Search Results (735)

Mangoes are an important part of Hainan’s tropical characteristic agriculture. In response to the requirements of building an ecological civilization pilot demonstration zone in Hainan, China, green and sustainable development will be the future development trend of the mango planting system. However, the economic benefits and environmental impact during its planting and management process remain unclear. This paper combines emergy, life cycle assessment (LCA), and economic analysis to compare the system sustainability, environmental impact, and economic benefits of the traditional mango cultivation system (TM) in Dongfang City, Hainan Province, and the early-maturing mango cultivation system (EM) in Sanya City. The emergy evaluation results show that the total emergy input of EM (1.37 × 10¹⁶ sej ha⁻¹) was higher than that of TM (1.32 × 10¹⁶ sej ha⁻¹). From the perspective of the emergy index, compared with TM, EM exerted less pressure on the local environment and has better stability and sustainability. This was due to the higher input of renewable resources in EM. The LCA results showed that based on mass as the functional unit, the potential environmental impact of the EM is relatively high, and its total environmental impact index was 18.67–33.19% higher than that of the TM. Fertilizer input and On-Farm emissions were the main factors causing environmental consequences. Choosing alternative fertilizers that have a smaller impact on the environment may effectively reduce the environmental impact of the system. The economic analysis results showed that due to the higher selling price of early-maturing mango, the total profit and cost–benefit ratio of the EM have increased by 55.84% and 36.87%, respectively, compared with the TM. These results indicated that EM in Sanya City can enhance environmental sustainability and boost producers’ annual income, but attention should be paid to the negative environmental impact of excessive fertilizer input. These findings offer insights into optimizing agricultural inputs for Hainan mango production to mitigate multiple environmental impacts while enhancing economic benefits, aiming to provide theoretical support for promoting the sustainable development of the Hainan mango industry. Full article

The potential of a protein to cause an allergic reaction is often assessed using a variety of computational techniques. Leveraging advances in high-throughput protein sequence data coupled with in silico or computational methods can be used to systematically analyze large proteomes for allergenic potential. Despite mango’s widespread consumption and growing clinical reports of hypersensitivity, the full extent of their allergenicity is yet unknown. In this study, for the first time, we conducted a genome-wide in silico analysis by analyzing a total of 54,010 protein sequences to identify the complete spectrum of potential mango allergens. These proteins were analyzed using various bioinformatics tools to predict their allergenic potential based on sequence similarity, structural features, and known allergen databases. In addition to the known mango allergens, including Man i 1, Man i 2, and Man i 3, our findings demonstrated that several isoforms of cysteine protease, non-specific lipid-transfer protein (LTP), legumin B-like, 11S globulin, vicilin, thaumatin-like protein, and ervatamin-B family proteins exhibited strong allergenic potential, with >80% 3D epitope identity, >70% linear 80 aa window identity, and matching with >80 known allergens. Thus, a genome-wide in silico study provided a comprehensive profile of the possible mango allergome, which could help identify the low-allergen-containing mango cultivars and aid in the development of accurate assays for variety-specific allergic reactions. Full article

Mango is highly sensitive to heat stress, which directly affects the yield and quality. The extreme heat waves of 2024, with temperatures reaching 41–47 °C over 25 days, caused significant impacts on sensitive cultivars. The impact of heat waves on ten commercial cultivars from subtropical regions viz.,‘Dashehari’, ‘Langra’, ‘Chausa’, ‘Bombay Green’, ‘Himsagar’, ‘Amrapali’, ‘Mallika’, ‘Sharda Bhog’, ‘Kesar’, and ‘Rataul’, and thirteen selected elite hybrids H-4208, H-3680, H-4505, H-3833, H-4504, H-1739, H-3623, H-1084, H-4264, HS-01, H-949, H-4065, and H-2805, is reported. The predominant effects that were observed include the following: burning symptoms or blackened tips, surrounded by a yellow halo, with premature ripening in affected parts and, in severe cases, tissue mummification. Among commercial cultivars, viz., ‘Amrapali’ (25%), ‘Mallika’ (30%), ‘Langra’ (30%), ‘Dashehari’ (50%), and ‘Himsagar’ and ‘Bombay Green’ had severe impacts, with ~80% of fruits being affected, followed by ‘Sharda Bhog’. In contrast, mid-maturing cultivars like ‘Kesar’, ‘Rataul’, and late-maturing elite hybrids, which were immature during the stress period, showed no symptoms, indicating they are tolerant. Biochemical analyses revealed significantly elevated total soluble solids (TSS > 25 °B) in affected areas of sensitive genotypes compared to non-affected tissues and tolerant genotypes. Aroma profiling indicated variations in compounds such as caryophyllene and humulene between affected and unaffected parts. The study envisages that the phenological maturity scales are indicators for the selection of climate-resilient mango varieties/hybrids and shows potential for future breeding programs. Full article

