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Search Results (2,272)

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20 pages, 4982 KiB  
Article
Effects of Soy Protein Isolate and Inulin Conjugate on Gel Properties and Molecular Conformation of Spanish Mackerel Myofibrillar Protein
by Wei Wang, Sirui Ma, Qing Shao and Shumin Yi
Foods 2024, 13(18), 2920; https://doi.org/10.3390/foods13182920 (registering DOI) - 15 Sep 2024
Viewed by 84
Abstract
The gel properties and molecular conformation of Spanish mackerel myofibrillar protein (MP) induced by soy protein isolate–inulin conjugates (SPI–inulin conjugates) were investigated. The addition of SPI–inulin conjugates significantly enhanced the quality of the protein gel. An analysis of different additives was conducted to [...] Read more.
The gel properties and molecular conformation of Spanish mackerel myofibrillar protein (MP) induced by soy protein isolate–inulin conjugates (SPI–inulin conjugates) were investigated. The addition of SPI–inulin conjugates significantly enhanced the quality of the protein gel. An analysis of different additives was conducted to assess their impact on the gel strength, texture, water-holding capacity (WHC), water distribution, intermolecular force, dynamic rheology, Raman spectrum, fluorescence spectrum, and microstructure of MP. The results demonstrated a substantial improvement in the strength and water retention of the MP gel with the addition of the conjugate. Compared with the control group (MP), the gel strength increased from 35.18 g·cm to 41.90 g·cm, and WHC increased from 36.80% to 52.67% with the inclusion of SPI–inulin conjugates. The hydrogen bond content was notably higher than that of other groups, and hydrophobic interaction increased from 29.30% to 36.85% with the addition of SPI–inulin conjugates. Furthermore, the addition of the conjugate altered the secondary structure of the myofibrillar gel, with a decrease in α-helix content from 62.91% to 48.42% and an increase in β-sheet content from 13.40% to 24.65%. Additionally, the SPI–inulin conjugates led to a significant reduction in the endogenous fluorescence intensity of MP. Atomic force microscopy (AFM) results revealed a substantial increase in the Rq value from 8.21 nm to 20.21 nm. Adding SPI and inulin in the form of conjugates is an effective method to improve the gel properties of proteins, which provides important guidance for the study of adding conjugates to surimi products. It has potential application prospects in commercial surimi products. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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Figure 1
<p>Effects of different additives on gel strength of myofibrillar gel. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different additives on water retention of myofibrillar gel. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different additives on T<sub>2</sub> relaxation time (<b>A</b>) and the peak area ratio (<b>B</b>) of myofibrillar gel. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different additives on the chemical force of myofibrillar protein gel. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different additives on the Raman spectra (<b>A</b>) and protein secondary (<b>B</b>) structure content of myofibrillar protein gel. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of different additives on energy storage modulus (<b>A</b>) and loss modulus (<b>B</b>) of myofibrillar protein. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively.</p>
Full article ">Figure 6 Cont.
<p>Effects of different additives on energy storage modulus (<b>A</b>) and loss modulus (<b>B</b>) of myofibrillar protein. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively.</p>
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<p>Effects of different additives on the endogenous fluorescence spectra of myofibrillar protein. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively.</p>
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<p>Effects of different additives on atomic force microscopy of myofibrillar protein. Note: MP: myofibrillar protein; MP-S: myofibrillar protein with 0.6% SPI; MP-I: myofibrillar protein with 0.6% inulin, MP-M: myofibrillar protein with 0.6% mixtures of SPI and inulin; MP-C: myofibrillar protein with 0.6% SPI–inulin conjugates, respectively. Different lowercase letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis of gel strength, texture, water distribution, water retention, chemical force, microstructure, and secondary structure parameters of myofibrillar protein gel.</p>
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15 pages, 3047 KiB  
Article
Exploring the Diversity and Potential Use of Flower-Derived Lactic Acid Bacteria in Plant-Based Fermentation: Insights into Exo-Cellular Polysaccharide Production
by Azadeh Khiabani, Hang Xiao, Anders Peter Wätjen, Miguel Tovar, Vera Kuzina Poulsen, Egon Bech Hansen and Claus Heiner Bang-Berthelsen
Foods 2024, 13(18), 2907; https://doi.org/10.3390/foods13182907 (registering DOI) - 13 Sep 2024
Viewed by 239
Abstract
Isolation of new plant-derived lactic acid bacteria (LAB) is highly prioritized in developing novel starter cultures for plant-based fermentation. This study explores the diversity of LAB in Danish flowers and their potential use for plant-based food fermentation. A total of 46 flower samples [...] Read more.
Isolation of new plant-derived lactic acid bacteria (LAB) is highly prioritized in developing novel starter cultures for plant-based fermentation. This study explores the diversity of LAB in Danish flowers and their potential use for plant-based food fermentation. A total of 46 flower samples under 34 genera were collected for LAB isolation. By introducing an enrichment step, a total of 61 LAB strains were isolated and identified using MALDI-TOF and 16S rRNA sequencing. These strains represent 24 species across 9 genera, predominantly Leuconostoc mesenteroides, Fructobacillus fructosus, Apilactobacillus ozensis, and Apilactobacillus kunkeei. Phenotypic screening for exo-cellular polysaccharide production revealed that 40 strains exhibited sliminess or ropiness on sucrose-containing agar plates. HPLC analysis confirmed that all isolates produced exo-cellular polysaccharides containing glucose, fructose, or galactose as sugar monomers. Therefore, the strains were glucan, fructan, and galactan producers. The suitability of these strains for plant-based fermentation was characterized by using almond, oat, and soy milk. The results showed successful acidification in all three types of plant-based matrices but only observed texture development in soy by Leuconostoc, Weissella, Lactococcus, Apilactobacillus, and Fructobacillus. The findings highlight the potential of flower-derived LAB strains for texture development in soy-based dairy alternatives. Full article
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<p>The abundance percentage and diversity of plant-based LAB within the overall LAB community strains isolated from flowers.</p>
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<p>Slime formation associated with homo-EPS production in seven random LAB strains on modified MLS-agar medium supplemented with (a) 2% sucrose, (b) 6% sucrose, and (c) control MLS medium.</p>
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<p>Slime formation associated with homo-EPS production in eight random LAB strains on (a) modified MLS-agar medium supplemented with 2% sucrose and (b) MLS-agar medium supplemented with 2% sucrose and 1% glucose.</p>
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<p>Fingerprint of EPS monosaccharide composition of LAB strains analyzed by HPLC. Glucose is represented by an orange bar color, fructose by a green bar color, and galactose is shown as a purple bar color.</p>
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<p>pH values of strains in fermented plant-based drinks.</p>
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<p>TADM areas of strains in fermented plant-based drinks.</p>
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12 pages, 2791 KiB  
Article
Daidzein Inhibits Muscle Atrophy by Suppressing Inflammatory Cytokine- and Muscle Atrophy-Related Gene Expression
by Chihiro Munekawa, Takuro Okamura, Saori Majima, Budau River, Sayaka Kawai, Ayaka Kobayashi, Hanako Nakajima, Nobuko Kitagawa, Hiroshi Okada, Takafumi Senmaru, Emi Ushigome, Naoko Nakanishi, Masahide Hamaguchi and Michiaki Fukui
Nutrients 2024, 16(18), 3084; https://doi.org/10.3390/nu16183084 - 13 Sep 2024
Viewed by 295
Abstract
Background: Sarcopenic obesity, which is associated with a poorer prognosis than that of sarcopenia alone, may be positively affected by soy isoflavones, known inhibitors of muscle atrophy. Herein, we hypothesize that these compounds may prevent sarcopenic obesity by upregulating the gut metabolites with [...] Read more.
