A fast and nondestructive detection method based on hyperspectral imaging technology (HSI) was investigated in this study to discriminate different oolong tea varieties. Five varieties of oolong tea were taken as the research object. Multiplicative scatter correction was used to reduce the influence of noise in the raw spectra. Then competitive adaptive reweighted sampling and bootstrapping soft shrinkage (BOSS) were applied, respectively, to select characteristic wavelengths. Extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) were individually utilized to establish classification models. Finally, the BOSS‐LightGBM model for discriminating tea varieties achieved the best performance, with the accuracy of 100% in the training set and 97.33% in the prediction set. Therefore, it is feasible to use HSI technology coupled with the BOSS‐LightGBM model for the classification of oolong tea varieties.Practical applicationsTieguanyin tea is a high value commodity in the tea market. Replacing Tieguanyin tea with cheaper oolong tea varieties is a common way utilized by illegal traders to maximize profit. Traditional methods for identifying tea varieties are time‐consuming and destructive, and are thus unable to meet the requirements of modern agriculture. In this study, hyperspectral imaging technology (HSI) was applied to realize the fast and nondestructive detection of tea varieties. The final results show that using HSI technology to discriminate different oolong tea varieties is feasible, and also provide a theoretical basis for the design of a portable tea variety detection device.
Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.
Hexavalent chromium
(Cr(VI)) pollution is a global problem, and
the reduction of highly toxic Cr(VI) to less toxic Cr(III) is considered
to be an effective method to address Cr(VI) pollution. In this study,
low-toxicity carbon quantum dots (CQDs) were used to reduce Cr(VI)
in wastewater. The results show that CQDs can directly reduce Cr(VI)
at pH 2 and can achieve a reduction efficiency of 94% within 120 min.
It is observed that under pH higher than 2, CQDs can activate peroxymonosulfate
(PMS) to produce reactive oxygen species (ROS) for the reduction of
Cr(VI) and the reduction efficiency can reach 99% within 120 min even
under neutral conditions. The investigation of the mechanism shows
that the hydroxyl groups on the surface of CQDs can be directly oxidized
by Cr(VI) because of the higher redox potential of Cr(VI) at pH 2.
As the pH increases, the carbonyl groups on the surface of CQDs can
activate PMS to generate ROS, O
2
•–
, and
1
O
2
, which result in Cr(VI) being reduced.
To facilitate the practical application of CQDs, the treatment of
Cr(VI) in real water samples by CQDs was simulated and the method
reduced Cr(VI) from an initial concentration of 5 mg/L to only 8 μg/L
in 150 min, which is below the California water quality standard of
10 μg/L. The study provides a new method for the removal of
Cr(VI) from wastewater and a theoretical basis for practical application.
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