In today’s industrial applications, cutting fluids have attained prime importance due to their all-round features, including increase of tool life by lubrication of the tool at the tool–workpiece interface. This study compares the effects of mango seed oil and pongamia oil on cutting force and surface morphology during the turning of AISI 304 steel. The design of experiments was applied using Taguchi’s method with an L9 array of experiments. During the experiment, it was discovered that mango seed and pongamia-based cutting fluid exhibited the lowest contact angles of 22.1° and 24.4°, respectively, at a 97:3 volumetric concentration of deionized water and eco-friendly oil. The use of mango seed oil as a cutting fluid with MQL (Minimum Quantity Lubrication) resulted in the lowest surface roughness of 0.809 µm, compared to 0.921 µm with pongamia-based cutting fluid. The cutting force was reduced by a maximum of 28.68% using mango seed-based cutting fluid, compared to pongamia-based cutting fluid. ANOVA analysis revealed that feed rate had the maximum influence on the optimization of output parameters for mango seed cutting fluid. For pongamia-based cutting fluid, feed rate had the maximum influence on cutting force, while the depth of cut had the strongest influence on surface roughness. Full article

This study aimed to develop a fermented mango-based beverage using Lactiplantibacillus plantarum strains Lp6 and Lp32, focusing on enhancing its functional properties, ensuring microbiological safety, improving nutritional value, and achieving sensory acceptability. A central composite design (CCD) was employed to assess the effects of two factors (fermentation time and inoculum concentration) on several response variables: viable cell concentration (CC), total phenolic compounds (TPCs), total flavonoid compounds (TFCs), and concentrations of L-lactic acid and D-lactic acid. The optimized formulation was achieved using L. plantarum Lp6, with an inoculum concentration of 9.89 Log (7.76 × 10⁹) CFU/mL and a fermentation time of 20.47 h. Under these conditions, the beverage reached the highest values for CC, TPC, TF, and L-lactic acid while minimizing the production of D-lactic acid. Following optimization, the fermented beverage underwent further characterization, including physicochemical analysis, microbiological evaluation, proximate composition analysis, and sensory evaluation. The final product exhibited a viable cell count of 13.01 Log (10.23 × 10¹²) CFU/mL, demonstrated functional potential, complied with microbiological safety standards, and showed adequate nutritional content. Sensory analysis revealed high consumer acceptability, attributed to its distinctive mango aroma and flavor. These findings highlight the potential of this fermented mango-based beverage as a novel functional food with promising market appeal. Full article

B-box (BBX) transcription factors are critical regulators of light-mediated anthocyanin biosynthesis, influencing peel coloration in plants. To explore their role in red mango cultivars, we identified 32 BBX genes (MiBBX1–MiBBX32) in the mango (Mangifera indica L.) genome using a genome-wide analysis. Phylogenetic and structural analyses classified these genes into five subfamilies based on conserved domains. A collinearity analysis revealed segmental duplication as the primary mechanism of MiBBX gene family expansion, with purifying selection shaping their evolution. A promoter analysis identified numerous light- and hormone-responsive cis-elements, indicating regulatory roles in the light and hormonal signaling pathways. Expression profiling in the ‘Sensation’ cultivar revealed organ-specific patterns, with several MiBBX genes showing higher expression in the peel than in the flesh. Many of these genes also consistently exhibited elevated expression in the peel of red-skinned cultivars (‘Sensation’ and ‘Guifei’) compared to yellow and green cultivars, suggesting their role in red peel pigmentation. Furthermore, postharvest light treatment of ‘Hongmang No. 6’ fruit significantly upregulated multiple MiBBX genes, suggesting their involvement in light-induced anthocyanin accumulation in red mango peel. These findings provide valuable insights into the molecular mechanisms governing light-regulated peel coloration in mango and establish a foundation for functional studies of MiBBX genes in fruit pigmentation. Full article

Postharvest fungal diseases are a major cause of fruit spoilage and economic losses, particularly in perishable commodities like strawberries. In this study, a plant-derived Weissella paramesenteroides strain R2 was isolated from the mango fruit surface and evaluated for its antifungal potential. Dual-culture assays revealed the strong inhibitory activity of strain R2 against key postharvest pathogens, including Botrytis cinerea, Colletotrichum gloeosporioides, and Fusarium oxysporum. Notably, cell-free fermentation broth exhibited no antifungal activity, whereas the volatile organic compounds (VOCs) produced by R2 significantly suppressed fungal growth in sealed plate assays. GC-MS analysis identified 84 VOCs, with pyrazines as the dominant group. Three major compounds, 2,5-dimethylpyrazine, 2,4-di-tert-butylphenol, and 2-furanmethanol, were validated for their antifungal activity. The application of R2 VOCs in strawberry preservation significantly reduced disease incidence and severity during storage. These findings highlight W. paramesenteroides R2 as a promising, food-safe biocontrol agent for postharvest disease management via VOC-mediated mechanisms. Full article

