Suitable crop loading: An effective method to improve "Shine Muscat" grape quality

LC-MS and GC-MS Metabolomics Analyses Revealed That Different Exogenous Substances Improved the Quality of Blueberry Fruits under Soil Cadmium Toxicity

Exogenous substances (ESs) can regulate plant growth and respond to environmental stress, but the effects of different ESs on blueberry fruit quality under soil cadmium (Cd) toxicity and related metabolic mechanisms are still unclear. In this study, four ES treatments [salicylic acid (SA), spermidine (Spd), 2,4-epibrassinolide (EBR), and melatonin (MT)] significantly increased blueberry fruit size, single-fruit weight, sweetness, and anthocyanin content under soil Cd toxicity and effectively reduced fruit Cd content to safe consumption levels by promoting mineral uptake (Ca, Mg, Mn, Cu and Zn). Furthermore, a total of 445, 360, 429, and 554 differentially abundant metabolites (DAMs) (LC-MS) and 63, 48, 79, and 73 DAMs (GC-MS) were identified from four comparison groups (SA/CK, Spd/CK, EBR/CK and MT/CK), respectively. The analyses revealed that ESs improved blueberry fruit quality and tolerance to Cd toxicity mainly by regulating the changes in metabolites related to ABC transporters, the TCA cycle, flavonoid biosynthesis, and phenylpropanoid biosynthesis.

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.