Furfural and organic acid targeted carbon dot sensor array for the accurate identification of Chinese baijiu.

Baijiu quality control has always been a major challenge for researchers. In this paper, taking furfural which is closely related to baijiu brewing process and organic acids related to baijiu fermentation process and microorganism types as the main discriminating factors, a carbon dot (CDs) sensor array targeting furfural and organic acids was constructed to identify 41 kinds of baijiu. Through the fluorescence response investigation of CDs synthesized by isomers of benzenediol, aminophenol, and phenylenediamine to different baijiu, two CDs synthesized by meta-benzene substitutions containing -NH2 were selected to build a fluorescence sensor array. Due to the aggregation-induced enhancement effect between furfural and the CDs, and the protonation of organic acid and the CDs, different fluorescence changes were observed, the sensor array combined with partial least squares regression could quantitatively analyze furfural and organic acids. What is more, semi-quantitative analysis of furfural and lactic acid in baijiu was performed. Owing to the interaction of the two CDs with furfural and organic acids in baijiu, the sensor array could accurately identify different baijiu through linear discriminant analysis. This sensor array has potential applications in the quantitative analysis of flavor substances in other alcoholic beverages, moreover, this method could provide a quick response and practical tool for real-time quality control monitoring in the baijiu industry.

Four-channel fluorescent sensor array based on various functionalized CdTe quantum dots for the discrimination of Chinese baijiu.

As a special carrier of traditional Chinese culture, baijiu is rich in terms of types and ingredients. Its quality analysis and control are always important and complex issues that urgently need reliable evaluation methods. In this study, four different modified CdTe quantum dots (QDs) were used to characterize their sensing performance to various baijiu. A sensor array was then constructed through the complementary properties of differential fluorescence signals. To achieve an accurate and rapid evaluation of different baijiu types, a linear discriminant analysis (LDA) was introduced to extract and process spectral information. And the array was able to distinguish commercial baijiu samples with different aroma-types, brands, qualities and storage years with a recognition rate of 100%. In addition, according to the heat map, the organic acids in baijiu were shown to be the main components causing the fluorescence change through electron transfer (hydrogen bond) and resonance energy transfer among QDs and acids. Furthermore, using the partial least squares regression (PLSR) model, five representative organic acids were accurately quantified with a quantitative range of 10 µmol/L-80 µmol/L with a high selectivity. This QDs fluorescence sensing strategy provides an accurate, simple, and fast baijiu sensing method, which provides a potential use for on-line baijiu monitoring.