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23 pages, 814 KiB  
Article
Neuroprotective Effects of Myrtle Berry By-Product Extracts on 6-OHDA-Induced Cytotoxicity in PC12 Cells
by Debora Dessì, Giacomo Fais, Paolo Follesa and Giorgia Sarais
Antioxidants 2025, 14(1), 88; https://doi.org/10.3390/antiox14010088 - 13 Jan 2025
Abstract
The rising global focus on healthy lifestyles and environmental sustainability has prompted interest in repurposing plant-based by-products for health benefits. With increasing life expectancy, the incidence of neurodegenerative diseases—characterized by complex, multifactorial mechanisms such as abnormal protein aggregation, mitochondrial dysfunction, oxidative stress, and [...] Read more.
The rising global focus on healthy lifestyles and environmental sustainability has prompted interest in repurposing plant-based by-products for health benefits. With increasing life expectancy, the incidence of neurodegenerative diseases—characterized by complex, multifactorial mechanisms such as abnormal protein aggregation, mitochondrial dysfunction, oxidative stress, and inflammation—continues to grow. Medicinal plants, with their diverse bioactive compounds, offer promising therapeutic avenues for such conditions. Myrtus communis L., a Mediterranean plant primarily used in liquor production, generates significant waste rich in antioxidant and anti-inflammatory properties. This study explores the neuroprotective potential of Myrtus berry by-products in a cellular model of neurodegeneration. Using PC12 cells exposed to 6-hydroxydopamine (6-OHDA), we assessed cell viability via MTT assay and measured reactive oxygen species (ROS) production using DCFDA fluorescence. Additionally, we analyzed the expression of genes linked to oxidative stress and neuronal function, including AChE, PON2, Grin1, Gabrd, and c-fos, by RT-PCR. Our findings reveal that Myrtus extract significantly protects against 6-OHDA-induced cytotoxicity, reduces ROS levels, and modulates the expression of key stress-related genes, underscoring its potential as a neuroprotective agent. These results highlight the therapeutic promise of Myrtus extracts in mitigating neurodegenerative processes, paving the way for future interventions. Full article
36 pages, 13780 KiB  
Article
Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines
by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven and Rebecca Retzlaff
Sensors 2025, 25(2), 431; https://doi.org/10.3390/s25020431 - 13 Jan 2025
Viewed by 105
Abstract
Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely [...] Read more.
Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy). Full article
(This article belongs to the Special Issue Remote Sensing for Crop Growth Monitoring)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>A</b>) Investigation area in Bernkastel-Kues within the Moselle wine region mapped on a high-precision orthomosaic (CRS (Coordinate Reference System) with EPSG (European Petroleum Survey Group) 25,832 ETRS (European Terrestrial Reference System) 89/UTM (Universal Transverse Mercator) zone 32N). Red points represent each vine position that was localized with differential-GPS (see <a href="#sec2dot5-sensors-25-00431" class="html-sec">Section 2.5</a> for more information) (<b>B</b>) Zoomed out view of the investigation area and overview of local vineyard structure and the Moselle river (mapped on Google Earth Satellite map from QuickMapService plugin in QGIS version 3.22) (<b>C</b>) Intermediate zoom of the investigation area, with orthomosaic on Google Earth Satellite map. It can be seen that the UAV- sensor- based orthomosaic and the Google Satellite map show some offset to each other, due to different absolute geographic accuracies and spatial resolution.</p>
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<p>(<b>A</b>) Zoomed-out view of the canopy-free vine rows of the investigation area. (<b>B</b>) Zoomed-in view of the training system in the investigation area (photos taken in December 2024).</p>
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<p>Ground truth template examples with label descriptions for the growth classes after Porten [<a href="#B43-sensors-25-00431" class="html-bibr">43</a>]. The specific visual characteristics and correlations to viticultural, oenological, and environmental parameters are described in detail by [<a href="#B43-sensors-25-00431" class="html-bibr">43</a>].</p>
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<p>Color-coded growth classification after Porten [<a href="#B43-sensors-25-00431" class="html-bibr">43</a>] for single grapevines in the investigation area mapped on multispectral orthomosaic. All geodata are projected to CRS with EPSG: 25,832 ETRS89/UTM zone 32N.</p>
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<p>Visualization of the developed and applied geo- and image processing workflow in this study, with QGIS and different geospatial libraries in Phyton. Geoprocessing was the foundation for further statistical analysis, and machine learning model predictions of the growth classes after [<a href="#B43-sensors-25-00431" class="html-bibr">43</a>].</p>
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<p>Sampling rectangles around the vines’ position for zonal statistics pixel aggregation process, together with growth class categorized grapevine stem positions and vine row extracted OSAVI (OSAVI extracted). All geodata are projected to CRS with EPSG: 25,832 ETRS89/UTM zone 32N.</p>
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<p>Growth class grouped CHM Volume boxplots with significance stars (*) between boxplots generated according to the Mann–Whitney-U-test with <span class="html-italic">p</span>-value significance. ** Signal greater than 0.01 (intermediate significance). *** Signal less than 0.001 (vital significance).</p>
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<p>Input feature group (1–7) grouped boxplot OA (overall accuracy) in % for the SVM classifier of the seven different SVM models (see legend color of grouped boxplots), with significance stars (*) generated according to the Mann–Whitney-U-test between statistical significant model (SVM 1–SVM 7) results, where significant accuracy differences, derived from repeated-k-fold-cross-validation occurred, with <span class="html-italic">p</span>-value significance classes: ** Signal greater than 0.01 (intermediate significance). *** Signal less than 0.001 (vital significance). No stars between boxplots indicate no statistical differences between the model outputs according to Mann-Whitney-U-test.</p>
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<p>Input feature group (1–7) grouped boxplot accuracy in % for the RF classifier of the seven different RF classifier models (see legend color of grouped boxplots), with significance stars (*) generated according to the Mann–Whitney-U-test between statistical significant model (RF 1–RF 7) results, where significant OA (overall accuracy) differences derived from repeated-k-fold-cross-validation occurred, with <span class="html-italic">p</span>-value significance classes: * Signal greater than 0.1 (weak significance).** Signal greater than 0.01 (intermediate significance). *** Signal less than 0.001 (vital significance). No stars between boxplots indicate no statistical differences between the model outputs according to Mann-Whitney-U-test.</p>
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<p>Pairwise statistical comparison of OA (overall accuracy) of the test and train data in % for the SVM classifier of the seven different feature groups input sets (1–7) with significance stars (*) generated according to the Mann–Whitney-U-test between boxplots where significant accuracy differences (OA) derived from repeated-k-fold-cross-validation occurred, with <span class="html-italic">p</span>-value significance classes. * Signal greater than 0.1 (weak significance). *** Signal less than 0.001 (vital significance).</p>
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<p>Pairwise statistical comparison of accuracy in % and f1-weighted score in % of the test data sets for the RF classifier of the seven different input feature groups (see legend color of grouped boxplots) with significance stars (*) generated according to the Mann–Whitney-U-test between accuracy and f1-weighted, with significant differences derived from repeated-k-fold-cross-validation occurred, with <span class="html-italic">p</span>-value significance classes. *** Signal less than 0.001 (vital significance).</p>
Full article ">Figure 12
<p>Pairwise statistical comparison of overall accuracy of train data set in % for the SVM classifier with f1-weighted in score in % of the seven different input feature groups (see legend color of grouped boxplots) with significance stars (*) generated according to the Mann–Whitney-U-test between accuracy and f1-weighted, where significant accuracy differences derived from repeated-k-fold cross- validation occurred, with <span class="html-italic">p</span>-value significance classes. ** Signal greater than 0.01 (intermediate significance). *** Signal less than 0.001 (vital significance). No stars between boxplots indicate no statistical differences between the model outputs according to Mann-Whitney-U-test.</p>
Full article ">Figure 13
<p>Visualization example of the difference between the ground truth growth classes and the model predicted growth classes from the output from SVM 7 model. Red numbers next to grapevine stems (brown points) with values over zero represent an underestimation of the model prediction compared to ground truth data. In contrast, values less than zero would indicate an overestimation of the growth class model prediction, compared to the ground truth data. Zero values indicate a perfect match of the ground truth with the ML model prediction. The red rectangles represent the area of the zonal statistics aggregation, and the red outline the generated vine row mask (see <a href="#sec2dot6dot9-sensors-25-00431" class="html-sec">Section 2.6.9</a>), where the spatial aggregation of the features was achieved. Pixels outside the vine row mask were not considered for spatial aggregation. All mapped geodata are projected to CRS with EPSG: 25,832/ ETRS89/UTM zone 32N.</p>
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38 pages, 1123 KiB  
Review
Proteostasis Decline and Redox Imbalance in Age-Related Diseases: The Therapeutic Potential of NRF2
by Brigitta Buttari, Antonella Tramutola, Ana I. Rojo, Niki Chondrogianni, Sarmistha Saha, Alessandra Berry, Letizia Giona, Joana P. Miranda, Elisabetta Profumo, Sergio Davinelli, Andreas Daiber, Antonio Cuadrado and Fabio Di Domenico
Biomolecules 2025, 15(1), 113; https://doi.org/10.3390/biom15010113 - 13 Jan 2025
Viewed by 164
Abstract
Nuclear factor erythroid 2-related factor 2 (NRF2) is a master regulator of cellular homeostasis, overseeing the expression of a wide array of genes involved in cytoprotective processes such as antioxidant and proteostasis control, mitochondrial function, inflammation, and the metabolism of lipids and glucose. [...] Read more.
