The flower buds of Daphne genkwa have been reported as a potent resource associated with anti-angiogenic, anti-tumor, anti-rheumatoid arthritis activities, as well as immunoregulation. This paper aimed to establish an optimal extraction method for flavonoids, as active phytochemicals, and to conduct a comparative analysis by profiling the different blooming stages. Optimized shaking extraction conditions from the design of experiments (DoE), such as minutely mixture design, 2³ full factorial design, and polynomial regression analysis, involved an agitation speed of 150 rpm and temperature of 65 °C for 12 h in 56% (v/v) acetone solvent. After, a comparative analysis was performed on three blooming stages, juvenile bud, mature purple bud, and complete flowering, by ultra-high-performance liquid chromatography-photodiode array-mass spectrometry (UHPLC-PDA-MS). Most flavonoids increased during bud growth and then decreased when the bud opened for blooming. In particular, apigenin 7-O-glucuronide, genkwanin 5-O-primeveroside, and genkwanin strikingly showcased this pattern. Furthermore, the raw spectrometric dataset was subjected to orthogonal projection to latent structures discriminant analysis (OPLS-DA) to find significant differences in the flavonoids from the juvenile bud, mature purple bud, and complete flowering. In conclusion, the present study facilitates an understanding of flavonoid change at different blooming stages and provides a momentous reference in the research of D. genkwa.

Various problems worldwide are caused by illegal production and distribution of timber, such as deception about timber species and origin and illegal logging. Numerous studies on wood tracking are being conducted around the world to demonstrate the legitimacy of timber. Tree species identification is the most basic element of wood tracking research because the quality of wood varies greatly from species to species and is consistent with the botanical origin of commercially distributed wood. Although many recent studies have combined machine learning-based classification methods with various analytical methods to identify tree species, it is unclear which classification model is most effective. The purpose of this work is to examine and compare the performance of three supervised machine learning classification models, support vector machine (SVM), random forest (RF), and artificial neural network (ANN), in identifying five conifer species and propose an optimal model. Using direct analysis in real-time ionization combined with time-of-flight mass spectrometry (DART-TOF-MS), metabolic fingerprints of 250 individual specimens representing five species were collected three times. When the machine learning models were applied to classify the wood species, ANN outperformed SVM and RF. All three models showed 100% prediction accuracy for genus classification. For species classification, the ANN model had the highest prediction accuracy of 98.22%. The RF model had an accuracy of 94.22%, and the SVM had the lowest accuracy of 92.89%. These findings demonstrate the practicality of authenticating wood species by combining DART-TOF-MS with machine learning, and they indicate that ANN is the best model for wood species identification.