Varietal Authenticity Assessment of QTMJ Tea Using Non-Targeted Metabolomics and Multi-Elemental Analysis with Chemometrics.

In this paper, a combination of non-targeted metabolomics and multi-element analysis was used to investigate the impact of five different cultivars on the sensory quality of QTMJ tea and identify candidate markers for varietal authenticity assessment. With chemometric analysis, a total of 54 differential metabolites were screened, with the abundances significantly varied in the tea cultivars. By contrast, the QTMJ tea from the Yaoshan Xiulv (XL) monovariety presents a much better sensory quality as result of the relatively more abundant anthocyanin glycosides and the lower levels of 2'-o-methyladenosine, denudatine, kynurenic acid and L-pipecolic acid. In addition, multi-elemental analysis found 14 significantly differential elements among the cultivars (VIP > 1 and p < 0.05). The differences and correlations of metabolites and elemental signatures of QTMJ tea between five cultivars were discussed using a Pearson correlation analysis. Element characteristics can be used as the best discriminant index for different cultivars of QTMJT, with a predictive accuracy of 100%.

Impact of storage time on non-volatile metabolites and fungal communities in Liupao tea using LC-MS based non-targeted metabolomics and high-throughput sequencing.

Long-term storage of Liupao tea (LPT) is usually believed to enhance its quality and commercial value. The non-volatile metabolites variations and the fungal succession play a key role for organoleptic qualities during the storage procedure. To gain in-depth understanding the impact of storage time on the quality of LPT, two different brands of LPT with different storage time, including Maosheng LPTs (MS) with 0, 5, 10 and 15 years and Tianyu LPTs (TY) with 0, 3, 5, 8 and 10 years, were resorted to investigate the changes of non-volatile metabolites and fungi as well as their correlation by multi-omics. A total of 154 and 119 differential metabolites were identified in these two different brands of MS and TY, respectively, with the aid of high-performance liquid chromatography with quadrupole-time-of-flight mass spectrometry. In both categories of LPTs, the transformation of differential metabolites in the various stages referred to the formation of alkaloids, increase of organic acids, biosynthesis of terpenoids as well as glycosylation and methylation of flavonoids. Thereinto, glycosylation and methylation of flavonoids were the critical stages for distinguishing MS and TY, which were discovered in MS and TY stored for about 10 and 8 years, respectively. Moreover, the results of high-throughput sequencing showed that the key fungal genera in the storage of LPTs consisted of Eurotium, Aspergillus, Blastobotrys, Talaromyces, Thermomyces and Trichomonascus. It was confirmed on the basis of multivariate analysis that the specific fungal genera promoted the transformation of metabolites, affecting the tea quality to some extent. Therefore, this study provided a theoretical basis for the process optimization of LPT storage.

A comprehensive review of the current trends and recent advancements on the authenticity of honey.

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

Target Detection Based on Improved Hausdorff Distance Matching Algorithm for Millimeter-Wave Radar and Video Fusion.

The intelligent transportation system (ITS) is inseparable from people's lives, and the development of artificial intelligence has made intelligent video surveillance systems more widely used. In practical traffic scenarios, the detection and tracking of vehicle targets is an important core aspect of intelligent surveillance systems and has become a hot topic of research today. However, in practical applications, there is a wide variety of targets and often interference factors such as occlusion, while a single sensor is unable to collect a wealth of information. In this paper, we propose an improved data matching method to fuse the video information obtained from the camera with the millimetre-wave radar information for the alignment and correlation of multi-target data in the spatial dimension, in order to address the problem of poor recognition alignment caused by mutual occlusion between vehicles and external environmental disturbances in intelligent transportation systems. The spatio-temporal alignment of the two sensors is first performed to determine the conversion relationship between the radar and pixel coordinate systems, and the calibration on the timeline is performed by Lagrangian interpolation. An improved Hausdorff distance matching algorithm is proposed for the data dimension to calculate the similarity between the data collected by the two sensors, to determine whether they are state descriptions of the same target, and to match the data with high similarity to delineate the region of interest (ROI) for target vehicle detection.

Efficiency Improvement of Quantum Dot Light-Emitting Diodes via Thermal Damage Suppression with HATCN.

With many advantages including superior color saturation and efficiency, quantum dot light-emitting diodes (QLEDs) are considered a promising candidate for the next-generation displays. Emission uniformity over the entire device area is a critical factor to the overall performance and reliability of QLEDs. In this work, we performed a thorough study on the origin of dark spots commonly observed in operating QLEDs and developed a strategy to eliminate these defects. Using advanced cross section fabrication and imaging techniques, we discovered the occurrence of voids in the organic hole transport layer and directly correlated them to the observed emission nonuniformity. Further investigations revealed that these voids are thermal damages induced during the subsequent thermal deposition of other functional layers and can act as leakage paths in the device. By inserting a thermo-tolerant 1,4,5,8,9,11-hexaazatriphenylene-hexacarbonitrile (HATCN) interlayer with an optimized thickness, the thermally induced dark spots can be completely suppressed, leading to a current efficiency increase by 18%. We further demonstrated that such a thermal passivation strategy can work universally for various types of organic layers with low thermal stability. Our findings here provide important guidance in enhancing the performances and reliability of QLEDs and also other sandwich-structured devices via the passivation of heat-sensitive layers.