Nuclear factor erythroid 2-related factor 2 (NRF2) is a master regulator of cellular homeostasis, overseeing the expression of a wide array of genes involved in cytoprotective processes such as antioxidant and proteostasis control, mitochondrial function, inflammation, and the metabolism of lipids and glucose. The accumulation of misfolded proteins triggers the release, stabilization, and nuclear translocation of NRF2, which in turn enhances the expression of critical components of both the proteasomal and lysosomal degradation pathways. This process facilitates the clearance of toxic protein aggregates, thereby actively maintaining cellular proteostasis. As we age, the efficiency of the NRF2 pathway declines due to several factors including increased activity of its repressors, impaired NRF2-mediated antioxidant and cytoprotective gene expression, and potential epigenetic changes, though the precise mechanisms remain unclear. This leads to diminished antioxidant defenses, increased oxidative damage, and exacerbated metabolic dysregulation and inflammation—key contributors to age-related diseases. Given NRF2’s role in mitigating proteotoxic stress, the pharmacological modulation of NRF2 has emerged as a promising therapeutic strategy, even in aged preclinical models. By inducing NRF2, it is possible to mitigate the damaging effects of oxidative stress, metabolic dysfunction, and inflammation, thus reducing protein misfolding. The review highlights NRF2’s therapeutic implications for neurodegenerative diseases and cardiovascular conditions, emphasizing its role in improving proteostasis and redox homeostasis Additionally, it summarizes current research into NRF2 as a therapeutic target, offering hope for innovative treatments to counteract the effects of aging and associated diseases. Full article
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Figure 1
<p>Schematic overview of the NRF2 interaction mechanisms with the unfold protein response (UPR), the mTOR/autophagy pathways and the ubiquitin-proteasome system (UPS). See details in the text (created with BioRender, Toronto, ON, Canada).</p>
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<p>Schematic overview of the central role of NRF2 in regulating protein homeostasis and redox balance. Green lines describe the interventions of NRF2 in homeostatic mechanisms (created with BioRender, Toronto, ON, Canada).</p>
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24 pages, 3416 KiB  
Article
Vibration–Collision Coupling Modeling in Grape Clusters for Non-Damage Harvesting Operations
by Baocheng Xu, Jizhan Liu, Yucheng Jin, Kaiyu Yang, Shengyi Zhao and Yun Peng
Agriculture 2025, 15(2), 154; https://doi.org/10.3390/agriculture15020154 - 12 Jan 2025
Viewed by 321
Abstract
In the table grape production process, issues such as berry detachment and damage caused by cluster vibrations and berry collisions are significant challenges. To investigate the underlying mechanisms and dynamics of these phenomena, a vibration–collision coupling method for table grape clusters was developed. [...] Read more.
In the table grape production process, issues such as berry detachment and damage caused by cluster vibrations and berry collisions are significant challenges. To investigate the underlying mechanisms and dynamics of these phenomena, a vibration–collision coupling method for table grape clusters was developed. Based on the vibration model of a grape cluster, the smallest vibration–collision coupling unit—referred to as the dual-twig–berry system—was proposed. This system was described using a “(viscoelastic hinge)–(rigid bar)–(flexible sphere)–(viscoelastic link)” model. The dynamic vibration–collision coupling equation of the dual-twig–berry system was derived by incorporating expressions for the viscoelastic vibration of the twigs, viscoelastic collision of the berries, and a generalized collision force (based on the Kelvin model) into the framework of the Lagrange equation. A computational-simulation method for solving this dynamic vibration–collision coupling equation was also developed. The simulation results revealed that the vibration–collision coupling pattern exhibited a shorter vibration period, smaller vibration amplitude, and higher vibration frequency compared to the vibration pattern without coupling. A reduction in vibration amplitude mitigates berry detachment caused by excessive instantaneous loads. However, the increase in vibration frequency exacerbates berry detachment due to fatigue and causes varying degrees of berry damage. This study provides a theoretical foundation for understanding the mechanisms of berry detachment and damage, offering valuable insights for mitigating these issues in table grape production. Full article
(This article belongs to the Special Issue Agricultural Collaborative Robots for Smart Farming)
16 pages, 5517 KiB  
Article
Rubus idaeus RiACS1 Gene Is Involved in Ethylene Synthesis and Accelerates Fruit Ripening in Solanum lycopersicum
by Tiemei Li, Wenjiao Xin, Hang Zhang, Jiarong Jiang, Kunmiao Ding, Mengyu Liu, Nanyan Li and Guohui Yang
Agronomy 2025, 15(1), 164; https://doi.org/10.3390/agronomy15010164 - 10 Jan 2025
Viewed by 314
Abstract
Raspberry is a berry whose fruit is not tolerant to storage; breeding varieties with extended storage time and high comprehensive quality are significant for raspberries in cold regions. 1-Aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) is a limiting enzyme in the ethylene synthesis process, which [...] Read more.
