Joshi Rahul, Adhikari Samir, Pil Son Jong, Jang Yudong, Lee Donghan, Cho Byoung-Kwan
Department of Biosystems Machinery Engineering, Chungnam National University, 99 Daehak-to, Yuseong-gu, Daejeon 34134, Republic of Korea.
Institute of Quantum Systems, Chungnam National University, Daejeon 34134, Republic of Korea.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Sep 5;297:122734. doi: 10.1016/j.saa.2023.122734. Epub 2023 Apr 13.
Conventional spectroscopic methods like IR, and Raman are not very effective at detecting low levels of pesticides or harmful chemicals in food matrices. A quick, highly accurate approach that can identify pesticides present in different food products at lower levels must be developed in order to address this problem and ensure food safety. In this study, a highly sensitive and uniform wafer-scale Au nanogap surface-enhanced Raman spectroscopy (SERS) substrate was used for the quantitative analysis of carbaryl pesticide levels in standard solution, mango juice, and milk samples using chemometrics. Carbaryl was detected up to 3 ppb concentration levels for all three group of samples. However, due to the higher sensitivity, uniformity, and enhancement factors of the SERS substrate used in this investigation, the limit of detection (LOD) values for the standard solution, mango juice, and milk were 0.37 ppb, 0.57 ppb, and 0.15 ppb at 1380 cm, 1380 cm, and 1364 cm wavenumber ranges. In order to predict different carbaryl concentrations (1, 2, 3, 4, and 5 ppb), the variable importance in projection (VIP) method combined with partial least squares regression (PLSR) and attained the coefficient of determination (R) values of 0.994, 0.989, and 0.978 along with minimum root mean square error (RMSE) values of 0.112, 0.190, and 0.278 ppb for the prediction datasets. Furthermore, PLS-DA was able to distinguish between pure and adulterated samples with the highest classification accuracy of 100 % for a standard solution, and mango juice and 94.4 % for milk samples. Considering this, we can conclude that the SERS Au Nanogap substrate can rapidly and effectively detect carbaryl pesticides quantitatively and qualitatively in mango juice and milk.
像红外光谱和拉曼光谱这样的传统光谱方法，在检测食品基质中低含量的农药或有害化学物质时效果不是很好。为了解决这个问题并确保食品安全，必须开发一种快速、高精度的方法，能够识别不同食品中低含量的农药。在本研究中，使用了一种高度灵敏且均匀的晶圆级金纳米间隙表面增强拉曼光谱（SERS）基底，结合化学计量学对标准溶液、芒果汁和牛奶样品中的西维因农药含量进行定量分析。在所有三组样品中，西维因的检测浓度高达3 ppb。然而，由于本研究中使用的SERS基底具有更高的灵敏度、均匀性和增强因子，在1380 cm、1380 cm和1364 cm波数范围内，标准溶液、芒果汁和牛奶的检测限（LOD）值分别为0.37 ppb、0.57 ppb和0.15 ppb。为了预测不同的西维因浓度（1、2、3、4和5 ppb），采用投影变量重要性（VIP）方法结合偏最小二乘回归（PLSR），预测数据集的决定系数（R）值分别为0.994、0.989和0.978，最小均方根误差（RMSE）值分别为0.112、0.190和0.278 ppb。此外，PLS-DA能够区分纯样品和掺假样品，标准溶液和芒果汁的最高分类准确率为100%，牛奶样品为94.4%。据此，我们可以得出结论，SERS金纳米间隙基底能够快速有效地对芒果汁和牛奶中的西维因农药进行定量和定性检测。