Background: Sarcopenic obesity, which is associated with a poorer prognosis than that of sarcopenia alone, may be positively affected by soy isoflavones, known inhibitors of muscle atrophy. Herein, we hypothesize that these compounds may prevent sarcopenic obesity by upregulating the gut metabolites with anti-inflammatory effects. Methods: To explore the effects of soy isoflavones on sarcopenic obesity and its mechanisms, we employed both in vivo and in vitro experiments. Mice were fed a high-fat, high-sucrose diet with or without soy isoflavone supplementation. Additionally, the mouse C2C12 myotube cells were treated with palmitic acid and daidzein in vitro. Results: The isoflavone considerably reduced muscle atrophy and the expression of the muscle atrophy genes in the treated group compared to the control group (Fbxo32, p = 0.0012; Trim63, p < 0.0001; Foxo1, p < 0.0001; Tnfa, p = 0.1343). Elevated levels of daidzein were found in the muscles and feces of the experimental group compared to the control group (feces, p = 0.0122; muscle, p = 0.0020). The real-time PCR results demonstrated that the daidzein decreased the expression of the palmitate-induced inflammation and muscle atrophy genes in the C2C12 myotube cells (Tnfa, p = 0.0201; Il6, p = 0.0008; Fbxo32, p < 0.0001; Hdac4, p = 0.0002; Trim63, p = 0.0114; Foxo1, p < 0.0001). Additionally, it reduced the palmitate-induced protein expression related to the muscle atrophy in the C2C12 myotube cells (Foxo1, p = 0.0078; MuRF1, p = 0.0119). Conclusions: The daidzein suppressed inflammatory cytokine- and muscle atrophy-related gene expression in the C2C12 myotubes, thereby inhibiting muscle atrophy. Full article
(This article belongs to the Special Issue Exercise, Diet and Type 2 Diabetes)
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<p>The <span class="html-italic">C57BL6/J</span> mice fed a high-fat high-sucrose diet (HFHSD) and administered water containing 0.1% isoflavone (Iso group) showed significant improvements in glucose tolerance, obesity, metabolic disorders, and muscle loss compared to the <span class="html-italic">C57BL6/J</span> mice without the isoflavone (Ctrl group). (<b>A</b>) Mice were fed HFHSD ± water containing 0.1% isoflavone for 12 weeks, starting at 8 weeks of age. (<b>B</b>) Changes in body weight (<span class="html-italic">n</span> = 6 in each case). (<b>C</b>) Oral intake of 0.1% isoflavone water. (<b>D</b>,<b>E</b>) The results of the intraperitoneal glucose tolerance test (2 g/kg body weight) for 20-week-old mice and the area-under-the-curve (AUC) analysis (<span class="html-italic">n</span> = 6 in each case). (<b>F</b>,<b>G</b>) The results of the insulin tolerance test (0.75 U/kg body weight) for the 20-week-old mice and the AUC analysis (<span class="html-italic">n</span> = 6 in each case). (<b>H</b>) Relative grip strength (<span class="html-italic">n</span> = 6 in each case). (<b>I</b>–<b>K</b>) Serum levels of alanine aminotransferase (ALT), total cholesterol (T-Chol), and triglycerides (TG) (<span class="html-italic">n</span> = 6 in each case). (<b>L</b>–<b>N</b>) The relative weight of the epididymal fat, soleus muscle, and plantaris muscle (<span class="html-italic">n</span> = 6 in each case). The data are expressed as mean ± standard deviation (SD). The data were analyzed using an unpaired <span class="html-italic">t</span>-test. * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, *** <span class="html-italic">p &lt;</span> 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Effects of Isoflavone on muscle morphology and gene expression. (<b>A</b>,<b>B</b>) The cross-sectional areas of the soleus and plantaris muscles in the 20-week-old mice (<span class="html-italic">n</span> = 6 in each case). (<b>C</b>–<b>F</b>) The relative mRNA expressions of (<b>C</b>) <span class="html-italic">Fbxo32</span>, (<b>D</b>) <span class="html-italic">Trim63</span>, (<b>E</b>) <span class="html-italic">Foxo1</span>, and (<b>F</b>) <span class="html-italic">Tnfa</span> mRNA expression in the soleus muscle normalized to the expression of <span class="html-italic">Gapdh</span> (<span class="html-italic">n</span> = 6 in each case). The data are expressed as mean ± standard deviation (SD). The data were analyzed using an unpaired <span class="html-italic">t</span>-test. * <span class="html-italic">p &lt;</span> 0.05, ** <span class="html-italic">p &lt;</span> 0.01, **** <span class="html-italic">p</span> &lt; 0.0001, ns not significant.</p>
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<p>The administration of the isoflavone increased the concentration of the daidzein in the serum, feces, and muscles. The concentrations of the daidzein in (<b>A</b>) serum, (<b>B</b>) feces, and (<b>C</b>) soleus muscle (<span class="html-italic">n</span> = 6 in each group). The concentrations of the genistein in (<b>D</b>) serum, (<b>E</b>) feces, and (<b>F</b>) soleus muscle (<span class="html-italic">n</span> = 6 in each group). The concentrations of the equol in (<b>G</b>) serum, (<b>H</b>) feces, and (<b>I</b>) soleus muscle (<span class="html-italic">n</span> = 6 in each group). The data are expressed as mean ± standard deviation (SD). The data were analyzed using an unpaired <span class="html-italic">t</span>-test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, ns not significant.</p>
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<p>Impact of daidzein on gene expression in C2C12 myotube cells. The relative mRNA expression of (<b>A</b>) <span class="html-italic">Tnfa,</span> (<b>B</b>) <span class="html-italic">Il-6</span>, (<b>C</b>) <span class="html-italic">Fbxo32</span>, (<b>D</b>) <span class="html-italic">Hdac4</span>, (<b>E</b>) <span class="html-italic">Trim63</span>, and (<b>F</b>) <span class="html-italic">Foxo1</span> all normalized to the expression of <span class="html-italic">Gapdh</span> in the C2C12 cells (<span class="html-italic">n</span> = 6 in each case). The data were analyzed using a one-way ANOVA followed by Holm–Šídák’s multiple-comparisons test. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001, ns not significant.</p>