Raspberry is a berry whose fruit is not tolerant to storage; breeding varieties with extended storage time and high comprehensive quality are significant for raspberries in cold regions. 1-Aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS) is a limiting enzyme in the ethylene synthesis process, which plays essential roles in fruit ripening and softening in plants. In this study, the RiACS1 gene in raspberry (Rubus idaeus L.) variety ‘Polka’ was cloned. The RiACS1 gene overexpression vector was constructed and transformed into tomato plants using the Agrobacterium tumefaciens infection method to verify its function in their reproductive development. The RiACS1 gene, with a total length of 1476 bp, encoded a protein with 491 amino acids. The subcellular localization analysis of the RiACS1 protein in the tobacco transient expression system revealed that the RiACS1-GFP fusion protein was mainly located in the nucleus. Compared with the control, the flowering time and fruit color turning time of transgenic strains were advanced, and the fruit hardness was reduced. Overexpression of RiACS1 increased the activity of ACC synthase, ethylene release rate, and respiration rate during the transchromic phase. It changed the substance content, increased the content of vitamin C and anthocyanin in the fruit ripening process, and decreased the content of chlorophyll and titrable acid at the maturity stage. In addition, RiACS1 increased the relative expression levels of ethylene synthesis-related genes such as SlACS4, SlACO3, and SlACO1 in the fruit ripening process, while it decreased the expression levels of SlACS2 at the maturity stage. These results suggested that the RiACS1 gene could promote early flowering and fruit ripening in tomato plants. This study provided a basis for further modifying raspberry varieties using molecular biology techniques. Full article
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Figure 1

Figure 1
<p>Sequence alignment and phylogenetic relationship of RiACS1 protein with other species ACS proteins. (<b>A</b>) Comparison of amino acid sequences of RiACS1 and ACS of other species. The red box contains the seven conserved domains of ACS protein. Identical amino acids are shown in dark blue. Red color means that the amino acid similarity is more than 75%, and green color means that the amino acid similarity is more than 50%. (<b>B</b>) Evolutionary tree of RiACS1 and other ACS proteins. The red dot marks the target protein. The accession numbers are as follows: RrACS1 (XM_062142475.1, Rosa rugosa), RcACS1 (XP_040369294.1, Rosa chinensis), RbACS5 (MH276990.1, Rosa x borboniana), FvACS1 (XM_004306687.2, Fragaria vesca subsp. vesca ), FaACS2 (BK010989.1, Fragaria x ananassa), MdACS1 (XM_008347119.3, Malus domestica), PdACS1 (XM_034349079.1, Prunus dulcis), PaACS1 (XM_021952265.1, Prunus avium), PcACS4 (AF386518.1, Pyrus communis), PpACS1a (KC632526.1, Pyrus pyrifolia), PbACS1 (XM_009368287.3, Pyrus x bretschneideri).</p>
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<p>Subcellular localization of <span class="html-italic">RiACS1</span> in the tobacco leaves. The four channels from left to right are green fluorescent protein (GFP), bright field, DAPI blue nuclear fluorescent dye, and the first three channels superimposed on each other (Merge).</p>
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<p>Expression analysis of the <span class="html-italic">RiACS1</span> gene in different tissues and fruit ripening stages of raspberry. The values of each group were compared with the expression level in the root of the raspberry. Data in the graph are the average and standard errors of three replicate reactions. The asterisk indicates significant differences from the control (** <span class="html-italic">p</span> ⩽ 0.01.).</p>
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<p>Expression of the <span class="html-italic">RiACS1</span> gene in transgenic tomato strains. (<b>A</b>) Phenotype of tomato plants after four weeks of planting. (<b>B</b>) Record of the flowering time of tomato strains. (<b>C</b>) PCR detection of <span class="html-italic">RiACS1</span> transgenic tomato strains (R1–R3), wild type (WT), empty carrier (UL), and pCAMBIA1300-<span class="html-italic">RiACS1</span> positive plasmid (PL). (<b>D</b>) Relative expression level of <span class="html-italic">RiACS1</span> gene. Data in the graph are the average and standard errors of three replicate reactions. The asterisk indicates significant differences from the control (* <span class="html-italic">p</span> ⩽ 0.05, ** <span class="html-italic">p</span> ⩽ 0.01.).</p>
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<p>Phenotype, the relative expression level of <span class="html-italic">RiACS1</span> gene, firmness, the activity of ACC synthase, ethylene release rate, and respiration rate in tomato fruits overexpressing <span class="html-italic">RiACS1</span>. (<b>A</b>) Fruit phenotype at 28 d, 37 d, and 45 d after flowering. (<b>B</b>) Relative expression level of <span class="html-italic">RiACS1</span> gene of tomato fruit overexpressed of RiACS1. (<b>C</b>) Fruit hardness. (<b>D</b>) ACC synthase activity. (<b>E</b>) Fruit ethylene release rate. (<b>F</b>) Fruit respiration rate. Data in the graph are the average and standard errors of three replicate reactions. The asterisk indicates significant differences from the control (* <span class="html-italic">p</span> ⩽ 0.05, ** <span class="html-italic">p</span> ⩽ 0.01.).</p>
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<p>The contents of TA, Vc, anthocyanins, and chlorophyll of tomato fruit overexpressed of <span class="html-italic">RiACS1</span>. (<b>A</b>) Titratable acid content. (<b>B</b>) Vitamin C content. (<b>C</b>) Anthocyanin content. (<b>D</b>) Chlorophyll content. Data in the graph are the average and standard errors of three replicate reactions. The asterisk indicates significant differences from the control (* <span class="html-italic">p</span> ⩽ 0.05, ** <span class="html-italic">p</span> ⩽ 0.01.).</p>
Full article ">Figure 7
<p>Expression analysis of genes related to ethylene synthesis of tomato fruit overexpressed of <span class="html-italic">RiACS1</span>. (<b>A</b>) Relative expression of <span class="html-italic">SlACS2</span> gene. (<b>B</b>) Relative expression of <span class="html-italic">SlACS4</span> gene. (<b>C</b>) Relative expression of <span class="html-italic">SlACO1</span> gene. (<b>D</b>) Relative expression of <span class="html-italic">SlACO3</span> gene. Data in the graph are the average and standard errors of three replicate reactions. The asterisk indicates significant differences from the control (* <span class="html-italic">p</span> ⩽ 0.05, ** <span class="html-italic">p</span> ⩽ 0.01.).</p>
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19 pages, 2542 KiB  
Article
Effect of a Novel Food Rich in Miraculin on the Intestinal Microbiome of Malnourished Patients with Cancer and Dysgeusia
by Julio Plaza-Diaz, Marco Brandimonte-Hernández, Bricia López-Plaza, Francisco Javier Ruiz-Ojeda, Ana Isabel Álvarez-Mercado, Lucía Arcos-Castellanos, Jaime Feliú-Batlle, Thomas Hummel, Samara Palma-Milla and Angel Gil
Nutrients 2025, 17(2), 246; https://doi.org/10.3390/nu17020246 - 10 Jan 2025
Viewed by 425
Abstract
Background/Objectives: Dysgeusia contributes to malnutrition and worsens the quality of life of patients with cancer. Despite the different strategies, there is no effective treatment for patients suffering from taste disorders provided by the pharmaceutical industry. Therefore, we developed a novel strategy for reducing [...] Read more.
Background/Objectives: Dysgeusia contributes to malnutrition and worsens the quality of life of patients with cancer. Despite the different strategies, there is no effective treatment for patients suffering from taste disorders provided by the pharmaceutical industry. Therefore, we developed a novel strategy for reducing side effects in cancer patients by providing a novel food supplement with the taste-modifying glycoprotein miraculin, which is approved by the European Union, as an adjuvant to medical–nutritional therapy. Methods: A pilot randomized, parallel, triple-blind, and placebo-controlled intervention clinical trial was carried out in which 31 malnourished patients with cancer and dysgeusia receiving antineoplastic treatment were randomized into three arms—standard dose of dried miracle berries (DMBs) (150 mg DMB/tablet), high dose of DMBs (300 mg DMB/tablet), or placebo (300 mg freeze-dried strawberry)—for three months. Patients consumed a DMB or placebo tablet before each main meal (breakfast, lunch, and dinner). Using stool samples from patients with cancer, we analyzed the intestinal microbiome via nanopore methodology. Results: We detected differences in the relative abundances of genera Phocaeicola and Escherichia depending on the treatment. Nevertheless, only the Solibaculum genus was more abundant in the standard-dose DMB group after 3 months. At the species level, Bacteroides sp. PHL 2737 presented a relatively low abundance in both DMB groups, whereas Vescimonas coprocola presented a relatively high abundance in both treatment groups after 3 months. Furthermore, a standard dose of DMB was positively associated with TNF-α levels and Lachnoclostridium and Mediterraneibacter abundances, and a high dose of DMB was negatively associated with TNF-α levels and the relative abundance of Phocaeicola. Following the administration of a high dose of DMB, a positive correlation was observed between erythrocyte polyunsaturated fatty acids and the presence of Lachnoclostridium and Roseburia. Additionally, a positive association was identified between Phocaeicola and the acetic acid concentration of feces. There was a negative association between the relative abundance of Phocaeicola and taste perception in the high-dose DMB group. Conclusions: The combination of DMB intake with nutritional treatment and individualized dietary guidance results in positive changes in the intestinal microbiome of patients with cancer and dysgeusia. Changes observed in the intestinal microbiome might contribute to maintaining an appropriate immune response in cancer patients. As the current pilot study included a limited number of participants, further clinical trials on a larger group of patients are needed to draw robust findings. Full article
(This article belongs to the Special Issue The Potential of Gut Microbiota in Cancer)
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Figure 1
<p>Group balances are presented in an overview. The top of the plot indicates that groups of taxa constitute the global balance. Box plots illustrating the distribution of balance scores for the DMB 150 mg (standard dose) and placebo groups (<b>A</b>) and the DMB 300 mg (high dose) and placebo groups (<b>B</b>). On the right, the ROC curve with its AUC value and the density curve are displayed.</p>
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<p>Correlations between the intestinal microbiome, nutritional status, electrical taste perception, and inflammatory cytokines. (<b>A</b>) DMB 150 mg (standard dose), (<b>B</b>) DMB 300 mg (high dose), and (<b>C</b>) placebo.</p>
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<p>Correlations between the intestinal microbiome, nutritional status, electrical taste perception, and inflammatory cytokines. (<b>A</b>) DMB 150 mg (standard dose), (<b>B</b>) DMB 300 mg (high dose), and (<b>C</b>) placebo.</p>
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20 pages, 1044 KiB  
Review
Effect of Antioxidants on the Gut Microbiome Profile and Brain Functions: A Review of Randomized Controlled Trial Studies
by Aleksandra Hyży, Hanna Rozenek, Ewa Gondek and Mariusz Jaworski
Foods 2025, 14(2), 176; https://doi.org/10.3390/foods14020176 - 8 Jan 2025
Viewed by 317
Abstract
Background: Antioxidants are widely recognized for their potential health benefits, including their impact on cognitive function and gut microbiome modulation. Understanding these effects is essential for exploring their broader clinical applications. Objectives: This review aims to evaluate the effects of antioxidants on the [...] Read more.
