Chromosome-level genome and multi-omics analyses provide insights into the geo-herbalism properties of Alpinia oxyphylla

Spatiotemporal analysis of microstructure, sensory attributes, and full-spectrum metabolomes reveals the relationship between bitterness and nootkatone in Alpinia oxyphylla miquel fruit peel and seeds

The Alpinia oxyphylla fruit (AOF) is a popular condiment and traditional Chinese medicine in Asia, known for its neuroprotective compound nootkatone. However, there has not been a comprehensive study of its flavor or the relationship between sensory and bioactive compounds. To address this issue, we examined AOF's microstructure, flavor, and metabolomic profiles during fruit maturation. The key markers used to distinguish samples included fruit expansion, testa pigmentation, aril liquefaction, oil cell expansion, peel spiciness, aril sweetness, and seed bitterness. A full-spectrum metabolomic analysis, combining a nontargeted metabolomics approach for volatile compounds and a widely targeted metabolomics approach for nonvolatile compounds, identified 1,448 metabolites, including 1,410 differentially accumulated metabolites (DAMs). Notably, 31 DAMs, including nootkatone, were associated with spicy peel, sweet aril, and bitter seeds. Correlational analysis indicated that bitterness intensity is an easy-to-use biomarker for nootkatone content in seeds. KEGG enrichment analysis linked peel spiciness to phenylpropanoid and capsaicin biosynthesis, seed bitterness to terpenoid (especially nootkatone) biosynthesis, and aril sweetness to starch and sucrose metabolism. This investigation advances the understanding of AOF's complex flavor chemistry and underlying bioactive principle, encapsulating the essence of the adage: "no bitterness, no intelligence" within the realm of phytochemistry.

Improving the geographical origin classification of Radix glycyrrhizae (licorice) through hyperspectral imaging assisted by U-Net fine structure recognition

Radix glycyrrhizae (licorice) is extensively employed in traditional Chinese medicine, and serves as a crucial raw material in industries such as food and cosmetics. The quality of licorice from different origins varies greatly, so classification of its geographical origin is particularly important. This study proposes a technique for fine structure recognition and segmentation of hyperspectral images of licorice using deep learning U-Net neural networks to segment the tissue structure patterns (phloem, xylem, and pith). Firstly, the three partitions were separately labeled using the Labelme tool, which was utilized to train the U-Net model. Secondly, the obtained optimal U-Net model was applied to predict three partitions of all samples. Lastly, various machine learning models (LDA, SVM, and PLS-DA) were trained based on segmented hyperspectral data. In addition, a threshold method and a circumcircle method were applied to segment licorice hyperspectral images for comparison. The results revealed that compared with the threshold segmentation method (which yielded SVM classifier accuracies of 99.17%, 91.15%, and 92.50% on the training set, validation set, and test set, respectively), the U-Net segmentation method significantly enhanced the accuracy of origin classification (99.06%, 94.72% and 96.07%). Conversely, the circumcircle segmentation method did not effectively improve the accuracy of origin classification (99.65%, 91.16% and 92.13%). By integrating Raman imaging of licorice, it can be inferred that the U-Net model, designed for region segmentation based on the inherent tissue structure of licorice, can effectively improve the accuracy origin classification, which has positive significance in the development of intelligence and information technology of Chinese medicine quality control.