Youn-Il Park

Wheat (Triticum aestivum L.) is among the most widely consumed cereal grains worldwide and is essential for global food security. This review critically examines current evidence on wheat bioactive compounds, nutritional composition, and health-promoting properties. Wheat contains diverse nutritional and bioactive compounds, including phenolic acids, flavonoids, lignans, alkylresorcinols, phytosterols, and bioactive peptides. This review systematically evaluates evidence across multiple levels, from chemical characterization and cell-based studies to animal models and human clinical trials, with explicit differentiation of evidence quality. Wheat-derived compounds demonstrate antioxidant, antidiabetic, anti-obesity, anti-inflammatory, anticancer, antimicrobial, and gut microbiota-modulating activities in preclinical models. Identified molecular mechanisms converge on Nrf2-mediated antioxidant defense, NF-κB inflammatory pathway inhibition, AMPK metabolic regulation, and PI3K/AKT insulin signaling. Recent advances in artificial intelligence (AI) and machine learning provide new tools for identifying wheat bioactive molecules, predicting biological targets, and assessing flour quality through hyperspectral imaging. AI-integrated multi-omics approaches offer potential for personalized wheat-based nutrition strategies by matching individual genetic, metabolic, and microbiome profiles to specific wheat varieties or bioactive extracts. This review concludes that wheat-derived bioactive compounds show significant nutraceutical potential; however, clinical translation requires rigorous human studies, and safety considerations for gluten-sensitive populations must be addressed.

Introduction: Wheat kernel hardness, vitreousness, and creaseness are key determinants of milling performance, yet they reflect different physical scales of grain structure and are not necessarily coupled. Methods: We developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat. Pixel-level spectral unmixing resolved glassy, intermediate, and mealy endosperm components within individual kernels, enabling vitreousness to be expressed as a continuous spatial index. Results: The hyperspectral-derived vitreousness index showed moderate associations with kernel protein content and the protein-to-starch ratio, consistent with variation in endosperm packing density, but weak relationships with kernel hardness and crease geometry. Kernel hardness, primarily determined by puroindoline genotype, showed limited association with bulk protein and starch composition. Crease geometry, quantified using composite indices from RGB images, captured macroscopic grain features largely independent of both hardness and vitreousness. Discussion: These results demonstrate that hardness, vitreousness, and creaseness represent complementary but largely independent dimensions of grain quality, corresponding to molecular-scale adhesion, mesoscale packing, and macroscopic geometry, respectively. The proposed framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity, enabling improved digital phenotyping for wheat breeding and quality assessment.

Hyperspectral imaging (HSI) has emerged as a powerful tool for non-destructive phenotyping, yet fruit crop applications remain underexplored. We propose a methodological framework to enhance the spectral characterization of apple fruits by identifying robust vegetation indices (VIs) and interpretable endmembers. We screened 284 Vis, which were evaluated using four feature selection algorithms (Boruta, MI+Lasso, RFE, and ensemble voting), generalizing across red, yellow, green, and purple apple cultivars. An ensemble criterion (≥2 algorithms) yielded 50 selected VIs from the NDSI/DSI/RSI families, preserving > 95% classification accuracy and capturing cultivar-specific variation. Pigment-sensitive wavelength bands were identified via PLS-DA VIP scores and one-vs-rest ANOVA. Using these bands, we formulated a new normalized-difference, ratio, and difference spectral indices tailored to cultivar-specific pigmentation. Several indices achieved >89% classification accuracy and showed patterns consistent with those of anthocyanin, carotenoid, and chlorophyll. A two-stage spectral unmixing pipeline (K-Means → N-FINDR) achieved the lowest reconstruction RMSE (0.043%). This multi-level strategy provides a scalable, interpretable framework for enhancing phenotypic resolution in apple hyperspectral data, contributing to fruit index development and generalized spectral analysis methods for horticultural applications.

Previous studies have identified three families of knotted phytochrome photoreceptors in cyanobacteria. We describe a fourth type: ‘hybrid’ phytochromes with putative bilin‐binding cysteine residues in both their N‐terminal ‘knot’ extensions and cGMP‐phosphodiesterase/adenylate cyclase/FhlA (GAF) domains, which we designate as dual‐cysteine bacteriophytochromes (DCBs). Recombinant expression of DCBs in Escherichia coli yields photoactive phycocyanobilin (PCB) adducts with red/far‐red photocycles similar to those of the GAF‐Cys‐containing cyanobacterial phytochromes (Cph1s). Incorporation of the PCB precursor, biliverdin IXα (BV), gave multiple populations, one of which appears similar to those of cyanobacterial bacteriophytochromes (cBphPs). A crystal structure of FiDCB bound to BV exhibits two thioether linkages between the GAF‐ and ‘PAS‐knot’‐Cys residues and the C3¹ and C3² atoms of BV. When expressed in Synechocystis sp. PCC 6803, DCBs incorporate PCB rather than BV. DCBs can be converted to photoactive cBphP‐, Cph1‐, and tandem‐cysteine cyanobacterial phytochrome (TCCP) analogs by removal and/or addition of a cysteine residue by site‐directed mutagenesis. This structural plasticity contrasts with our inability to generate functional photosensor analogs by analogous site‐directed mutagenesis of TCCP and Cph1 representatives. Phylogenetic analysis demonstrates that DCBs do not form a monophyletic clade and also suggest that Cph1 and TCCP families independently emerged from different lineages of cBphPs, possibly via DCB intermediates.

Throughout the entire cycle of leaf phenological events, leaf colour undergoes changes that are influenced by either abiotic stress or biotic infection. These changes in colouration are closely linked to the quantity and quality of photosynthetic pigments, which directly impact the primary productivity of plants. Therefore, monitoring and quantifying leaf colouration changes are crucial for distinguishing damage caused by pine wilt nematodes from natural tree senescence. In this study, a hyperspectral camera sensor was employed for the non‐invasive and non‐destructive evaluation of needle colour changes in coniferous trees grown in field tests. Three distinct needle colour variations of six coniferous tree species were selected and monitored using a hyperspectral sensor: those displaying seasonal autumn colours, undergoing nematode‐infected necrosis processes, and experiencing natural death. To mitigate the inherently mixed spectral properties of hyperspectral data, endmembers were extracted from individual images using the Purity Pixel Index algorithm under the assumption of linear mixing of endmembers. From a total of 1,321 endmembers extracted from 378 hyperspectral images of six pine species, eight endmembers were ultimately chosen to reconstruct hyperspectral images and generate abundance maps. Among these eight endmembers, four represent varying levels of photosynthetic pigment contents—ranging from very low to high. Consequently, these coniferous endmembers hold promise for assessing seasonal leaf phenology and the extent of damage in pine trees infected by pine wilt nematodes. This comprehensive approach underscores the effectiveness of spectral unmixing of hyperspectral images in advancing precision forestry through meticulous coniferous needle trait analysis.

Key message: Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.

Man-Hyuk Han, Hee Wook Yang, Jungmin Yoon, Yvette Villafani, Ji-Young Song, Cheol Ho Pan, Keunwan Park, Youngmoon Cho, Ji-Joon Song, Seung Joong Kim, Youn-Il Park, and Jiyong Park. Mol. Cells 2023;46:513-25. https://doi.org/10.14348/molcells.2023.2186