Background: Antioxidants are widely recognized for their potential health benefits, including their impact on cognitive function and gut microbiome modulation. Understanding these effects is essential for exploring their broader clinical applications. Objectives: This review aims to evaluate the effects of antioxidants on the gut microbiome and cognitive function, with a focus on findings from randomized controlled trials (RCTs). Methods: The studies involved human participants across a range of age groups, with interventions encompassing natural antioxidant sources, such as berries, as well as specific antioxidant vitamins. An extensive search across PubMed, SCOPUS, and Web of Science databases identified six relevant RCTs, each evaluated for potential bias. Results: These studies focused on a variety of antioxidant-rich products, including both naturally derived sources and supplemental forms. Antioxidants, including vitamins C, B2, and D, along with polyphenols such as xanthohumol, fermented papaya, peanuts, and berry extracts, demonstrate the potential to support cognitive function and promote gut health through mechanisms that modulate microbiome diversity and reduce inflammation. However, observed changes in microbiome diversity were modest and inconsistent across the studies. Conclusions: While preliminary evidence suggests that antioxidants may benefit gut health and cognitive function, the heterogeneity of existing studies limits their immediate clinical applicability. Additionally, more robust RCTs are needed to substantiate these findings and guide future interventions. Full article
(This article belongs to the Special Issue Feature Review on Plant Foods)
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<p>PRISMA flow diagram of studies selected.</p>
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<p>Antioxidants, microbiome, and cognition.</p>
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18 pages, 2307 KiB  
Article
Copolyamide-Based Modified Atmosphere Packaging Attenuates Phenolic Degradation and Maintains Postharvest Quality of Rubus Berries
by Hafiz Muhammad Shoaib Shah, Zora Singh, Mahmood Ul Hasan, Eben Afrifa-Yamoah and Andrew Woodward
Horticulturae 2025, 11(1), 47; https://doi.org/10.3390/horticulturae11010047 - 6 Jan 2025
Viewed by 422
Abstract
The highly perishable nature of Rubus berries, particularly their susceptibility to water loss and earlier senescence, significantly limits their shelf life. In this study, we investigated the mechanistic role of modified atmosphere packaging (MAP) on the physiochemical quality, phenolic metabolism, and antioxidant potential [...] Read more.
The highly perishable nature of Rubus berries, particularly their susceptibility to water loss and earlier senescence, significantly limits their shelf life. In this study, we investigated the mechanistic role of modified atmosphere packaging (MAP) on the physiochemical quality, phenolic metabolism, and antioxidant potential of blackberries and raspberries during cold storage (2 ± 1 °C) for 12 and 10 days, respectively. Modified atmosphere (MA)-packed Rubus berries exhibited higher total phenolics accompanied by higher activities of shikimate dehydrogenase, and phenylalanine ammonia-lyase. Furthermore, MA-packed Rubus berries demonstrated lower hydrogen peroxide by maintaining higher catalase activity and delayed lipid peroxidation during the entire period of cold storage. Relatively higher levels of glutathione and ascorbic acid as well as the activities of enzymes involved in the ascorbate-glutathione cycle in MA-packed Rubus berries were also observed. Conversely, MAP reduced the respiration rate and weight loss while maintaining higher postharvest quality attributes in raspberries and blackberries than control fruit. In conclusion, MAP is an effective method for extending the cold storage life and maintaining the quality of Rubus berries. Full article
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<p>Effects of modified atmosphere packaging on respiration rate (<b>A</b>,<b>B</b>) and weight loss (<b>C</b>,<b>D</b>) in cold-stored blackberries and raspberries; n = 3 replicates. Different letters at intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on total phenolics (<b>A</b>,<b>B</b>) and activities of phenylalanine ammonia lyase (<b>C</b>,<b>D</b>) and shikimate dehydrogenase (<b>E</b>,<b>F</b>) in cold-stored blackberries and raspberries; n = 3 replicates. Different letters at the different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on hydrogen peroxide (<b>A</b>,<b>B</b>) and activities of superoxide dismutase (<b>C</b>,<b>D</b>) and catalase (<b>E</b>,<b>F</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on ascorbic acid level (<b>A</b>,<b>B</b>) and activities of ascorbate peroxidase (<b>C</b>,<b>D</b>), dehydroascorbate reductase (<b>E</b>,<b>F</b>), and monodehydroascorbate reductase (<b>G</b>,<b>H</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at respective intervals express significant differences between MAP and control fruit by LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on glutathione level (<b>A</b>,<b>B</b>) and activities of glutathione peroxidase (<b>C</b>,<b>D</b>) and glutathione reductase (<b>E</b>,<b>F</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effect of modified atmosphere packaging on total antioxidants (<b>A</b>,<b>B</b>) polyphenol oxidase activity (<b>C</b>,<b>D</b>), TBARS (<b>E</b>,<b>F</b>) and lipoxygenase activity (<b>G</b>,<b>H</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on soluble solid content (<b>A</b>,<b>B</b>), titratable acidity (<b>C</b>,<b>D</b>), and SSC/TA ratio (<b>E</b>,<b>F</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Effects of modified atmosphere packaging on total anthocyanins (<b>A</b>,<b>B</b>) and total flavonoids (<b>C</b>,<b>D</b>) in cold-stored blackberries and raspberries. n = 3 replicates. Different letters at different intervals express significant differences between MAP and control fruit per LSD test at <span class="html-italic">p</span> &lt; 0.05.</p>
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23 pages, 6551 KiB  
Article
Anomalous Polarization in One-Dimensional Aperiodic Insulators
by Anouar Moustaj, Julius Krebbekx and Cristiane Morais Smith
Condens. Matter 2025, 10(1), 3; https://doi.org/10.3390/condmat10010003 - 6 Jan 2025
Viewed by 280
Abstract
Multilevel charge pumping is a feature that was recently observed in quasiperiodic systems. In this work, we show that it is more generic and appears in different aperiodic systems. Additionally, we show that for aperiodic systems admitting arbitrarily long palindromic factors, the charge [...] Read more.
