Hygienic Safety and Analysis Center, World Institute of Kimchi, Gwangju 61755, Republic of Korea.
Food Chem. 2020 Oct 30;328:127149. doi: 10.1016/j.foodchem.2020.127149. Epub 2020 May 26.
A novel approach for the discrimination of different glucosinolates (sinigrin, progoitrin, gluconapin, 4-methoxyglucobrassicin, glucoraphanin, glucobrassicin, glucoiberin, glucobrassicanapin, glucoraphenin, and glucoerucin) using a colorimetric sensor array (CSA) is reported herein. The developed CSA technique exhibited an acceptable linearity (r ≥ 0.97) over a concentration range of 0-150 μM for the 10 glucosinolates. The CSA coupled with principal component analysis and hierarchical cluster analysis correctly distinguished the majority of glucosinolate samples according to their type. In addition, the CSA coupled with linear discriminant analysis correctly classified the majority of 8 kinds of cruciferous vegetable samples with an overall accuracy of 94%. Furthermore, the partial least squares regression results showed that the CSA responses were correlated with the concentration in a correlation coefficient (R) range of 0.813-0.964. These results demonstrate that the described procedure based on the CSA technique could be useful for the rapid discrimination of different glucosinolates.
本文报道了一种利用比色传感器阵列(CSA)区分不同硫代葡萄糖苷(黑芥子硫苷、白芥子硫苷、葡萄糖苷、4-甲氧基葡萄糖苷、萝卜硫素、葡萄糖苷、葡萄糖苷、葡萄糖苷、葡萄糖苷、葡萄糖苷)的新方法。所开发的 CSA 技术在 0-150μM 的浓度范围内对 10 种硫代葡萄糖苷表现出良好的线性(r≥0.97)。CSA 结合主成分分析和层次聚类分析可以根据硫代葡萄糖苷的类型正确区分大多数硫代葡萄糖苷样品。此外,CSA 结合线性判别分析可以正确分类 8 种十字花科蔬菜样品中的大多数,总体准确率为 94%。此外,偏最小二乘回归结果表明,CSA 响应与浓度之间存在相关性,相关系数(R)范围为 0.813-0.964。这些结果表明,基于 CSA 技术的描述程序可用于快速区分不同的硫代葡萄糖苷。