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Fourier transform near-infrared spectroscopy (FT-NIR) coupled to chemometric algorithms such as back propagation (BP)-AdaBoost and synergy interval partial least square (Si-PLS) were deployed for the rapid prediction taste quality and taste-related components in black tea. Eight main taste-related components were determined via chemical analysis and Pearson correlations. The achieved chemical results of the eight taste-related components in black tea infusion were predicted based on 160 tea samples obtained from different countries. Prediction results revealed BP-AdaBoost models gave superior predictions, with all the correlation coefficients of the prediction set (R) > 0.76, and the root mean square error values of the prediction set (RMSEP) < 1.7% compared with Si-PLS models (0.71 ≤ Rp ≤ 0.94, 0.08% ≤ RMSEP ≤ 1.73%). This implies that FT-NIR combined to BP-AdaBoostis capable of being deployed for the rapid evaluation of black tea taste quality and taste-related components content simultaneously.

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Diethylstilbestrol (DES), an endocrine disrupting chemical, has been linked to serious health problems in humans. In this work, a regenerative flexible upconversion-fluorescence biosensor was designed for the detection of DES in foodstuffs and environmental samples. Herein, amino-functionalized upconversion nanoparticles (UCNPs) were synthesized and immobilized on the surface of a flexible polydimethylsiloxane substrate, which was further modified with complementary DNA and dabcyl-labeled DES aptamer. The fluorescence resonance energy transfer (FRET) system was established for DES detection between dabcyl and UCNPs as the acceptor and donor pairs, respectively, which resulted in the quenching of the upconversion luminescence intensity. In the presence of a target, the FRET system was destroyed and upconversion fluorescence was restored due to the stronger affinity of the aptamer toward DES. The designed biosensor was also implemented in a dual-mode signal readout based on images from a smartphone and spectra from a spectrometer. Under the optimized experimental conditions, good linear relationships were achieved based on imaging (y = 53.055x + 36.175, R 2 = 0.9851) and spectral data (y = 1.1582x + 1.9561, R 2 = 0.9897). The designed biosensor revealed great practicability with a spiked recovery rate of 77.91–97.95% for DES detection in real environment and foodstuff samples. Furthermore, the proposed biosensor was regenerated seven times with an accuracy threshold of 80% demonstrating its durability and reusability. Thus, this biosensor is expected to be applied to point-of-care and on-site detection based on the developed portable smartphone device and android application.

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