Multilevel charge pumping is a feature that was recently observed in quasiperiodic systems. In this work, we show that it is more generic and appears in different aperiodic systems. Additionally, we show that for aperiodic systems admitting arbitrarily long palindromic factors, the charge pumping protocol connects two topologically distinct insulating phases. This confirms the existence of topological phases in aperiodic systems whenever their finite-size realizations admit inversion symmetry. These phases are characterized by an anomalous edge response resulting from the bulk–boundary correspondence. We show that these signatures are all present in various chains, each representing a different class of structural aperiodicity: the Fibonacci quasicrystal, the Tribonacci quasicrystal, and the Thue–Morse chain. More specifically, we calculate three quantities: the Berry phase of the periodic approximation of the finite-size systems, the polarization response to an infinitesimal static and constant electric field in systems with open boundary conditions, and the degeneracy of the entanglement spectrum. We find that all of them provide signatures of a topologically nontrivial phase. Full article
(This article belongs to the Section Condensed Matter Theory)
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<p>Equivalence of the topological phase diagram of the periodic Rice–Mele model, at half-filling, between (<b>a</b>) the Chern number formulation and (<b>b</b>) the Bott index formulation. The red regions contain a Chern (Bott) number of <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> and the blue regions of <math display="inline"><semantics> <mrow> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>.</p>
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<p>Top row: Time evolution of the Berry phases of each model studied under a pumping cycle. Vertical dashed lines indicate points when <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (blue) or <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mi>π</mi> </mrow> </semantics></math> (red). Bottom row: Behavior of the eigenvalues under adiabatic time evolution. The colors indicate the localization behavior of each mode, with grey denoting bulk modes and red (blue) indicating localization on the right (left) of the chain. The green shaded area indicates the bulk gap for which the Berry phase was calculated. (<b>a</b>,<b>b</b>) Periodic Rice–Mele. (<b>c</b>,<b>d</b>) Fibonacci Rice–Mele. (<b>e</b>,<b>f</b>) Tribonacci Rice–Mele. (<b>g</b>,<b>h</b>) Thue–Morse Rice–Mele. System sizes are (<b>a</b>,<b>b</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, (<b>c</b>,<b>d</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>55</mn> </mrow> </semantics></math>, (<b>e</b>,<b>f</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>44</mn> </mrow> </semantics></math>, and (<b>g</b>,<b>h</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>64</mn> </mrow> </semantics></math>.</p>
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<p>Cumulative charge pumped as a function of time. (<b>a</b>) The periodic modulation at half-filling. (<b>b</b>) The Fibonacci modulation at fillings <math display="inline"><semantics> <mi>τ</mi> </semantics></math> (red), <math display="inline"><semantics> <msup> <mi>τ</mi> <mn>3</mn> </msup> </semantics></math> (green), and <math display="inline"><semantics> <msup> <mi>τ</mi> <mn>6</mn> </msup> </semantics></math> (blue), with <math display="inline"><semantics> <mrow> <mi>τ</mi> <mo>=</mo> <mo>(</mo> <msqrt> <mn>5</mn> </msqrt> <mo>−</mo> <mn>1</mn> <mo>)</mo> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>, the inverse of the golden ratio. (<b>c</b>) The Tribonacci modulation at fillings <math display="inline"><semantics> <mi>β</mi> </semantics></math> (red), <math display="inline"><semantics> <msup> <mi>β</mi> <mn>3</mn> </msup> </semantics></math> (green), and <math display="inline"><semantics> <msup> <mi>β</mi> <mn>4</mn> </msup> </semantics></math> (blue), with <math display="inline"><semantics> <mrow> <msup> <mi>β</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>≈</mo> <mn>1.8393</mn> </mrow> </semantics></math>, the real root of the cubic equation <math display="inline"><semantics> <mrow> <msup> <mi>x</mi> <mn>3</mn> </msup> <mo>−</mo> <msup> <mi>x</mi> <mn>2</mn> </msup> <mo>−</mo> <mi>x</mi> <mo>−</mo> <mn>1</mn> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, also called the Tribonacci constant. (<b>d</b>) The Thue–Morse modulation at fillings 1/3 (red), 1/10 (green), and 1/27 (blue). For the aperiodic models, the behavior tends to a step-like function for smaller fillings, which is a feature of multilevel charge pumping. System sizes are (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>55</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>81</mn> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>256</mn> </mrow> </semantics></math>.</p>
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<p>Phase diagram of the inversion-symmetric realizations of aperiodic chains. The generations chosen are (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>9</mn> </mrow> </semantics></math> for the Fibonacci chain, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> for the Tribonacci chain, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math> for the Thue–Morse chain. The hopping parameter has been set to <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> for the Fibonacci and Tribonacci chains, while it has been set to <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo stretchy="false">˜</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> for the Thue–Morse chain to reflect the phase achieved in the pumping cycle shown in <a href="#condensedmatter-10-00003-f002" class="html-fig">Figure 2</a>h.</p>
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<p>Energy and polarization of the SSH chain. (<b>a</b>) The OBC spectrum of the SSH chain in the topological phase: two degenerate edge states are pinned at the Fermi energy. (<b>b</b>) The OBC spectrum in the trivial phase: all states belong to the bulk. (<b>c</b>) The two different polarization responses, in the trivial phase (blue) and in the topological phase (red). The dimerization parameter in Equation (<a href="#FD1-condensedmatter-10-00003" class="html-disp-formula">1</a>) has been set to <math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mo>±</mo> <mn>0.5</mn> </mrow> </semantics></math> [<math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>δ</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math>): topological (trivial) phase].</p>
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<p>Polarization response of the aperiodic chains. OBC spectra (in the nontrivial phase) and polarization responses of the (<b>a</b>,<b>b</b>) Fibonacci chain, (<b>c</b>,<b>d</b>) Tribonacci chain, and (<b>e</b>,<b>f</b>) Thue–Morse chain. The fillings are chosen such that the Fermi energies (light blue lines) lie in the largest gap in each case. In all cases, the polarization exhibits an anomalous behavior around <math display="inline"><semantics> <mrow> <mi mathvariant="script">E</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, with a jump from <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>0.5</mn> </mrow> </semantics></math> caused by the eigenstates colored in red in (<b>a</b>,<b>c</b>,<b>e</b>) whenever the system is in the <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mi>π</mi> </mrow> </semantics></math> phase. On the other hand, the polarization does not show a sudden jump in the <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> phase (in blue). The system sizes are <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msub> <mi>F</mi> <mn>14</mn> </msub> <mo>−</mo> <mn>2</mn> <mo>=</mo> <mn>608</mn> </mrow> </semantics></math> for the Fibonacci chain, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msub> <mi>T</mi> <mn>11</mn> </msub> <mo>−</mo> <mrow> <mo>|</mo> <msub> <mi>E</mi> <mn>11</mn> </msub> <mo>|</mo> </mrow> <mo>=</mo> <mn>325</mn> </mrow> </semantics></math> for the Tribonacci chain, and <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <msup> <mn>2</mn> <mn>8</mn> </msup> <mo>=</mo> <mn>256</mn> </mrow> </semantics></math> for the Thue–Morse chain. The number of unit cells was fixed at 1, corresponding to a single aperiodic cell.</p>
