated with high-fructose corn syrup (HFCS) or maltose syrup (MS). Competitive adaptive reweighted sampling (CARS) was employed to select key variables. Partial least squares linear discriminant analysis (PLS-LDA) was adopted to classify the adulterated honey samples. The CARS-PLS-LDA models showed an accuracy of 86.3% (honey vs. adulterated honey with HFCS) and 96.1% (honey vs. adulterated honey with MS), respectively. PLS regression (PLSR) was used to predict the extent of adulteration in the honeys. The results showed that NIR combined with PLSR could not be used to quantify adulteration with HFCS, but could be used to quantify adulteration with MS: coefficient (RP) and root mean square of prediction (RMSEP) were 0.901 and 4.041 for MS-adulterated samples from different floral origins, and 0.981 and 1.786 for MS-adulterated samples from the same floral origin (Brassica spp.), respectively. (C) 2016 Published by Elsevier Ltd.
作者机构：
[李姣娟; 李水芳; 黄自知] College of Science, Central South University of Forestry and Technology, Changsha 4l0004, China;[Shan, Yang] Hunan Food Test and Analysis Center, Changsha 410125, China;[张欣] Longping Branch Graduate School, Central South University, Changsha 410125, China
通讯机构：
Hunan Food Test and Analysis Center, China
关键词：
无损检测;拉曼光谱;葡萄糖;蜂蜜;果糖含量;偏最小二乘法;支持向量机法
摘要：
应用拉曼光谱结合化学计量学方法对蜂蜜果糖和葡萄糖含量进行了定量分析。用自适应迭代重加权惩罚最小二乘（adaptive iteratively reweighted penalized least squares，airPLS）算法进行基线校正，用竞争性自适应重加权采样（competitive adaptive reweighted sampling，CARS）算法筛选变量，分别用线性的偏最小二乘（partial least squares，PLS）回归算法和非线性的支持向量机（support vector machines，SVM）回归算法建立定量校正模型，并进行预测。2种模型都有较好的预测结果。对果糖，SVM模型预测值与高效液相色谱法（high performance liquid chromatography，HPLC）测定值的相关系数（R）和预测均方根误差（root mean square error of prediction，RMSEP）分别为0.902和1.401，略优于PLS模型（R为0.892，RMSEP为1.604）；对葡萄糖，PLS模型的R和RMSEP分别为0.968和0.669，优于SVM模型（R为0.933，RMSEP为1.410）。结果表明拉曼光谱结合化学计量学方法可快速无损测定蜂蜜果糖和葡萄糖含量。
摘要：
Abstract: Total of 4 pattern recognition methods for the authentication of pure camellia oil applying near infrared (NIR) spectroscopy were evaluated in this study. Total of 115 samples were collected and their authenticities were confirmed by gas chromatography (GC) in according to China Natl. Standard (GB). A preliminary study of NIR spectral data was analyzed by unsupervised methods including principal component analysis (PCA) and hierarchical cluster analysis (HCA). Total of 2 supervised classification techniques based on discriminant analysis (DA) and radical basis function neural network (RBFNN) were utilized to build calibration model and predict unknown samples. In the wavenumber range of 6000 to 5750 cm−1, correct classification rate of both supervised and unsupervised solutions all can reach 98.3% when smoothing, first derivative, and autoscaling were used. The good performance showed that NIR spectroscopy with multivariate calibration models could be successfully used as a rapid, simple, and nondestructive method to discriminate pure camellia oil.
期刊：
Journal of Food Composition and Analysis,2012年28(1):69-74 ISSN：0889-1575
通讯作者：
Li, Shuifang
作者机构：
[Li, Shuifang; Ling, Guowei] Cent S Univ Forestry & Technol, Coll Sci, Changsha 410004, Hunan, Peoples R China.;[Zhu, Xiangrong; Shan, Yang] Hunan Acad Agr Sci, Hunan Agr Prod Proc Inst, Changsha 410125, Hunan, Peoples R China.;[Zhang, Xin] Cent S Univ, Grad Sch, Longping Branch, Changsha 410025, Hunan, Peoples R China.
通讯机构：
[Li, Shuifang] C;Cent S Univ Forestry & Technol, Coll Sci, Changsha 410004, Hunan, Peoples R China.
关键词：
Food composition;Food analysis;Honey;Adulteration;Raman spectroscopy;Adaptive iteratively reweighted penalized least squares (airPLS);Spectral background signal removing;Partial least squares-linear discriminant analysis (PLS-LDA)
摘要：
Raman spectroscopy was used to detect adulterants such as high fructose corn syrup (HFCS) and maltose syrup (MS) in honey. HFCS and MS were each mixed with authentic honey samples in the following ratios: 1:10 (10%), 1:5 (20%) and 1:2.5 (40%, w/w). Adaptive iteratively reweighted penalized least squares (airPLS) was chosen to remove background of spectral data. Partial least squares-linear discriminant analysis (PLS-LDA) was used to develop a binary classification model. Classification of honey authenticity using PLS-LDA showed a total accuracy of 91.1% (authentic honey vs. adulterated honey with HFCS), 97.8% (authentic honey vs. adulterated honey with MS) and 75.6% (authentic honey vs. adulterated honey with HFCS and MS), respectively. Classification of honey adulterants (e.g. HFCS or MS) using PLS-LDA gave a total accuracy of 84.4%. The results showed that Raman spectroscopy combined with PLS-LDA was a potential technique for detecting adulterants in honey. (C) 2012 Elsevier Inc. All rights reserved.
作者机构：
[李水芳] College of Science, Central South University of Forestry and Technology, Changsha 410004, China;[李忠海] College of Food Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China;[Shan, Yang; 朱向荣] Hunan Food Test and Analysis Center, Changsha 410025, China
