Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

China grows and consumes numerous types of tea, which have diverse processing techniques. West Lake Longjing Tea is one of the most famous and popular varieties of tea in China. It is difficult for consumers to assess the quality of Longjing green tea, as it usually requires well-trained experts to make the judgement based on colour, aroma, and taste. To this end, we propose a quality identification system consisting of a self-developed electronic nose and a data analysis algorithm to assess the quality of West Lake Longjing Tea based on its aroma. The equipment was tested extensively in experiments conducted on real-world data. The results show that the proposed system is capable of distinguishing the tea grades accurately. Furthermore, we studied the quality specifications of Longjing tea sold by different brands and found that standard certified brands have more accurate quality identification criteria than non-standard certified brands. Our findings will assist customers and tea factories in evaluating the quality of Longjing Tea and guide the optimisation of quality standards.

Quality level identification of West Lake Longjing green tea using electronic nose

Combining stable C, N, O, H, Sr isotope and multi-element with chemometrics for identifying the geographical origins and farming patterns of Huangjing herb

/pdf/electronic-sensor-technologies-in-monitoring-quality-of-tea-23t1mx6y.pdf

Electronic Sensor Technologies in Monitoring Quality of Tea: A Review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

. In this work, electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) were jointly applied as intelligent instruments to acquire aroma, taste and color signals of tea samples. Features were severally extracted from E-nose, E-tongue and E-eye signals and were fused for analysis. The polyphenols, catechins, caffeine and amino acid as quality indices were detected by traditional methods as reference. For qualitative identification, support vector machine (SVM) and random forest (RF) were comparatively employed in modeling severally based on individual and fusion signals. The SVM and RF models based on the fusion signals achieved perfect classification results with the accuracy of 100%. For quantitative prediction of tea quality indices, partial least squares regression (PLSR), SVM and RF were applied based on individual and fusion signals to establish regression models between electronic signals and the amount of polyphenols, catechins, caffeine and amino acid. The RF prediction models reached higher correlation coefficients (R2) and lower root mean square errors (RMSE) than the PLSR and SVM models did. Meanwhile, the fusion signals had a better performance than the individual signals in PLSR, SVM and RF regression models. This work indicated that the simultaneous utilization of E-nose, E-tongue and E-eye based on appropriate chemometrics method could be successfully applied for qualitative and quantitative analysis of tea quality.

The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye signals combining with chemometrics methods

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The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye signals combining with chemometrics methods