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<p>First few eigenvalues of the ES plotted against the total particle number of the eigenvalue configuration. The first row indicates all the trivial realizations of the inversion-symmetric chains, and the lower one indicates all the topological realizations. (<b>a</b>,<b>b</b>) The SSH chain. (<b>c</b>,<b>d</b>) The Fibonacci chain. (<b>e</b>,<b>f</b>) The Tribonacci chain. (<b>g</b>,<b>h</b>) The Thue–Morse chain. A red bar indicates at least double degeneracy at the given particle number, while a blue bar is a single occurrence of the eigenvalue particle number <span class="html-italic">n</span>. However, an eigenvalue <math display="inline"><semantics> <msub> <mi>ξ</mi> <mi>α</mi> </msub> </semantics></math> can also appear for a different particle number configuration, yielding another type of degeneracy (see <a href="#app2-condensedmatter-10-00003" class="html-app">Appendix B</a> for more details). The system sizes chosen are consistent with those used in <a href="#condensedmatter-10-00003-f006" class="html-fig">Figure 6</a>. Note that for the Fibonacci chain, the eigenvalues exhibit quasi-degeneracy due to finite-size effects. As the system size increases, this degeneracy becomes more pronounced.</p>
Full article ">Figure A1
<p>Berry phase and level crossing for the Fibonacci chain. Each column corresponds to a different choice of a unit cell, where the number above each column represents the fraction of the length of the Fibonacci word used as a starting point to generate the unit cell. (<b>a</b>,<b>b</b>): The unit cell starts at the beginning of the chain, represented by a distance 0. (<b>c</b>,<b>d</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. (<b>e</b>,<b>f</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>g</b>,<b>h</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. The green shaded area in the bottom row represents the chosen bulk gap for the Berry phase calculation. The system size is <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>55</mn> </mrow> </semantics></math>. The red (blue) dashed lines mark the times when <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mi>π</mi> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>).</p>
Full article ">Figure A2
<p>Berry phase and level crossing for the Tribonacci chain. (<b>a</b>,<b>b</b>): The unit cell starts at the beginning of the chain, represented by a distance 0. (<b>c</b>,<b>d</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. (<b>e</b>,<b>f</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>g</b>,<b>h</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. The green shaded area in the bottom row represents the chosen bulk gap for the Berry phase calculation. The system size is <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>44</mn> </mrow> </semantics></math>.</p>
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<p>Berry phase and level crossing for the Thue–Morse chain. (<b>a</b>,<b>b</b>): The unit cell starts at the beginning of the chain, represented by a distance 0. (<b>c</b>,<b>d</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. (<b>e</b>,<b>f</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>. (<b>g</b>,<b>h</b>): The unit cell starts at the beginning of the chain, represented by a distance <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>. The green shaded area in the bottom row represents the chosen bulk gap for the Berry phase calculation. The system size is <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>32</mn> </mrow> </semantics></math>.</p>
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19 pages, 1488 KiB  
Article
Frozen Fermented Dairy Snacks with Probiotics and Blueberry Bagasse: Stability, Bioactivity, and Digestive Viability
by Alejandra Hurtado-Romero, Andrea Zepeda-Hernández, Javier Cárdenas-Rangel, Ricardo Aguilar-Márquez, Luis Eduardo Garcia-Amezquita, Danay Carrillo-Nieves and Tomás García-Cayuela
Microorganisms 2025, 13(1), 86; https://doi.org/10.3390/microorganisms13010086 - 4 Jan 2025
Viewed by 485
Abstract
The demand for healthier snack options has driven innovation in frozen dairy products. This study developed and characterized novel frozen dairy snacks fermented with probiotics (Lactobacillus acidophilus LA5; Lacticaseibacillus rhamnosus GG, and Streptococcus thermophilus BIOTEC003) and containing 2% blueberry bagasse. Four formulations [...] Read more.
The demand for healthier snack options has driven innovation in frozen dairy products. This study developed and characterized novel frozen dairy snacks fermented with probiotics (Lactobacillus acidophilus LA5; Lacticaseibacillus rhamnosus GG, and Streptococcus thermophilus BIOTEC003) and containing 2% blueberry bagasse. Four formulations (LA5, LGG, LA5-BERRY, and LGG-BERRY) were analyzed for their nutritional, physicochemical, functional, and sensory properties. High protein content (>17% d.w.) and increased dietary fiber (5.77–5.88% d.w.) were observed in bagasse-containing formulations. Stable technological characteristics were maintained, with melting rates increasing slightly during storage. Probiotic viability remained high (>8.5 log CFU/mL) after freezing and storage at −20 °C for 30 days. Post-simulated digestion, probiotics retained >7.5 log CFU/mL, while blueberry bagasse formulations exhibited significantly higher phenolic content (7.62–8.74 mg/g d.w.) and antioxidant capacity, though anthocyanin content decreased (66–68%). Sensory evaluation by 100 panelists revealed high acceptance scores (>63%), with LGG-BERRY achieving the highest score (78%). These formulations demonstrate significant potential for incorporating probiotics and functional ingredients, providing an innovative solution for probiotic delivery and the sustainable utilization of fruit by-products in the food industry. Full article
(This article belongs to the Special Issue Probiotic Bacteria in Fermented Foods)
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<p>Experimental sequence for developing and evaluating the frozen dairy snacks fermented with probiotics and containing blueberry bagasse.</p>
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<p>Physicochemical parameters (<b>A</b>), viscosity; (<b>B</b>), melting rate; (<b>C</b>), pH; and (<b>D</b>), titratable acidity) of frozen fermented dairy snack formulations immediately on the same day they were prepared (day 0), one day after freezing (day 1), and after 30 days of storage at −20 °C (day 30). Capital letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among days of storage, and lower-case letters indicate significant differences among formulations. The error bars indicate the standard deviation. Frozen snack formulations are described in <a href="#microorganisms-13-00086-t001" class="html-table">Table 1</a>.</p>
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<p>Viability (log CFU/mL) of probiotics <span class="html-italic">Lactobacillus acidophilus</span> LA5 and <span class="html-italic">Lacticaseibacillus rhamnosus</span> GG in MRS medium (<b>A</b>) and <span class="html-italic">Streptococcus thermophilus</span> BIOTEC003 in L-M17 medium (<b>B</b>) within frozen fermented dairy snack formulations before and after in vitro digestion (intestinal phase) on day 1 and day 30 during storage at −20 °C. Error bars indicate the standard deviation. Capital letters represent significant differences before and after in vitro digestion within each formulation. No significant differences were observed between storage days (<span class="html-italic">p</span> &lt; 0.05). Frozen snack formulations are described in <a href="#microorganisms-13-00086-t001" class="html-table">Table 1</a>. CFU, Colony Forming Units.</p>
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<p>Sensory properties of frozen fermented dairy snack formulations were evaluated based on the acceptability index using a 9-point hedonic scale. Capital letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05) among formulations. The error bars indicate the standard deviation. Frozen snack formulations are described in <a href="#microorganisms-13-00086-t001" class="html-table">Table 1</a>.</p>
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17 pages, 2691 KiB  
Article
Phytochemical Profile Screening and Selected Bioactivity of Myrtus communis Berries Extracts Obtained from Ultrasound-Assisted and Supercritical Fluid Extraction
by Ilir Mërtiri, Gigi Coman, Mihaela Cotârlet, Mihaela Turturică, Nicoleta Balan, Gabriela Râpeanu, Nicoleta Stănciuc and Liliana Mihalcea
Separations 2025, 12(1), 8; https://doi.org/10.3390/separations12010008 - 3 Jan 2025
Viewed by 399
Abstract
This research paper investigates the phytochemical profile, antioxidant activity, antidiabetic potential, and antibacterial activity of Myrtus communis berries. Two extraction methods were employed to obtain the extracts: solid–liquid ultrasound-assisted extraction (UAE) and supercritical fluid extraction (SFE). The extracts were characterized using spectrophotometric methods [...] Read more.
