Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China.
Optics-Electrics Application of Biomaterials Lab, College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China; Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Provence, Jiangxi Agricultural University, Nanchang 330045, China.
Poult Sci. 2021 Jan;100(1):296-301. doi: 10.1016/j.psj.2020.10.018. Epub 2020 Oct 14.
There is a critical need for a rapid and simple method of qualitative and quantitative analysis of testosterone propionate (TP) and nandrolone (NT) residues in duck meat. In this study, we applied surface-enhanced Raman spectroscopy (SERS) coupled multivariate analysis for the classification and detection of TP and NT residues in duck meat. A total of 294 duck meat extract samples were obtained from duck breast meats based on a LC-MS/MS sample preparation method with slight modification including 102 duck meat extract samples without TP and NT, 43 duck meat samples containing TP, 47 duck meat extract samples containing NT, and 102 duck meat extract samples containing TP and NT. Raw Raman spectra were pretreated by using adaptive iteratively reweighted penalized least squares (airPLS), normalization and first derivative, and then the score values of first 10 principal components were selected as the inputs of the developed models. A particle swarm optimization-support vector classification (PSO-SVC) model was created to classify all the duck meat samples into the 4 groups (i.e., control group, TP group, NT group, and TP combined with NT group) with the classification accuracies of 99.49 and 100% for training set and test set, respectively. Furthermore, 2 least squares support vector regression (LS-SVR) models were developed to predict the TP values in samples with a determination coefficient (R) value of 0.9316, root mean square error (RMSE) value of 2.1739, and ratio of prediction to deviation (RPD) value of 3.2189 for the test set, and NT values in samples with an R value of 0.9038, RMSE value of 2.2914, and RPD value of 2.9701 for the test set. Surface-enhanced Raman spectroscopy technology, in combination with multivariate analysis, has the potential to become the qualitative and quantitative analysis tool for TP and NT residues in duck meat extract.
迫切需要一种快速而简单的方法来定性和定量分析鸭肉中的丙酸睾酮（TP）和诺龙（NT）残留。在本研究中，我们应用表面增强拉曼光谱（SERS）结合多元分析，用于分类和检测鸭肉中的 TP 和 NT 残留。根据 LC-MS/MS 样品制备方法，共获得 294 个鸭胸肉提取物样品，该方法经过轻微修改，包括 102 个不含 TP 和 NT 的鸭肉提取物样品、43 个含 TP 的鸭肉样品、47 个含 NT 的鸭肉提取物样品和 102 个含 TP 和 NT 的鸭肉提取物样品。原始拉曼光谱通过自适应迭代重加权惩罚最小二乘（airPLS）、归一化和一阶导数预处理，然后选择前 10 个主成分的得分值作为开发模型的输入。创建了粒子群优化支持向量分类（PSO-SVC）模型，将所有鸭肉样品分为 4 组（即对照组、TP 组、NT 组和 TP 与 NT 混合组），训练集和测试集的分类准确率分别为 99.49%和 100%。此外，还建立了 2 个最小二乘支持向量回归（LS-SVR）模型来预测样品中的 TP 值，测试集的决定系数（R）值为 0.9316，均方根误差（RMSE）值为 2.1739，预测偏差比（RPD）值为 3.2189，预测样品中的 NT 值，测试集的 R 值为 0.9038，RMSE 值为 2.2914，RPD 值为 2.9701。表面增强拉曼光谱技术结合多元分析，有望成为鸭肉提取物中 TP 和 NT 残留的定性和定量分析工具。