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<p>Effect of daidzein on muscle atrophy proteins in C2C12 myotube cells. (<b>A</b>) Western blot analysis depicting the levels of <span class="html-italic">Foxo1</span>, MuRF1, and <span class="html-italic">Gapdh</span> in the C2C12 myotube cells. (<b>B</b>,<b>C</b>) The relative optical density of <span class="html-italic">Foxo1</span> (<b>B</b>) and MuRF1 (<b>C</b>) normalized to <span class="html-italic">Gapdh</span>. The values are expressed as mean ± s.e.m. * <span class="html-italic">p</span>  &lt;  0.05, ** <span class="html-italic">p</span>  &lt;  0.01, as determined using a one-way ANOVA.</p>
Full article ">
17 pages, 592 KiB  
Article
Effects of Replacing Cow’s Milk with Plant-Based Beverages on Potential Nutrient Intake in Sustainable Healthy Dietary Patterns: A Case Study
by Paola Biscotti, Massimiliano Tucci, Donato Angelino, Valentina Vinelli, Nicoletta Pellegrini, Cristian Del Bo’, Patrizia Riso and Daniela Martini
Nutrients 2024, 16(18), 3083; https://doi.org/10.3390/nu16183083 - 13 Sep 2024
Viewed by 332
Abstract
More consumers are replacing cow’s milk (CM) with plant-based drinks (PBD), but data indicating the nutritional impact are limited. This theoretical study aims to assess the effect of substituting CM with PBD sold in Italy on nutrient intake within two dietary patterns: one [...] Read more.
More consumers are replacing cow’s milk (CM) with plant-based drinks (PBD), but data indicating the nutritional impact are limited. This theoretical study aims to assess the effect of substituting CM with PBD sold in Italy on nutrient intake within two dietary patterns: one aligned with the EAT-Lancet Commission reference diet adapted to Italian food habits (EAT-IT) and another one in line with the Italian Dietary Guidelines (IDG). Nutrition information from 368 PBD were collected and categorized according to their descriptive name and their fortification or not with calcium (Ca- and nCa-fortified). The substitution of CM with each PBD category in both dietary patterns was conducted, and an analysis of nutrient content and adequacy was performed. Substituting CM with all PBD resulted in reduced protein intake, except for nCa-fortified soy drinks, decreased saturated fat and vitamins B2 and B12, and increased fiber intake. Replacing CM with nCa-fortified PBD within both diets decreased Ca intake. Following the substitution of CM with Ca-fortified PBD, variations in vitamin D intake depended on the PBD category. The main risk of nutritional inadequacy was observed in Ca and vitamin D levels, which may even be amplified considering the different bioavailability based on the source of nutrients. This study highlighted the important role of CM in meeting calcium requirements and the potential unintended consequences of substituting CM with PBD without considering their nutritional differences. Full article
(This article belongs to the Special Issue Transition towards Sustainable Healthy Diets: A Complex Journey)
23 pages, 4200 KiB  
Article
A Novel Approach to Protect Brazil Nuts from Lipid Oxidation: Efficacy of Nanocellulose–Tocopherol Edible Coatings
by Debora Ribeiro Nascimento, Juliana Mesquita, Thayanne da Silva, Thais Hernandes, Elaine Cristina Lengowski and Katiuchia Takeuchi
Coatings 2024, 14(9), 1182; https://doi.org/10.3390/coatings14091182 - 12 Sep 2024
Viewed by 280
Abstract
High levels of unsaturated fatty acids in Brazil nuts compromise their sensory quality through lipid oxidation. To mitigate this reaction, it is crucial to package nuts under a vacuum and in aluminate packaging. An alternative method is the application of an edible coating [...] Read more.
High levels of unsaturated fatty acids in Brazil nuts compromise their sensory quality through lipid oxidation. To mitigate this reaction, it is crucial to package nuts under a vacuum and in aluminate packaging. An alternative method is the application of an edible coating with antioxidant properties. This study aimed to develop an edible coating composed of carboxymethylcellulose and sorbitol, physically reinforced with nanocellulose, and chemically fortified with tocopherol. The edible coating was characterized based on its physical properties, mechanical strength, biodegradability, optical light transmission properties, color parameters, and water vapor permeability. Formulations CC5 (Carboxymethyl cellulose (CMC) + sorbitol + 5% nanocellulose) and CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% nanocellulose) showed enhanced mechanical strength. The combination of nanocellulose with tocopherol in formulations CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% nanocellulose) and CCT5 developed superior barriers to visible and ultraviolet light, a desired characteristic for coatings intended to increase the shelf life of Brazil nuts. The nuts coated with CC5 and CCT3 showed the lowest PV values at the end of the accelerated oxidation test conducted at 60 °C. Full article
(This article belongs to the Special Issue Edible Films and Coatings: Fundamentals and Applications, 2nd Edition)
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<p>CNF produced with pulp of <span class="html-italic">Eucalyptus</span> sp. Bleached. (<b>a</b>) 5 kx; (<b>b</b>) 10 kx magnification.</p>
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<p>X-ray diffraction pattern of bleached fibrillated nanocellulose.</p>
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<p>Solutions of film-forming edible coatings of formulations C01, CC1, CC3, and CC5, after the 7th day of the creaming test. CC1 (CMC + sorbitol + 1% de CNF); CC3 (CMC + sorbitol + 3% de CNF); CC5 (CMC + sorbitol + 5% de CNF). Source: Author (2023).</p>
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<p>Film-forming solutions of edible coatings of formulations C02, CCT1, CCT3, and CCT5, after the 7th day of the creaming test. CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF). Source: Author (2023).</p>
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<p>Results of analyses of (<b>a</b>) water content (%); (<b>b</b>) thickness (mm). C01 (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF).</p>
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<p>The visual appearance of transparency and opacity of prepared edible films. C01 (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF).</p>
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<p>Results of water vapor permeability analysis (g h<sup>−1</sup> m<sup>−2</sup> kPa<sup>−1</sup>). C01 (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF). Different capital letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) by the Kruskal–Wallis test.</p>
