Mabood Fazal, Hussain Javid, Jabeen Farah, Abbas Ghulam, Allaham Batoul, Albroumi Mohammed, Alghawi Said, Alameri Saif, Gilani Syed A, Al-Harrasi Ahmed, Haq Quazi M I, Farooq Saima
a Department of Biological Sciences and Chemistry, College of Arts and Sciences , University of Nizwa , Nizwa , Oman.
b Department of Chemistry , University of Malakand , Chakdara , Pakistan.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2018 Jun;35(6):1052-1060. doi: 10.1080/19440049.2018.1457802. Epub 2018 Jun 18.
Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.
检测富含碳水化合物的食品（如果汁）中的掺假物特别困难，因为市面上有各种各样的商业甜味剂，其浓度分布与食品中主要碳水化合物的浓度分布相匹配。在本研究中，已开发出一种新的灵敏且稳健的检测方法，即使用傅里叶变换近红外光谱（FT-NIRS）结合偏最小二乘法（PLS）多变量方法，用于检测和定量不同商业果汁样品中的糖精掺假情况。在本次调查中，对六种不同的市售果汁样品故意掺入以下百分比水平的糖精：0%、0.10%、0.30%、0.50%、0.70%、0.90%、1.10%、1.30%、1.50%、1.70%和2.00%（重量/体积）。总共使用了198个样品，包括18个纯果汁样品（未掺假）和180个掺有糖精的果汁样品。将包括偏最小二乘判别分析（PLS-DA）和偏最小二乘回归（PLSR）在内的PLS多变量方法应用于获得的光谱数据以建立模型。PLS-DA模型用于区分纯果汁样品和掺有糖精的样品。PLS-DA模型获得的R值为97.90%，均方根误差（RMSE）为0.67%。同样，还开发了一个PLS回归模型来定量果汁样品中糖精掺假物的含量。PLSR模型获得的R值为97.04%，交叉验证均方根误差（RMSECV）为0.88%。然后使用一个包含30%掺假果汁样品总数的测试集对所采用的模型进行交叉验证。该模型的出色性能通过预测值的低均方根误差0.92%和高相关系数0.97得到证明。这种新开发的方法稳健、无损、高度灵敏且经济。