This research paper investigates the phytochemical profile, antioxidant activity, antidiabetic potential, and antibacterial activity of Myrtus communis berries. Two extraction methods were employed to obtain the extracts: solid–liquid ultrasound-assisted extraction (UAE) and supercritical fluid extraction (SFE). The extracts were characterized using spectrophotometric methods and Reversed-Phase High-Performance Liquid Chromatography (RP-HPLC). The UAE extract exhibited higher total flavonoid and anthocyanin content, while the SFE extract prevailed in total phenolic content and antioxidant activity in the DPPH radical screening assay. RP-HPLC characterization identified and quantified several polyphenolic compounds. In the UAE extract, epigallocatechin was found in a concentration of 2656.24 ± 28.15 µg/g dry weight (DW). In the SFE extract, cafestol was the identified compound with the highest content at a level of 29.65 ± 0.03 µg/g DW. Both extracts contained several anthocyanin compounds, including cyanidin 3-O-glucoside chloride, cyanidin-3-O-rutinoside chloride, malvidin-3-O-glucoside chloride, pelargonidin 3-O-glucoside chloride, peonidin 3-O-glucoside chloride, and peonidin-3-O-rutinoside chloride. The antidiabetic potential was evaluated in vitro by measuring the inhibition of α-amylase from porcine pancreas (type I-A). The results highlighted the ability of myrtle berry extracts to inhibit α-amylase enzymatic activity, suggesting its potential as an alternative for controlling postprandial hyperglycemia. The UAE extract showed the lowest IC50 value among the two extracts, with an average of 8.37 ± 0.52 µg/mL DW. The antibacterial activity of the extracts was assessed in vitro against Bacillus spp., Escherichia coli, and Staphylococcus aureus using the disk diffusion method. Both myrtle berry extracts exhibited similar antibacterial activity against the tested bacterial strains. The results support further investigation of myrtle berries extracts as a potential ingredient in functional food formulation, particularly due to its antioxidant, antidiabetic, and antibacterial properties. Full article
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<p>Chromatograms of wild myrtle berries extracts. UAE extract: (<b>a</b>) 280 nm; 2—gallic acid; 5—epicatechin; 8—ferulic acid; 10—synapic acid; 1, 3, 4, 6, 7, 9, 11–17—unidentified compounds. (<b>b</b>) 520 nm; 2—gallic acid; 7—kuromanin chloride; 8—synapic acid; 10—naringin; 11—rutin trihydrate; 12—peonidin-3-<span class="html-italic">O</span>-rutinoside chloride; 15—quercetin; 1, 3–6, 9, 13, 14, 16—unidentified compounds. SFE extract: (<b>c</b>) 280 nm; 1—cafestol; 2—gallic acid; 7—ferulic acid; 16—quercetin; 3–6, 8–15—unidentified compounds. (<b>d</b>) 520 nm; 4—kuromanin chloride; 5—callistephin chloride; 9—oenin chloride; 1–3, 6–8, 10–17—unidentified compounds. (<b>e</b>) 450 nm: 8—zeaxanthin; 1–7, 9–11—unidentified compounds.</p>
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<p>Antibacterial results of disk diffusion method against the tested bacterial strains. The samples codes represent the following: UAE—myrtle berries extract from ultrasound-assisted extraction; SFE—myrtle berries extract extract from supercritical fluid extraction; CC1—ciprofloxacin (positive control); CS—solubilization solvent (negative control).</p>
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21 pages, 2156 KiB  
Article
Valorization of Betalain Pigments Extracted from Phytolacca americana L. Berries as Natural Colorant in Cheese Formulation
by Ionuț Dumitru Veleșcu, Ioana Cristina Crivei, Andreea Bianca Balint, Vlad Nicolae Arsenoaia, Alexandru Dragoș Robu, Florina Stoica and Roxana Nicoleta Rațu
Agriculture 2025, 15(1), 86; https://doi.org/10.3390/agriculture15010086 - 2 Jan 2025
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Abstract
In response to consumer demand for more sustainable and health-conscious products, the food sector is increasingly shifting towards the use of natural additives. Pokeweed (Phytolacca americana L.) is a medicinal plant that contains valuable biologically active compounds, including betacyanins, which serve as [...] Read more.
In response to consumer demand for more sustainable and health-conscious products, the food sector is increasingly shifting towards the use of natural additives. Pokeweed (Phytolacca americana L.) is a medicinal plant that contains valuable biologically active compounds, including betacyanins, which serve as its red pigments, along with phenolic acids, flavonoids, polyphenolic compounds, and others. Phytolacca americana (P. americana) is a plant renowned for its bioactive compounds, which exhibit anti-inflammatory, anti-mutagenic, antioxidant, anticancer, and antibacterial properties. This study investigates the potential of betalain pigments extracted from the berries of P. americana as a natural colorant for cheese formulation. The impact of these pigments on the color attributes, sensory qualities, and physicochemical and phytochemical composition of the cheeses was systematically evaluated. The Phytolacca americana (PA) powder demonstrated significant levels of total polyphenols (111.95 ± 1.60 mg GAE/g dw) and antioxidant activity (21.67 ± 0.19 µmol TE/g dw). The incorporation of PA powder increased the physicochemical and phytochemical contents and antioxidant activity in the final product (4.40 ± 0.22 µmol TE/g dw for CPAP1 and 6.11 ± 0.22 µmol TE/g dw for CPAP2). The sensory study revealed that the PA-supplemented cheeses were acceptable. The enhanced cheeses present a distinctive color profile, attracting health-conscious consumers looking for innovative dairy products. The study concludes that PA powder can effectively enhance cheese, producing a phytochemical-enriched product that appeals to health-conscious consumers. Full article
(This article belongs to the Special Issue Quality Assessment and Processing of Farm Animal Products)
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<p>(<b>A</b>) <span class="html-italic">P. americana</span> plant, (<b>B</b>) <span class="html-italic">P. americana</span> fruits, and (<b>C</b>) <span class="html-italic">P. americana</span> powder.</p>
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<p>Technological flow of cheese manufacturing.</p>
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<p>Spider diagrams corresponding to the descriptive sensory analysis of the control and enhanced semi-hard cheeses (control (CC), cheese with 1% PA (CPAP1), and cheese with 2% PA (CPAP2)).</p>
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<p>Principal components analysis (PCA) of the sensory attributes of the control and enhanced semi-hard cheeses (control (CC), cheese with 1% PA (CPAP1), and cheese with 2% PA (CPAP2)).</p>
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<p>Images of the control and enhanced semi-hard cheeses. (<b>a</b>) Control (CC), (<b>b</b>) cheese with 1%PA (CPAP1), and (<b>c</b>) cheese with 2%PA (CPAP2).</p>
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18 pages, 3170 KiB  
Article
Aroma Analysis of Table Grape Berries Based on Electronic Nose Detection
by Shengyang Niu, Xuewei Liu, Meiling Lin, Xiucai Fan, Ying Zhang, Lei Sun, Chonghuai Liu and Jianfu Jiang
Agronomy 2025, 15(1), 104; https://doi.org/10.3390/agronomy15010104 - 1 Jan 2025
Viewed by 613
Abstract
In this study, the aroma of 182 table grapes was detected using a PEN3.5 electronic nose in order to explore the aroma components of table grape berries and provide a reference for aroma evaluation and quality improvements. Table grape varieties from the Zhengzhou [...] Read more.
In this study, the aroma of 182 table grapes was detected using a PEN3.5 electronic nose in order to explore the aroma components of table grape berries and provide a reference for aroma evaluation and quality improvements. Table grape varieties from the Zhengzhou Fruit Research Institute of the Chinese Academy of Agricultural Sciences were used as research materials. All of them were harvested in fruit trees over 10 years old from August to October 2023, which provided a reference for aroma evaluation and quality improvement of the table grapes. Radar analysis, correlation analysis, principal component (PCA) analysis, cluster analysis, and difference analysis were used to study these aroma substances. The results show that the sensor contribution rate from high to low is W5S (nitrogen oxides), W2S (alcohols and some aromatic compounds), W1S (alkanes), and W2W (sensor contribution rate from high to low). Cluster analysis can distinguish the varieties of table grapes a with common aroma content, and the varieties with a higher content are in the second category (II). PCA showed that the contribution rate of the first and second principal components of the three main sensors was 97.6% and 2.3%, respectively, and the total contribution value was 99.9%. The contribution rates of the first and second principal components of the three aromatic sensors are 79.5% and 15.9%, respectively, and the total contribution value is 95.4%. The results showed that there were significant differences in the content and composition of aroma substances in different grape varieties. Eight special germplasm with strong aroma (organic compounds of nitrogen oxides, alcohols, alkanes and sulfur) were selected: ‘Spabang’, ‘Neijingxiang’, ‘Zaotian Muscat’, ‘Jinmeigui’, ‘Zhengguo 6’, ‘Muscat Angel’, ‘Zizao’, and ‘Qiumi’. This study confirmed that electronic nose technology can effectively distinguish different varieties of table grapes. This study not only provides a scientific basis for the variety selection for the table grape processing industry, but it can also be used for male or female grape hybridization, which provides valuable data resources for table grape breeding. Full article
(This article belongs to the Special Issue Postharvest Physiology of Fruits and Vegetables—2nd Edition)
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<p>Sensor response to aroma of “cardinal” table grape variety.</p>
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<p>Radar map of electronic nose response values of different grape varieties. Note: (<b>A</b>): <span class="html-italic">V. vinifera</span> × <span class="html-italic">V. labrusca</span>; (<b>B</b>): <span class="html-italic">V. vinifera</span>.</p>
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<p>The cluster analysis of 182 table grape germplasm was carried out based on 10 sensor response values. Note: green is the first class (I); red and blue are the second class (II).</p>
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<p>PCA of response values of three main sensors.</p>
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<p>PCA of response values of three aromatic sensors.</p>
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<p>Comparison of response values of the three main sensors. Note: (<b>A</b>): W5S sensor response value; (<b>B</b>): W2S sensor response value; (<b>C</b>): W1S sensor response value.</p>
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<p>Comparison of response values of three aromatic sensors. Note: (<b>A</b>): W1C sensor response value; (<b>B</b>): W3S sensor response value; (<b>C</b>): W2W Sensor response value.</p>
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15 pages, 3077 KiB  
Article
New Approaches in Viticulture: Different Rates of Net Shadow Applications to Yield, Must, Color and Wine Quality
by Tuba Uzun Bayraktar
Horticulturae 2025, 11(1), 21; https://doi.org/10.3390/horticulturae11010021 - 1 Jan 2025
Viewed by 287
Abstract
This study was conducted on the Sinceri grape variety in 2023. Three applications (35%, 55% and 75% net shadows) and a control were applied in the experiment. The shading materials were covered over the vines when the grapes were at veraison. The effects [...] Read more.