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<p>Result of analysis of mechanical properties for puncture deformation. (<b>a</b>) Puncture deformation (<b>b</b>) Puncture Force. (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF). Different lowercase letters indicate significant difference (<span class="html-italic">p</span> &lt; 0.05) by Tukey test.</p>
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<p>(<b>a</b>) Day 1 of biodegradability analysis. (<b>b</b>) Day 7 of biodegradability analysis.</p>
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<p>(<b>a</b>) Biplots of the eigenvectors and eigenvalues of two main components according to the variables of mechanical properties, such as a barrier against visible light, ultraviolet light, and water vapor. (<b>b</b>) Hierarchical cluster analysis. TS: Tensile strength; EB: Elongation at Break; VLT: Visible Light Transmission Value; URV: Ultraviolet Light Rejection Rate; IRR: Infrared Light Rejection Rate; WVP: Water vapor permeability.</p>
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<p>Evolution of the acidity index of oils extracted from coated Brazil nuts subjected to accelerated oxidation. (<b>a</b>) Formulations with reinforced physical barriers; (<b>b</b>) formulations with reinforced physical and chemical barriers. SR (uncoated); C01 (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF. Different lowercase letters indicate significant difference (<span class="html-italic">p</span> &lt; 0.05) by Tukey test.</p>
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<p>Evolution of the peroxide value of oils extracted from coated Brazil nuts subjected to accelerated oxidation. (<b>a</b>) Formulations with reinforced physical barrier; (<b>b</b>) formulations with reinforced physical and chemical barriers. SR (without coating); C01 (CMC + sorbitol); CC1 (CMC + sorbitol + 1% CNF); CC3 (CMC + sorbitol + 3% CNF); CC5 (CMC + sorbitol + 5% CNF); C02 (CMC + sorbitol + tocopherol + soy lecithin); CCT1 (CMC + sorbitol + tocopherol + soy lecithin + 1% CNF); CCT3 (CMC + sorbitol + tocopherol + soy lecithin + 3% CNF); CCT5 (CMC + sorbitol + tocopherol + soy lecithin + 5% CNF). Different capital letters indicate a significant difference (<span class="html-italic">p</span> &lt; 0.05) using the Kruskal–Wallis test.</p>
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16 pages, 2288 KiB  
Article
Effect of Mono- and Polysaccharide on the Structure and Property of Soy Protein Isolate during Maillard Reaction
by Kun Wen, Qiyun Zhang, Jing Xie, Bin Xue, Xiaohui Li, Xiaojun Bian and Tao Sun
Foods 2024, 13(17), 2832; https://doi.org/10.3390/foods13172832 - 6 Sep 2024
Viewed by 342
Abstract
As a protein extracted from soybeans, soy protein isolate (SPI) may undergo the Maillard reaction (MR) with co-existing saccharides during the processing of soy-containing foods, potentially altering its structural and functional properties. This work aimed to investigate the effect of mono- and polysaccharides [...] Read more.
As a protein extracted from soybeans, soy protein isolate (SPI) may undergo the Maillard reaction (MR) with co-existing saccharides during the processing of soy-containing foods, potentially altering its structural and functional properties. This work aimed to investigate the effect of mono- and polysaccharides on the structure and functional properties of SPI during MR. The study found that compared to oat β-glucan, the reaction rate between SPI and D-galactose was faster, leading to a higher degree of glycosylation in the SPI–galactose conjugate. D-galactose and oat β-glucan showed different influences on the secondary structure of SPI and the microenvironment of its hydrophobic amino acids. These structural variations subsequently impact a variety of the properties of the SPI conjugates. The SPI–galactose conjugate exhibited superior solubility, surface hydrophobicity, and viscosity. Meanwhile, the SPI–galactose conjugate possessed better emulsifying stability, capability to produce foam, and stability of foam than the SPI–β-glucan conjugate. Interestingly, the SPI–β-glucan conjugate, despite its lower viscosity, showed stronger hypoglycemic activity, potentially due to the inherent activity of oat β-glucan. The SPI–galactose conjugate exhibited superior antioxidant properties due to its higher content of hydroxyl groups on its molecules. These results showed that the type of saccharides had significant influences on the SPI during MR. Full article
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<p>pH value, UV-Vis absorbance (<b>A</b>), and fluorescence value (<b>B</b>) of the Maillard reaction system. Different letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>FT-IR spectra (<b>A</b>) and secondary structure (<b>B</b>) of SPI conjugates.</p>
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<p>Fluorescence spectra of SPI conjugates.</p>
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<p>Solubility of SPI conjugates.</p>
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<p>The apparent viscosity of SPI conjugates.</p>
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<p>α-Glucosidase inhibitory activity of SPI conjugates. Different letters indicate statistically significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>DPPH scavenging activity of SPI conjugates. The dotted line is the concentration at which the DPPH scavenging rate reaches 50%.</p>
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<p>Reducing power of SPI conjugates. The dotted line is the reducing power of the samples at a concentration of 8 mg/mL.</p>
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<p>Ferrous ion-chelating capacity of SPI conjugates. The dotted line is the concentration at which the ferrous ion-chelating capacity reaches 50%.</p>
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16 pages, 5125 KiB  
Article
Regional Sea Level Changes in the East China Sea from 1993 to 2020 Based on Satellite Altimetry
by Lujie Xiong, Fengwei Wang and Yanping Jiao
J. Mar. Sci. Eng. 2024, 12(9), 1552; https://doi.org/10.3390/jmse12091552 - 5 Sep 2024
Viewed by 289
Abstract
A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data [...] Read more.
