Search

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 μM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multiple properties of a fluorescent nanozyme increased sensing dimensionalities. • Various pesticides induced multi-signal changes along with salient discrepancy. • Sensor array for broad-spectrum screening of pesticides in diverse concentrations. • Discrimination of complex compositions and cross-contamination. • Constructed LDA model can be used for unknown pesticide recognition. Accurate identification of broad-spectrum pesticides remains a challenge for existing rapid detection technologies based on natural cholinesterase. Sensor arrays show excellent promise, but typically rely on increasing receptors to improve sensing dimensionality, which is both cumbersome and imprecise. In this work, inspired by the multiple properties of a fluorescent nanozyme, we constructed a facile and simple sensor array based on the laccase-like activity, peroxidase-like activity, and fluorescent feature of one nanozyme (Cu-BDC-NH 2). In the presence of pesticides, they produced differential positive responses and could be captured by linear discriminant analysis and hierarchical clustering analysis. Thus, different pesticides belonged to organophosphates, carbamates, organochlorines, pyrethroids, sulfonylureas, and diphenyloxazolines, respectively, have been successfully discriminated at 1 – 100 μg/mL. The differentiation of cross-contamination at different concentrations and compositions further validated the sensing performance. More importantly, they demonstrated strong immunity to interference as well as high feasibility for recognizing pesticides in fruits and vegetables, and performed precise identification for blind samples. Therefore, the sensor arrays provided a novel and reliable solution for broad-spectrum pesticide screening, which not only bridged the gap of existing rapid detection methods, but also opened up a new way for food safety regulation. [ABSTRACT FROM AUTHOR]