Fucoxanthin (FX), a marine carotenoid found in macroalgae and microalgae, exhibits several beneficial effects to health. The anti-obesity activity of FX is well documented, but FX has not been mass-produced or applied extensively or commercially because of limited availability of raw materials and complex extraction techniques. In this study, we investigated the anti-obesity effect of standardized FX powder (Phaeodactylum extract (PE)) developed from microalga Phaeodactylum tricornutum as a commercial functional food. The effects of PE on adipogenesis inhibition in 3T3-L1 adipocytes and anti-obesity in high-fat diet (HFD)-fed C57BL/6J mice were evaluated. PE and FX dose-dependently decreased intracellular lipid contents in adipocytes without cytotoxicity. In HFD-fed obese mice, PE supplementation for six weeks decreased body weight, organ weight, and adipocyte size. In the serum parameter analysis, the PE-treated groups showed attenuation of lipid metabolism dysfunction and liver damage induced by HFD. In the liver, uncoupling protein-1 (UCP1) upregulation and peroxisome proliferator activated receptor γ (PPARγ) downregulation were detected in the PE-treated groups. Additionally, micro computed tomography revealed lower fat accumulation in PE-treated groups compared to that in the HFD group. These results indicate that PE exerts anti-obesity effects by inhibiting adipocytic lipogenesis, inducing fat mass reduction and decreasing intracellular lipid content, adipocyte size, and adipose weight.

Mealworm (Tenebrio molitor L.) is a classic edible insect with high nutritional value for substituting meats from vertebrates. While interest in mealworms has increased, the determination of carbohydrate constituents of mealworms has been overlooked. Thus, the aim of the present study was to investigate the carbohydrate content and composition of mealworms. In addition, the characteristics of mealworm chitin were determined as these were the major components of mealworm carbohydrate. The crude carbohydrate content of mealworms was 11.5%, but the total soluble sugar content was only 30% of the total carbohydrate content, and fructose was identified as the most abundant free sugar in mealworms. Chitin derivatives were the key components of mealworm carbohydrate with a yield of 4.7%. In the scanning electron microscopy images, a lamellar structure with α-chitin configuration was observed, and mealworm chitosan showed multiple pores on its surface. The overall physical characteristics of mealworm chitin and chitosan were similar to those of the commercial products derived from crustaceans. However, mealworm chitin showed a significantly softer texture than crustacean chitin with superior anti-inflammatory effects. Hence, mealworm chitin and chitosan could be employed as novel resources with unique advantages in industries.

Ligularia fischeri, a leafy edible plant found in damp shady regions, has been used as an herbal medicine and is also consumed as a horticultural crop. In this study, we investigated the physiological and transcriptomic changes, especially those involved in phenylpropanoid biosynthesis, induced by severe drought stress in L. fischeri plants. A distinguishing characteristic of L. fischeri is a color change from green to purple due to anthocyanin biosynthesis. We chromatographically isolated and identified two anthocyanins and two flavones upregulated by drought stress using liquid chromatography-mass spectrometry and nuclear magnetic resonance analyses in this plant for the first time. In contrast, all types of caffeoylquinic acids (CQAs) and flavonol contents were decreased under drought stress. Further, we performed RNA sequencing to examine the molecular changes in these phenolic compounds at the transcriptome level. In an overview of drought-inducible responses, we identified 2,105 hits for 516 distinct transcripts as drought-responsive genes. Moreover, differentially expressed genes (DEGs) associated with phenylpropanoid biosynthesis accounted for the greatest number of both up- and downregulated DEGs by Kyoto Encyclopedia of Genes and Genomes enrichment analysis. We identified 24 meaningful DEGs based on the regulation of phenylpropanoid biosynthetic genes. Potential drought-responsive genes included upregulated flavone synthase (LfFNS, TRINITY DN31661 c0 g1 i1) and anthocyanin 5-O-glucosyltransferase (LfA5GT1, TRINITY DN782 c0 g1 i1), which could contribute to the high levels of flavones and anthocyanins under drought stress in L. fischeri. In addition, the downregulated shikimate O-hydroxycinnamolytransferase (LfHCT, TRINITY DN31661 c0 g1 i1) and hydroxycinnamoyl-CoA quinate/shikimate transferase (LfHQT4, TRINITY DN15180 c0 g1 i1) genes led to a reduction in CQAs. Only one or two BLASTP hits for LfHCT were obtained for six different Asteraceae species. It is possible that the HCT gene plays a crucial role in CQAs biosynthesis in these species. These findings expand our knowledge of the response mechanisms to drought stress, particularly regarding the regulation of key phenylpropanoid biosynthetic genes in L. fischeri.

The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.