A comprehensive analysis was carried out to investigate the driving factors and influencing mechanisms of spatiotemporal variation of sea level at multiple scales in the East China Sea (ECS) via satellite altimetry datasets from 1993 to 2020. Based on the altimetry grid data processed by the local mean decomposition method, the spatiotemporal changes of ECS sea level are analyzed from the multi-scale perspective in terms of multi-year, seasonal, interannual, and multi-modal scales. The results revealed that the ECS regional mean sea level change rate is 3.41 ± 0.58 mm/year over the 28-year period. On the seasonal scale, the regional mean sea level change rates are 3.45 ± 0.66 mm/year, 3.35 ± 0.60 mm/year, 3.39 ± 0.71 mm/year, and 3.57 ± 0.75 mm/year, for the four seasons (i.e., spring, summer, autumn, and winter) respectively. The spatial distribution analysis showed that ECS sea level changes are most pronounced in coastal areas. The northeast sea area of Taiwan and the edge of the East China Sea shelf are important areas of mesoscale eddy activity, which have an important impact on regional sea level change. The ECS seasonal sea level change is mainly affected by monsoons, precipitation, and temperature changes. The spatial distribution analysis indicated that the impact factors, including seawater thermal expansion, monsoons, ENSO, and the Kuroshio Current, dominated the ECS seasonal sea level change. Additionally, the ENSO and Kuroshio Current collectively affect the spatial distribution characteristics. Additionally, the empirical orthogonal function was employed to analyze the three modes of ECS regional sea level change, with the first three modes contributing 26.37%, 12.32%, and 10.47%, respectively. Spatially, the first mode mainly corresponds to ENSO index, whereas the second and third modes are linked to seasonal factors, and exhibit antiphase effects. The analyzed correlations between the ECS sea level change and southern oscillation index (SOI), revealed the consistent spatial characteristics between the regions affected by ENSO and those by the Kuroshio Current. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Marine Environmental Monitoring)
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<p>The flowchart of this study.</p>
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<p>The ECS average SLA (mm) for the period 1993 to 2020.</p>
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<p>The ECS sea level change rates (mm/year) for the period 1993 to 2020.</p>
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<p>ECS regional mean sea level change from 1993 to 2020. (The black line indicates the annual average SLA, while the red line shows the annual rate of increase from 1993 to 2020).</p>
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<p>The spatial distributions of ECS averaged SLA for four seasons, for the period from January 1993 to December 2020 (mm). (Average sea level distributions for (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter, with red indicating higher values and blue indicating lower values, allowing direct comparison across all seasons).</p>
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<p>The linear-trend spatial distributions of ECS sea level change for four seasons over the period from 1993 to 2020. (Spatial distribution of the annual average sea level rise rate for (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter, with red indicating higher values and blue indicating lower values, allowing direct comparison of sea level distribution across all seasons).</p>
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<p>Four seasonal ECS regional mean sea level changes for 1993 to 2020.</p>
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<p>Wavelet Analysis of ECS regional mean sea level change. (<a href="#jmse-12-01552-f008" class="html-fig">Figure 8</a>a shows the low-frequency components of the sea level time series. <a href="#jmse-12-01552-f008" class="html-fig">Figure 8</a>b displays the wavelet energy spectrum, where red indicates higher energy and blue lower energy; the circled area passes the significance test. <a href="#jmse-12-01552-f008" class="html-fig">Figure 8</a>c illustrates the energy values from the spectrum).</p>
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<p>EOF Analysis of SLA Variations in the ECS.</p>
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<p>Spatiotemporal correlation analysis between the SLAs and SOI values in the ECS. (<a href="#jmse-12-01552-f010" class="html-fig">Figure 10</a>a shows the time series of SOI and low-frequency SLA from 1993 to 2020, and <a href="#jmse-12-01552-f010" class="html-fig">Figure 10</a>b shows the spatio-temporal distribution of the correlation coefficient between SOI index and low-frequency SLA).</p>
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12 pages, 623 KiB  
Article
Color Stability Assessment of Single- and Multi-Shade Composites Following Immersion in Staining Food Substances
by Vittorio Checchi, Eleonora Forabosco, Giulia Della Casa, Shaniko Kaleci, Luca Giannetti, Luigi Generali and Pierantonio Bellini
Dent. J. 2024, 12(9), 285; https://doi.org/10.3390/dj12090285 - 4 Sep 2024
Viewed by 321
Abstract
Composite resins are the material of choice for direct restorations, and their success depends mainly on their color stability, since discoloration causes color mismatch, and consequent patient dissatisfaction. A single- and a multi-shade resin were compared in order to evaluate their pigmentation after [...] Read more.
Composite resins are the material of choice for direct restorations, and their success depends mainly on their color stability, since discoloration causes color mismatch, and consequent patient dissatisfaction. A single- and a multi-shade resin were compared in order to evaluate their pigmentation after immersion in staining substances and to investigate the effect of the polymerization time on their color stability. Two-hundred-and-forty composite specimens were created, half made of a single-shade (Group ONE, n = 120) and half of a multi-shade composite (Group OXP, n = 120). Each group was further divided into ONE30 (n = 60) and OXP30 (n = 60), polymerized for 30″, and ONE80 (n = 60) and OXP80 (n = 60), polymerized for 80″. Randomly, the specimens were immersed in turmeric solution, soy sauce, energy drink, or artificial saliva. By means of a spectrophotometer, ΔE00 and WId were calculated at 24 h (T0), at 7 (T1), and 30 (T2) days. Single-shade composites showed statistically significant differences in color change from the turmeric solution, energy drink, and soy sauce than the multi-shade composites (p < 0.005), showing a higher discoloration potential. The polymerization time did not have significative effects on color stability. Single-shade composites showed more color change than multi-shade systems after immersion in staining substances, and the curing time did not influence color variations. Full article
(This article belongs to the Special Issue State of the Art in Dental Restoration)
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<p>Linear prediction of ΔE<sub>00</sub>. Means (dots), 95% confidence interval (whiskers), samples at baseline T<sub>0</sub> (time T<sub>0</sub>–T<sub>1</sub>) and at T<sub>2</sub> (time T<sub>0</sub>–T<sub>3</sub>).</p>
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<p>Linear prediction of WI<sub>D</sub>. Means (dots), 95% confidence interval (whiskers), samples at baseline T<sub>0</sub>, T<sub>1</sub>, at T<sub>2</sub>.</p>
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21 pages, 9614 KiB  
Article
Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns
by Lin Liao, Saeed Rad, Junfeng Dai, Asfandyar Shahab, Jianying Mo and Shanshan Qi
Water 2024, 16(17), 2504; https://doi.org/10.3390/w16172504 - 3 Sep 2024
Viewed by 443
Abstract
In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based [...] Read more.
