Yousefi-Nejad Shekoufeh, Heidarbeigi Kobra, Roushani Mahmoud
Mechanical Engineering of Biosystems Department, Ilam University, Ilam, Iran.
Department of Chemistry, Ilam University, Ilam, Iran.
J Food Sci Technol. 2022 Sep;59(9):3548-3556. doi: 10.1007/s13197-021-05349-1. Epub 2022 Jan 6.
Electronic tongue is a new approach for simple and fast detection, classification, and quantification of the solved compounds. Crocin is the main source of color of saffron ( L.). An electronic tongue system was used to predict the concentration of saffron crocin in the present study. The measurement system included an electrochemical sensor array based on voltammetry electrodes, a three-electrode cell, a potentiostat, a personal computer. Aqueous analyte were provided by blending pure crocin and different saffron samples from Iran and Spain with distilled water. Output signals of the electronic tongue system were analyzed by principal component analysis and artificial neural networks. Based on principal component analysis, the total variance among pure crocin was 99% and that of saffron samples was 100%. The accuracy of artificial neural network model was 98.80%. The results indicated that the developed electronic tongue system and artificial neural network model can successfully predict crocin concentration in saffron.
电子舌是一种用于简单、快速检测、分类和定量已溶解化合物的新方法。藏红花素是藏红花(学名:Crocus sativus L.)颜色的主要来源。在本研究中,使用电子舌系统来预测藏红花中藏红花素的浓度。测量系统包括基于伏安电极的电化学传感器阵列、三电极池、恒电位仪和个人计算机。通过将纯藏红花素与来自伊朗和西班牙的不同藏红花样品与蒸馏水混合来提供水性分析物。电子舌系统的输出信号通过主成分分析和人工神经网络进行分析。基于主成分分析,纯藏红花素的总方差为99%,藏红花样品的总方差为100%。人工神经网络模型的准确率为98.80%。结果表明,所开发的电子舌系统和人工神经网络模型能够成功预测藏红花中藏红花素的浓度。