The accurate estimation of evapotranspiration ($ET$) and its components are vital for water resource management and irrigation planning. This study models tree transpiration ($T$) and $ET$ for grapefruit, litchi, and mango orchards using light gradient-boosting machine (LightGBM) optimized using the Bayesian hyperparameter optimization. Grounds $T$ and $ET$ for these crops were measured using the heat ratio method of monitoring sap flow and the eddy covariance technique for quantifying $ET$. The Sentinel 2 satellite was used to compute field leaf area index ($LAI$). The modelled data were used to partition the orchard $ET$ into beneficial ($T$) and non-beneficial water uses (orchard floor evaporation—$E_s$). We adopted the 10-fold cross-validation to test the model robustness and an independent validation to test performance on unseen data. The 10-fold cross-validation and independent validation on $ET$ and $T$ models produced high accuracy with coefficient of determination ($R^2$) $\ge$ 0.88, Kling–Gupta efficiency ($KGE$) $\ge$ 0.91, root mean square error ($RMSE$) $\le$ 0.04 mm/h, and mean absolute error ($MAE$) $\le$ 0.03 mm/h for all the crops. The study demonstrates that LightGBM can accurately model the transpiration and evapotranspiration for subtropical tree crops using Sentinel 2 data. The study found that $E_s$ which combined soil evaporation and understorey vegetation transpiration contributed 35, 32, and 31% to the grapefruit, litchi and mango orchard evapotranspiration, respectively. We conclude that improvements on orchard floor management practices can be utilized to minimize non-beneficial water losses while promoting the productive water use ($T$). Full article

Mango, as an important economic crop in tropical and subtropical regions, suffers from chilling injuries caused by postharvest low-temperature storage, which seriously affect its quality and economic benefits. Traditional detection methods have limitations such as low efficiency and strong destructiveness. This study designs and implements a flexible visible light spectral sensing system based on visible light spectral sensing technology and low-cost environmentally friendly flexible circuit technology. The system is structured based on a perception-analysis-warning-processing framework, utilizing laser-induced graphene electroplated copper integrated with laser etching technology for hardware fabrication, and developing corresponding data acquisition and processing functionalities. Taking Yunnan Yumang as the research object, a three-level chilling injury label dataset was established. After Z-Score standardization processing, the prediction accuracy of the SVM (Support Vector Machine) model reached 95.5%. The system has a power consumption of 230 mW at 4.5 V power supply, a battery life of more than 130 days, stable signal transmission, and a monitoring interface integrating multiple functions, which can provide real-time warning and intervention, thus offering an efficient and intelligent solution for chilling injury monitoring in mango cold chain storage. Full article

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Full article

The aim of this study was to evaluate the effect of the iota, kappa and lambda carrageenan fractions on the physical properties and crystal structure of a fruit sorbet prepared from frozen mango fruits. During this study, physical properties such as density, cryoscopic temperature, osmotic pressure, overrun and melting time were analyzed. In order to assess the crystal structure and its changes, microscope images were taken of each sample after 1, 30 and 90 days of storage. The stabilizers showed no significant effect on the physical properties of the ice cream mixture; however, the sample with iota carrageenan stood out for having the highest overrun (58.7%) and the sample with kappa carrageenan took the longest to melt of all tested samples (almost 21 min). This study shows a significant effect of carrageenans in reducing the initial size of ice crystals as well as reducing recrystallization during storage. The stabilizing blend using ι-carrageenan provided the most effective cryoprotective properties, with an ice crystal diameter of 9 µm. Full article

Agriculture in India supports millions of livelihoods and is a major force behind economic expansion. Challenges in modern agriculture depend on environmental factors (such as soil quality and climate variability) and biotic factors (such as pests and diseases). These challenges can be addressed by advancements in technology (such as sensors, internet of things, communication, etc.) and data-driven approaches (such as machine learning (ML) and deep learning (DL)), which can help with crop yield and sustainability in agriculture. This study introduces an innovative deep neural network (DNN) approach for identifying leaf diseases in paddy crops at an early stage. The proposed neural network is a hybrid DL model comprising feature extraction, channel attention, inception with residual, and classification blocks. Channel attention and inception with residual help extract comprehensive information about the crops and potential diseases. The classification module uses softmax to obtain the score for different classes. The importance of each block is analyzed via an ablation study. To understand the feature extraction ability of the modules, extracted features at different stages are fed to the SVM classifier to obtain the classification accuracy. This technique was experimented on eight classes with 7857 paddy crop images, which were obtained from local paddy fields and freely available open sources. The classification performance of the proposed technique is evaluated according to accuracy, sensitivity, specificity, F1 score, MCC, area under curve (AUC), and receiver operating characteristic (ROC). The model was fine-tuned by setting the hyperparameters (such as batch size, learning rate, optimizer, epoch, and train and test ratio). Training, validation, and testing accuracies of 99.91%, 99.87%, and 99.49%, respectively, were obtained for 20 epochs with a learning rate of 0.001 and sgdm optimizer. The proposed network robustness was studied via an ablation study and with noisy data. The model’s classification performance was evaluated for other agricultural data (such as mango, maize, and wheat diseases). These research outcomes can empower farmers with smarter agricultural practices and contribute to economic growth. Full article