In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks. Full article
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<p>Overview and Distribution of Meteorological Stations in the JRB.</p>
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<p>The interannual variation of extreme precipitation indices in the Jialing River Basin over the past 60 years (“*” and “***” represent the significance levels at <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.001, respectively).</p>
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<p>The interannual variation of extreme atmospheric temperature indices in the Jiangling River Basin from 1960–2020 (“*” and “***” represent the significance levels at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.001, respectively).</p>
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<p>The spatial distribution of the annual trend of extreme precipitation index in the Jialing River Basin from 1960 to 2020 (Triangles outside the domain indicate stations in the vicinity of JRB, the color of the ring represents the ratio of different trend stations).</p>
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<p>The spatial distribution of the annual trend of extreme atmospheric temperature index in the Jialing River Basin from 1960 to 2020 (Triangles outside the domain indicate stations in the vicinity of JRB, the color of the ring represents the ratio of different trend stations).</p>
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<p>Pearson correlation analysis of extreme indices in the Jialing River Basin from 1960 to 2020. (“*”, “**”, and “***” represent the significance levels at <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, respectively, the size of the circle corresponds to the size of the correlation coefficient).</p>
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<p>Pearson correlation analysis of extreme indices and atmospheric circulation patterns in the Jialing River Basin from 1960 to 2020. (“*” and “**” represent the significance levels at <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, the size of the circle corresponds to the size of the correlation coefficient).</p>
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<p>WTC of atmospheric circulation index and extreme precipitation index in the Jialing River Basin from 1960 to 2020. (The region surrounded by the solid black line represents the significance of the region, and arrows to the left represent negative coherence and to the right represent positive coherence).</p>
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<p>WTC of atmospheric circulation index and extreme atmospheric temperature index in the Jialing River Basin from 1960 to 2020. (The region surrounded by the solid black line represents the significance of the region, and arrows to the left represent negative coherence and to the right represent positive coherence).</p>
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12 pages, 2689 KiB  
Review
Key Factors Influencing Gelation in Plant vs. Animal Proteins: A Comparative Mini-Review
by Mohammadreza Khalesi, Kyeesha Glenn-Davi, Nima Mohammadi and Richard J. FitzGerald
Gels 2024, 10(9), 575; https://doi.org/10.3390/gels10090575 - 3 Sep 2024
Viewed by 550
Abstract
This review presents a comparative analysis of gelation properties in plant-based versus animal-based proteins, emphasizing key factors such as pH, ionic environment, temperature, and anti-nutritional factors. Gelation, a crucial process in food texture formation, is influenced by these factors in varying ways for [...] Read more.
This review presents a comparative analysis of gelation properties in plant-based versus animal-based proteins, emphasizing key factors such as pH, ionic environment, temperature, and anti-nutritional factors. Gelation, a crucial process in food texture formation, is influenced by these factors in varying ways for plant and animal proteins. Animal proteins, like casein, whey, meat, and egg, generally show stable gelation properties, responding predictably to pH, temperature, and ionic changes. In contrast, plant proteins such as soy, pea, wheat, and oilseed show more variable gelation, often requiring specific conditions, like the presence of NaCl or optimal pH, to form effective gels. Animal proteins tend to gel more reliably, while plant proteins require precise environmental adjustments for similar results. Understanding these factors is crucial for selecting and processing proteins to achieve desired textures and functionalities in food products. This review highlights how changing these key factors can optimize gel properties in both plant- and animal-based proteins. Full article
(This article belongs to the Special Issue Design, Fabrication, and Applications of Food Composite Gels)
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<p>Illustration of protein denaturation and network formation: native proteins in their folded, functional state; denatured proteins undergoing unfolding and aggregation; formation of unfolded protein networks and aggregates; development of gel and particulate networks after denaturation.</p>
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18 pages, 3633 KiB  
Article
Enzymatic Preparation, Identification by Transmembrane Channel-like 4 (TMC4) Protein, and Bioinformatics Analysis of New Salty Peptides from Soybean Protein Isolate
by Ziying Zhao, Mingzhe Yang, Zhijiang Li, Huacheng Tang, Xuejian Song and Xinhui Wang
Foods 2024, 13(17), 2798; https://doi.org/10.3390/foods13172798 - 3 Sep 2024
Viewed by 516
Abstract
To address the public health challenges posed by high-salt diets, this study utilized pepsin and flavourzyme for the continuous enzymatic hydrolysis of a soy protein isolate (SPI). The separation, purification, and identification of salt-containing peptides in SPI hydrolysate were conducted using ultrafiltration (UF), [...] Read more.
To address the public health challenges posed by high-salt diets, this study utilized pepsin and flavourzyme for the continuous enzymatic hydrolysis of a soy protein isolate (SPI). The separation, purification, and identification of salt-containing peptides in SPI hydrolysate were conducted using ultrafiltration (UF), gel filtration chromatography (GFC), and Liquid Chromatography–Mass Spectrometry/Mass Spectrometry (LC-MS/MS). Subsequently, a molecular docking model was constructed between salt receptor protein transmembrane channel 4 (TMC4) and the identified peptides. Basic bioinformatics screening was performed to obtain non-toxic, non-allergenic, and stable salt peptides. After the enzymatic hydrolysis, separation, and purification of SPI, a component with a sensory evaluation score of 7 and an electronic tongue score of 10.36 was obtained. LC-MS/MS sequencing identified a total of 1697 peptides in the above component, including 84 potential salt-containing peptides. A molecular docking analysis identified seven peptides (FPPP, GGPW, IPHF, IPKF, IPRR, LPRR, and LPHF) with a strong theoretical salty taste. Furthermore, residues Glu531, Asp491, Val495, Ala401, and Phe405 of the peptides bound to the TMC4 receptor through hydrogen bonds, hydrophobic interactions, and electrostatic interactions, thereby imparting a significant salty taste. A basic bioinformatics analysis further revealed that IPHF, LPHF, GGPW, and IPKF were non-toxic, non-allergenic, and stable salt-containing peptides. This study not only provides a new sodium reduction strategy for the food industry, but also opens up new avenues for improving the public’s healthy eating habits. Full article
(This article belongs to the Section Food Physics and (Bio)Chemistry)
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<p>Evaluation of salt taste of UF fractions. (<b>A</b>) Electronic tongue score of UF fractions. (<b>B</b>) Sensory evaluation score of UF fractions. Separation and purification of GFC fractions and evaluation of salt taste. (<b>C</b>) Gel permeation chromatography of MW &lt; 1 kDa fraction. (<b>D</b>) Electronic tongue score of GFC fractions. (<b>E</b>) Electronic tongue score for GFC fractions. Note: AHS means acid; CTS means salty; NMS means umami; SCS means bitter; ANS means sweet; CPS and CKS are chiasma types.</p>
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<p>Construction and evaluation of 3D structure of TMC4. (<b>A</b>) Three-dimensional image of RANK1 protein model. (<b>B</b>) Ramachandran plot of RANK1. (<b>C</b>) Error valuation of RANK1.</p>
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<p>Construction and evaluation of 3D structure of TMC4. (<b>A</b>) Three-dimensional image of RANK1 protein model. (<b>B</b>) Ramachandran plot of RANK1. (<b>C</b>) Error valuation of RANK1.</p>
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<p>Molecular docking binding energy of 7 selected peptides and positive control peptides.</p>
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<p>The 3D and 2D structural diagram of docking between salty peptides and TMC4. (<b>A</b>) FPPP; (<b>B</b>) GGPW; (<b>C</b>) IPHF; (<b>D</b>) IPKF; (<b>E</b>) IPRR; (<b>F</b>) LPHF; (<b>G</b>) LPRR.</p>
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<p>The 3D and 2D structural diagram of docking between salty peptides and TMC4. (<b>A</b>) FPPP; (<b>B</b>) GGPW; (<b>C</b>) IPHF; (<b>D</b>) IPKF; (<b>E</b>) IPRR; (<b>F</b>) LPHF; (<b>G</b>) LPRR.</p>
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<p>Peptide binding to TMC4 receptor produces interaction type and frequency of interactions.</p>
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<p>Key amino acid residues around the TMC4 active site.</p>
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14 pages, 273 KiB  
Article
Intakes of Dairy and Soy Products and 10-Year Coronary Heart Disease Risk in Korean Adults
by Sinwoo Hwang and Ae Wha Ha
Nutrients 2024, 16(17), 2959; https://doi.org/10.3390/nu16172959 - 3 Sep 2024
Viewed by 585
Abstract
Dairy and soy products are healthy food. However, studies have reported conflicting results associating their intake with coronary heart disease (CHD). Thus, this study determined the association between intake of dairy or soy products and 10-year CHD risk. Participants aged 40~69 years were [...] Read more.
