ABSTRACT
Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower's planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384-1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky-Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination (R2) of 0.9715 and root mean square error (RMSE) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.
ABSTRACT
Interactions between ß-glucan and starch influence the health benefits of barley-based foods and barley brewing performance. Here, we characterized ß-glucans from waxy and normal barley varieties and compared the effects of different ß-glucans on the pasting and degradation of waxy and normal barley starches as well as the filterability of mashes from unmalted waxy and normal barley. Waxy barley Zangqing18 ß-glucan displayed more compact micrographic features, higher molecular weight, larger particle size, higher thermal decomposition temperature and lower rheological viscosity than normal barley Zangqing2000 ß-glucan. ß-Glucan not only significantly decreased the pasting viscosities of waxy and normal starches but also lowered the pasting temperatures and peak times of normal starch, likely by inhibiting granule swelling and disrupting the integrity of the continuous phase. ß-Glucan also decreased in vitro digestion extent of starch and increased the resistant starch. The unmalted waxy barley had a mash filtration rate much faster than normal barley because starch and ß-glucan in waxy barley were rapidly and completely digested and formed more open filter passages. The effects of ß-glucan on starch properties varied with the types and contents of ß-glucans, whilst the types of starches showed more significant effects. CHEMICAL COMPOUNDS STUDIED: ß-Glucan (Pubchem CID: 439262); Amylopectin (Pubchem CID: 439207); Starch (Pubchem CID: 156595876).