A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. After spectra were acquired of the stored fruit, the fruit cheeks were cut longitudinally to allow visual assessment of the incidence of the internal disorders. Five models were evaluated: two tree-based algorithms (J48 and random forest), one neural network (multilayer perceptron, MLP), and two SVM training algorithms (sequential minimal optimization, SMO, and LibSVM). The models were evaluated using a tenfold cross-validation approach. Non-destructive discrimination of health from all disordered and healthy fruit from fruit with specific disorders was achieved with an accuracy ranging from 72.3 to 97.0% when using spectra collected at harvest and 63.7 to 96.2% when using spectra collected after ripening. No one machine learning algorithm out-performed other methods—for spectra collected at harvest, the highest discrimination accuracy was achieved with RF and MLP for black flesh, J48 for spongy tissue, and LibSVM for soft nose and jelly seed. For spectra collected of stored fruit, the highest discrimination accuracy was achieved with SMO for jelly seed and RF for soft nose. A recommendation is made for the consideration of ensemble models in future. The ability to predict the development of the disorder using spectra of at-harvest fruit offers the potential to guide postharvest practices and reduce incidence of internal disorders in mangoes. Full article

Mango peel (MP), an abundant agro-industrial residue, was evaluated as a solid biofuel using combined physicochemical characterisation and non-isothermal thermogravimetric kinetics (TGA). Fourier transform infrared (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) revealed hydroxyl-rich surfaces and porous microstructures. Thermogravimetric combustion, conducted at heating rates of 20–80 K min⁻¹, displayed three distinct stages. These stages correspond to dehydration (330–460 K), hemicellulose/cellulose oxidation (420–590 K), and cellulose/lignin oxidation (540–710 K). Kinetic analysis using six model-free methods (Friedman (FR), Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), Starink (STK), Kissinger (K), and Vyazovkin (VY)) yielded activation energies (E_a) of 52–197 kJ mol⁻¹, increasing with conversion (mean E_a ≈ 111 kJ mol⁻¹). Coats–Redfern (CR) fitting confirmed a three-dimensional diffusion mechanism (D3, R² > 0.99). Thermodynamic analysis revealed that the formation of the activated complex is endothermic, with activation enthalpy (ΔH) values of 45–285 kJ mol⁻¹. The process was found to be non-spontaneous under the studied conditions, with Gibbs free energy (ΔG) values ranging from 83 to 182 kJ mol⁻¹. With a high heating value (HHV) of 21.9 MJ kg⁻¹ and favourable combustion kinetics, MP is a promising supplementary fuel for industrial biomass boilers. However, its high potassium oxide (K₂O) content requires dedicated ash management strategies to mitigate slagging risks, a key consideration for its practical, large-scale application. Full article

Background: Acute visceral pain is among the most common symptoms of patients seeking in-hospital treatment and is related to various thoracic, abdominal, and pelvic diseases. It is characterized by distinguishable sensory qualities and can be described on a sensory-discriminative and affective-motivational level. These sensory qualities correlate with the activation of cerebral areas involved in the neuronal processing of visceral pain and can be visualized using functional neuroimaging. Methods: An ALE (activation likelihood estimation) meta-analysis of a total of 21 studies investigating different balloon distention paradigms during either PET or fMRI was performed to demonstrate the neuroanatomical correlates of visceral pain. The ALE meta-analysis was performed using the GingerAle software version 3.0.2 and was displayed with the Mango software 4.1 on an anatomical MNI template. Results: Summarizing studies investigating the functional neuroanatomy of visceral pain, bihemispheric activation of the insula, the thalamus, and clusters involving the right inferior parietal lobe/postcentral gyrus as well as the left postcentral gyrus/parietal inferior lobe were observed. Conclusions: This ALE meta-analysis substantiates the concept of two distinguishable neuroanatomical pathways of visceral pain which are related to either the sensory-discriminative or the affective-motivational dimension of pain processing. Full article