Wang Xuhui, Liu Yaqi, Cai Zhenli, Chen Wenxin, Fu Jiahao, Fan Yao, Fu Haiyan, She Yuanbin
State Key Laboratory of Green Chemical Synthesis and Conversion, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China.
The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
Spectrochim Acta A Mol Biomol Spectrosc. 2026 Jan 5;344(Pt 1):126690. doi: 10.1016/j.saa.2025.126690. Epub 2025 Jul 13.
In this study, a three-channel fluorescence sensor was developed by integrating hydroxylamine hydrochloride (NH₂OH·HCl) with three novel carbon quantum dots (CDs: m-AP@CDs, m-PD@CDs, and o-PD@CDs) to enable the identification of 16 aldehydes, ketones, and diverse baijiu samples. The three-channel sensor was first evaluated for its fluorescence response to aldehydes and ketones, revealing a response intensity hierarchy, ranked as follows: aromatic aldehydes, aliphatic aldehydes, diketones, aromatic ketones, monoketones, and acetal. With the aid of the Linear Discriminant Analysis (LDA) model, all 16 analytes at even low concentrations (2.0 × 10 - 2 mmol/L) could achieve 100 % discrimination accuracy. Furthermore, the spliced spectra of the sensor combined with the Partial Least Squares Regression (PLSR) model was used to accurately quantify 16 kinds of aldehydes and ketones in the range of 2.0 × 10 - 10 mmol/L. What's more, variations in aldehyde/ketone content across baijiu samples could generate cross-responses with the three-channel sensor, which ensured the sensor could accurately distinguish different flavors, brands and quality baijiu combined with the LDA model, underscoring its potential for rapid, on-site quality control and counterfeit detection in the baijiu industry.
在本研究中,通过将盐酸羟胺(NH₂OH·HCl)与三种新型碳量子点(碳点:间氨基苯酚修饰碳点、间苯二酚修饰碳点和邻苯二酚修饰碳点)集成,开发了一种三通道荧光传感器,用于识别16种醛、酮及多种白酒样品。首先评估了该三通道传感器对醛和酮的荧光响应,结果显示出响应强度的层次结构,顺序如下:芳香醛、脂肪醛、二酮、芳香酮、单酮和缩醛。借助线性判别分析(LDA)模型,即使是低浓度(2.0×10⁻² mmol/L)的所有16种分析物也能实现100%的判别准确率。此外,将传感器的拼接光谱与偏最小二乘回归(PLSR)模型相结合,用于准确量化2.0×10⁻¹⁰ mmol/L范围内的16种醛和酮。更重要的是,白酒样品中醛/酮含量的变化会与三通道传感器产生交叉响应,结合LDA模型确保该传感器能够准确区分不同风味、品牌和质量的白酒,突出了其在白酒行业快速现场质量控制和假冒检测方面的潜力。