This study was conducted on the Sinceri grape variety in 2023. Three applications (35%, 55% and 75% net shadows) and a control were applied in the experiment. The shading materials were covered over the vines when the grapes were at veraison. The effects of the applied net shadows on the grape yield, color parameters of the berry skin and physicochemical analyses in the must were examined. In addition, some chemical analyses [such as pH, ethyl alcohol (%), volatile acidity (mg/L), reducing sugar (g/L), density and total acidity (g/L)], secondary metabolites, color parameters and sensory analyses were performed in wines produced spontaneously by the microvinification method. In terms of the yield parameters, the 55% net shadow application provided a 61.93% increase in grape yield, 37.83% increase in cluster weight and 35.76% increase in 100-berries weight compared to the control. While the L* value and Hue angle increased, the a* value, b* value and Chroma value decreased as the proportion of shading material increased. In the must, the must yield and total acidity increased while the TSSC, maturity index and density decreased. In terms of the physicochemical wine analysis (ethyl alcohol, density, total phenolic compound and antioxidant amount) and sensory evaluations, the best result was given by 75% net shadow and, in terms of wine color parameters, by 55% net shadow application. Full article
(This article belongs to the Section Viticulture)
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<p>Satellite image of the experimental vineyard (Google Earth, 2023).</p>
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<p>Microvinification process in laboratory conditions.</p>
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<p>Gallic acid standard graph.</p>
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<p>Trolox standard graph (ABTS method).</p>
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<p>Trolox standard graph (DPPH method).</p>
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<p>Effects of nets on L*, a*, b*, C* and Hue angle values in berry skin. The differences between the averages were indicated by separate letters. LSDL *: 1.689; VCL *: 8.48; LSDa ***: 0.234; VCa *: 11.87; LSDb **: 0.701; VCb *: 12.90; LSDC *: 0.663; VCC *: 11.46; LSDh°: 6.342 *; VCh°: 3.06. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of net shadows on total phenolic compound and total antioxidant amounts in wine. The differences between the averages were indicated by separate letters. LSDtotal phenolic compound: 0.009 ***; VCtotal phenolic compound: 2.03; LSDtotal antioxidant with ABTS Method: 0.171 **; VCtotal antioxidant with ABTS Method: 8.94; LSDTotal Antioxidant with DPPH Method: 0.018 *; VCTotal Antioxidant with DPPH Method: 9.80. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of net shadows on L*, a*, b*, C* and Hue angle values in wine. The differences between the averages were indicated by separate letters. Lines on columns were standard deviations. LSDL *: 1.857 *; VCL *: 7.15; LSDa *: 0.283 ***; VCa *: 8.85; LSDb *: 0.677 **; VCb *: 4.17; LSDC *: 0.704 **; VCC *: 4.25; LSDh°: 1.363; VC h°: 0.72. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Effects of net shadows on organoleptic (sensory) analysis of wines. The differences between the averages were indicated by separate letters. LSDColor: 0.230 *; VCColor: 7.06; LSDClarity: 0.151 **; VCClarity: 4.55; LSDBouquet: 0.263 ***; VCBouquet: 4.54; LSDTaste and general impression: 0.899 *; VCTaste and general impression: 5.27; LSDTotal: 1.025 **; VCTotal: 3.48. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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28 pages, 11712 KiB  
Article
A Feasibility Study on Utilizing Remote Sensing Data to Monitor Grape Yield and Berry Composition for Selective Harvesting
by Leeko Lee, Andrew Reynolds, Briann Dorin and Adam Shemrock
Plants 2025, 14(1), 88; https://doi.org/10.3390/plants14010088 - 31 Dec 2024
Viewed by 326
Abstract
The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data [...] Read more.
The primary purpose of this study was to improve our understanding of remote sensing technologies and their potential application in vineyards to monitor yields and fruit composition, which could then be used for selective harvesting and winemaking. For yield and berry composition data collection, representative vines from the vineyard block were selected and geolocated, and the same vines were surveyed for remote sensing data collection by the multispectral and thermal sensors in the RPAS in 2015 and 2016. The spectral reflectance data were further analyzed for vegetation indices to evaluate the correlation between the variables. Moran’s global index and map analysis were used to determine spatial clustering patterns and correlations between variables. The results of this study indicated that remote sensing data in the form of vegetation indices from the RPAS were positively correlated with yield and berry weight across sites and years. There was a positive correlation between the thermal emission and berry pH, berry phenols, and anthocyanins in certain sites and years. Overall, remote sensing technology has the potential to monitor and predict grape quality and yield, but further research on the efficacy of this data is needed for selective harvesting and winemaking. Full article
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<p>PCA results of remote sensing, vineyard yield, and berry composition from sites 1, 2, and 3 in 2015 and 2016. No harvest data collected at site 3 vineyard in 2015. Abbreviations: NDVI = normalized difference vegetation index, Thermal = thermal emission data, Clusters = number of clusters, Berry WT = berry weight, TA = titratable acidity.</p>
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<p>PCA results of remote sensing, vineyard yield, and berry composition from sites 4, 5, and 6 in 2015 and 2016. Abbreviations: NDVI = normalized difference vegetation index, Thermal = thermal emission data, Clusters = number of clusters, Berry WT = berry weight, TA = titratable acidity.</p>
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<p>PCA results among indices from the RPAS flight, vineyard yield, and berry composition in six Niagara vineyards from 2016. Variables include data in six Ontario vineyards in 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield, and berry composition in site 1 from 2015 and 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield and berry composition in site 2 from 2015 and 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield, and berry composition in site 3 from 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield, and berry composition in site 4 from 2015 and 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield, and berry composition in site 5 from 2015 and 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Spatial maps of data from RPAS flight, vineyard yield, and berry composition in site 6 from 2015 and 2016. Abbreviations: Berry WT = berry weight, TA = titratable acidity, NDVI = normalized difference vegetation index, Thermal = thermal emission data, CI green = green chlorophyll index, CI red edge = red edge chlorophyll index, NDRE = red edge normalized difference vegetation index, GNDVI = NDVI green, RVI = ratio vegetation index.</p>
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<p>Mean growing season (May to September) temperature (°C) and total growing season rainfall (mm) from two Niagara resign locations. Port Weller AUT represented Niagara-on-the-lake vineyards, and Vineland Research Station represented vineland vineyards. Historical climate normal data from St. Catharine’s A station 1981–2010.</p>
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