Dairy and soy products are healthy food. However, studies have reported conflicting results associating their intake with coronary heart disease (CHD). Thus, this study determined the association between intake of dairy or soy products and 10-year CHD risk. Participants aged 40~69 years were grouped into those who consumed dairy products (more or less than twice a week) and those who consumed soy products (more or less than twice a week). Ten-year CHD risk (%), atherogenic index (AI), and atherogenic index of plasma (AIP) were calculated. The CHD risk, according to the level of dairy and soy product intake, was expressed as an odds ratio (OR) and a confidence interval (CI). Significant differences were observed in sex, age, education, income, and living area according to dairy intake frequencies, whereas only age showed significant differences according to soy products’ intake frequencies. Relative effects of Framingham Risk Score (FRS) factors on 10-year CHD risk in Korean adults were found to be significant in the order of age, high-density lipoprotein cholesterol (HDL-C), smoking, blood total cholesterol (TC), systolic blood pressure (SBP), diabetes, and sex. Overall, participants who consumed dairy products ≥2/week had a significantly lower OR of 10-year CHD risk compared to those who consumed dairy products <2/week after adjusting for confounding factors (OR: 0.742, 95% CI: 0.619 to 0.890). Otherwise, intake of soy products ≥2/week tended to decrease the OR of 10-year CHD risk, although the decrease was not statistically significant. In conclusion, Korean adults who consumed dairy products ≥2/week had higher HDL-C and lower 10-year CHD risk than those who consumed dairy products <2/week. However, these results did not appear when consuming soy products. Full article
(This article belongs to the Section Nutrition and Public Health)
11 pages, 247 KiB  
Article
Evaluation of Dietary Supplementation of a Multi-Carbohydrase Enzyme Complex on Growth Performance and Carcass Traits of Pekin Ducks Fed Corn–Soy Based Diets
by Hector Leyva-Jimenez, Emily Jiral, Melinda Grimes, Jessica J. Rocha, Carlos Soto, Yemi Burden, Brian P. Dirks and Gregory S. Archer
Poultry 2024, 3(3), 307-317; https://doi.org/10.3390/poultry3030023 - 2 Sep 2024
Viewed by 218
Abstract
The present study investigated the effect of supplementing a multi-carbohydrase enzyme complex (MCE) in corn–soy-based diets of Pekin ducks. The treatments were as follows: positive control (PC, 2980 and 3120 kcal/kg for starter and grower, respectively); negative control (NC, −132 kcal/kg energy reduction [...] Read more.
The present study investigated the effect of supplementing a multi-carbohydrase enzyme complex (MCE) in corn–soy-based diets of Pekin ducks. The treatments were as follows: positive control (PC, 2980 and 3120 kcal/kg for starter and grower, respectively); negative control (NC, −132 kcal/kg energy reduction to PC achieved by reduction of fat and wheat middlings as filler); NC + MCE at 75 ppm (E75); 100 ppm (E100); and 125 ppm (E125) randomly distributed in 10 replicate pens with 25 birds each. Performance was evaluated after 14 and 35 d. On day 36 of the trial, five ducks/pen were processed to evaluate carcass traits. During days 1–14, the PC had a lower (p < 0.01) feed intake compared to all other treatments. At 14 and 35 d of age the NC decreased (p < 0.001) the body weight (BW) of the ducks compared to the PC by −8.3% and −5.3%, respectively. The NC BW was lower (p < 0.001) compared to all MCE-supplemented treatments at 14 and 35 d. The BW of E75, E100, and E125 treatments was not different (p > 0.05) from the PC at both evaluation periods. Cumulatively (1–35 d), the NC resulted in a weight-adjusted FCR increase (p = 0.001) of 9.9% compared to the PC, and the FCR of E75, E100, and E125 were able to recover 72.3%, 66.4%, and 63.5%, respectively, compared to the PC. The carcass and breast weights were lower (p < 0.001) in the NC compared to all other treatments, and no differences (p > 0.05) were observed between the MCE-supplemented treatments and the PC. In conclusion, these results suggest that the MCE supplementation can maintain duck growth performance with no negative effects on carcass traits in energy-reduced corn–soybean meal-based diets. From the results of the trial, 75 ppm MCE delivered the best performance recovery and 125 ppm MCE supplementation had the best % breast yield. Full article
(This article belongs to the Collection Poultry Nutrition)
17 pages, 302 KiB  
Article
Sex and Age Differences in the Effects of Food Frequency on Metabolic Parameters in Japanese Adults
by Katsumi Iizuka, Kotone Yanagi, Kanako Deguchi, Chihiro Ushiroda, Risako Yamamoto-Wada, Kazuko Kobae, Yoshiko Yamada and Hiroyuki Naruse
Nutrients 2024, 16(17), 2931; https://doi.org/10.3390/nu16172931 - 2 Sep 2024
Viewed by 717
Abstract
Owing to differences in dietary preferences between men and women, the associations between dietary intake frequency and metabolic parameters may differ between the sexes. A retrospective observational study of the checkup findings of 3147 Japanese individuals (968 men, 2179 women) aged 20–59 years [...] Read more.
Owing to differences in dietary preferences between men and women, the associations between dietary intake frequency and metabolic parameters may differ between the sexes. A retrospective observational study of the checkup findings of 3147 Japanese individuals (968 men, 2179 women) aged 20–59 years was conducted to examine differences in dietary habits and associations between food frequency and blood parameters (eGFR, HbA1c, uric acid, and lipids) by sex and age. Males were more likely to consume meat, fish, soft drinks, and alcohol, whereas women were more likely to consume soybeans, dairy products, vegetables, fruits, and snacks. Multivariate linear regression models adjusted for age and BMI revealed that meat intake frequency was positively associated with HbA1c (β = 0.007, p = 0.03) and negatively associated with eGFR (β = −0.3, p = 0.01) only in males, whereas fish intake frequency was positively associated with eGFR (β = 0.4, p = 0.005) only in females. Egg and soy intake frequencies were positively and negatively associated with non-HDL-C (egg: β = 0.6, p = 0.02; soy: β = −0.3, p = 0.03) only in females. Alcohol consumption frequency was associated with uric acid (M: β = 0.06, p < 0.001; F: β = 0.06, p < 0.001) and HDL-C (M: β = 1.0, p < 0.001; F: β = 1.3, p < 0.001) in both sexes. Future research is needed to determine whether varying the emphasis of dietary guidance by sex and age group is effective, since the effects of dietary preferences on metabolic parameters vary by age and sex. Full article
(This article belongs to the Special Issue Dietary Habits and Metabolic Health)
27 pages, 10710 KiB  
Article
The Effect of High Hydrostatic Pressure (HHP) Induction Parameters on the Formation and Properties of Inulin–Soy Protein Hydrogels
by Anna Florowska, Tomasz Florowski, Patrycja Goździk, Adonis Hilal, Hanna Florowska and Emilia Janiszewska-Turak
Gels 2024, 10(9), 570; https://doi.org/10.3390/gels10090570 - 31 Aug 2024
Viewed by 337
Abstract
The aim of this study was to determine the effect of high hydrostatic pressure (HHP) induction parameters on the formation and properties of inulin–soy protein hydrogels. Solutions containing 20 g/100 g of inulin and 3 or 6 g/100 g of soy protein isolate [...] Read more.
The aim of this study was to determine the effect of high hydrostatic pressure (HHP) induction parameters on the formation and properties of inulin–soy protein hydrogels. Solutions containing 20 g/100 g of inulin and 3 or 6 g/100 g of soy protein isolate (3 SPI; 6 SPI) were subjected to HHPs of 150, 300, or 500 MPa for 5, 10, or 20 min. The HHP parameters had no significant impact on the effectiveness of hydrogel formation. In most cases, the time of solution pressurization had no significant effect on the characteristics of hydrogels. However, increasing the induction pressure from 150 to 300 MPa resulted in hydrogels with different characteristics being obtained, e.g., more flattened microstructure; higher stability (only 3 SPI); higher yield stress, firmness, and adhesiveness; and lower spreadability. These changes were more noticeable in the hydrogels with lower protein content. An increase in the induction pressure (to 500 MPa) did not result in a significant strengthening of the hydrogel structure. However, in the case of 6 SPI hydrogels, induction with a pressure of 500 MPa had an unfavorable effect on their stability. The results indicate that HHP (300 MPa) can be used as an effective method for strengthening the structure of inulin–protein hydrogels. Full article
(This article belongs to the Special Issue Modification of Gels in Creating New Food Products)
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Figure 1
<p>MSD (Mean Square Displacement) curves during gelation of aqueous inulin–protein solutions containing 3 g/100 g of soy protein isolate (24 h at 20 °C) after HHP treatment.</p>
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<p>MSD (Mean Square Displacement) curves during gelation of aqueous inulin–protein solutions containing 6 g/100 g of soy protein isolate (24 h at 20 °C) after HHP treatment.</p>
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<p>Changes in the fluidity index (FI) as a function of the gelation time of inulin–soy protein solutions (3 g/100 g) induced by HHP treatment with different pressure parameters (150, 300 and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the fluidity index (FI) as a function of the gelation time of inulin–soy protein solutions (6 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the elasticity index (EI) as a function of the gelation time of inulin–soy protein solutions (3 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the elasticity index (EI) as a function of the gelation time of inulin–soy protein solutions (6 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the Solid–Liquid Balance (SLB) as a function of the gelation time of inulin–soy protein solutions (3 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the Solid–Liquid Balance (SLB) as a function of the gelation time of inulin-soy protein solutions (6 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the Macroscopic Viscosity Index (MVI) as a function of the gelation time of inulin–soy protein solutions (3 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>Changes in the Macroscopic Viscosity Index (MVI) as a function of the gelation time of inulin–soy protein solutions (6 g/100 g) induced by HHP treatment with different pressure parameters (150, 300, and 500 MPa) and time (5, 10, and 20 min).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the stability of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the inulin–soy protein (3 SPI) hydrogel transmission profiles presented enabling LUMiSizer<sup>®</sup> analysis.</p>
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<p>The effect of high hydrostatic pressure induction parameters on the inulin–soy protein (6 SPI) hydrogel transmission profiles presented enabling LUMiSizer<sup>®</sup> analysis.</p>
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<p>The effect of high hydrostatic pressure induction parameters on the microstructure of inulin–soy protein hydrogels containing 3 g/100 g SPI.</p>
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<p>The effect of high hydrostatic pressure induction parameters on the microstructure of inulin–soy protein hydrogels containing 6 g/100 g SPI.</p>
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<p>The effect of high hydrostatic pressure induction parameters on the yield stress of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the firmness of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the spreadability of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the adhesiveness of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the lightness (color L* parameters) of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the color a* parameters of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>The effect of high hydrostatic pressure induction parameters on the color b* parameters of inulin–soy protein hydrogels (the average (n = 3) values marked with different letter symbols differ significantly (<span class="html-italic">p</span> &lt; 0.05)).</p>
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<p>Principal component analysis (PCA): (<b>a</b>) Score plot: F1 versus F2 of all samples. (<b>b</b>) Score plot: F1 versus F2 of data from determinations used as variables.</p>
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<p>Principal component analysis (PCA): (<b>a</b>) Score plot: F1 versus F2 of all samples. (<b>b</b>) Score plot: F1 versus F2 of data from determinations used as variables.</p>
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