Search Results (78)

Sweet potato is a valuable root due to its nutritional benefits, health-promoting properties, and technological applications. The peel, often discarded during food processing, can be employed in the food industry, supporting a circular economy. Purple sweet potato peel (PSPP) is rich in anthocyanins, which can be used as natural colourants and antioxidants. Optimising their extraction can enhance yield and reduce costs. The current work aimed to optimize anthocyanin and antioxidant recovery from PSPP using a Box-Behnken design and sonotrode ultrasound-assisted extraction (sonotrode-UAE). Three independent variables were analysed: extraction time (2–6 min), ethanol concentration (35–85%), and liquid-to-solid ratio (10–30 mL/g). The dependent variables included total monomeric anthocyanin content (TMAC), individual anthocyanins, and antioxidant activity. TMAC in 15 extracts ranged from 0.16 to 2.66 mg/g PSPP. Peonidin-3-caffeoyl-p-hydroxybenzoyl sophoroside-5-glucoside was the predominant anthocyanin. Among four antioxidant assays, Ferric-reducing antioxidant power (FRAP) showed the highest value. Ethanol concentration significantly influenced anthocyanin and antioxidant recovery (p < 0.05). The model demonstrated adequacy based on the coefficient of determination and variation. Optimal extraction conditions were 6 min with 60% ethanol at a 30 mL/g ratio. Predicted values were validated experimentally (coefficient of variation <10%). In conclusion, PSPP is a promising matrix for obtaining anthocyanin-rich extracts with antioxidant activity, offering potential applications in the food industry. Full article

Phenolic compounds are secondary metabolites widely distributed among plants, with bioactive properties, especially antioxidant activity. The search for sustainable extraction methods has driven the use of natural deep eutectic solvents (NaDESs), formed by combinations of natural compounds, such as organic acids, sugars, alcohols, and amino acids. This study optimized NaDES (sorbitol, citric acid, and glycine) efficiency and compared it to that of 70% methanol solution in extracting total soluble phenolic compounds (TSPCs) from six flours matrices—corn, buckwheat, biofortified orange sweet potato, red lentil, Sudan grass, and chickpea—before and after thermoplastic extrusion cooking. Quantification was performed using the Folin–Ciocalteu method, with statistical analysis at the 10% significance level. In general, the methanolic extracts showed higher TSPC levels in the raw materials, whereas the levels were higher in NaDESs for legumes. After extrusion, a reduction in the TSPC levels was observed, except in the sweet potato. Multivariate analysis (PLS-DA and heatmap) distinguished the raw and extruded samples, revealing structural and chemical changes from thermal processing. The AGREE scores were 0.7 (NaDES) and 0.54 (methanol), favoring NaDES. The BAGI score (75.0) confirmed the method’s robustness and suitability for sustainable analytical applications. Full article

Internet-of-Things (IoT) approaches are continually introducing new sensors into the fields of agriculture and animal welfare. The application of multi-sensor data fusion to these domains remains a complex and open-ended challenge that defies straightforward optimization, often requiring iterative testing and refinement. To respond to this need, we have created a new open-source framework as well as a corresponding Python tool which we call the “Data Fusion Explorer (DFE)”. We demonstrated and evaluated the effectiveness of our proposed framework using four early-stage datasets from diverse disciplines, including animal/environmental tracking, agrarian monitoring, and food quality assessment. This included data across multiple common formats including single, array, and image data, as well as classification or regression and temporal or spatial distributions. We compared various pipeline schemes, such as low-level against mid-level fusion, or the placement of dimensional reduction. Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. Additionally, independent component analysis outperformed principal component analysis slightly in a sweet potato imaging dataset. Lastly, we benchmarked the DFE tool with respect to the Vanilla Python3 packages using our four datasets’ pipelines and observed a significant reduction, usually more than 50%, in coding requirements for users in almost every dataset, suggesting the usefulness of this package for interdisciplinary researchers in the field. Full article

The aim of the present study was to investigate how different drying techniques (lyophilization, convective drying, and osmotic dehydration) affect the phytochemical profile, biological activities, color parameters, and antimicrobial potential of sweet potato peel from four varieties (white, pink, orange, and purple). Lyophilized orange peel showed the highest carotenoid content (21.31 mg β-carotene/100 g), while osmotic dehydration resulted in the highest retention of anthocyanins in purple peel (229.58 mg cyanidin-3-glucoside/100 g). Among phenolic compounds, the most abundant were caffeic and cinnamic acids, reaching up to 434.57 mg/100 g and 430.91 mg/100 g, respectively, in white peel. Antioxidant activity was strongest in purple peel, particularly in lyophilized samples. Convective drying enhanced anti-inflammatory activity in orange peel (68.25% inhibition), and all samples demonstrated significant α-glucosidase inhibition, with values up to 96.93%. Antimicrobial effects were observed only in purple peel extracts, which showed strong antifungal activity, especially against Saccharomyces cerevisiae (inhibition zone >50 mm). These results confirm that sweet potato peel holds considerable potential as a functional ingredient and that its bioactive value can be significantly influenced by the drying method applied. Full article

The root-knot nematode Meloidogyne enterolobii has emerged as a devastating pathogen in global agricultural systems. Its geographic distribution is progressively expanding from tropical to temperate zones, leading to difficulties in discerning the symptoms it causes from those of congeners such as M. incognita. Currently, some molecular diagnostic technologies (e.g., qPCR) have been established for detecting M. enterolobii, but these methods fail to meet field-based detection demands due to their reliance on laboratory-grade thermocyclers. We thus developed a method for detecting M. enterolobii based on enzyme-mediated duplex exponential amplification (EmDEA) technologies to address this issue. The EmDEA detection method demonstrated strict specificity for the target species, showing no amplification in 13 non-target nematodes or host tissue samples. Sensitivity analyses revealed detection limits of 3.6 × 10⁻⁴ ng/μL (purified DNA), 1/1000 of an individual nematode (single-organism detection), 8.97 nematodes/g sweet potato, and 4.08 nematodes/100 g soil, achieving equivalent performance to qPCR. Field validation confirmed successful on-site detection, with significantly higher nematode loads in root tissues (50.41–97.62 nematodes/g) than in rhizospheric soil (1.07–1.28 nematodes/g). The established detection method employs a 42 °C isothermal amplification technology paired with a palm-sized thermal module, enabling field-deployable detection. Its unique duplex exponential amplification mechanism achieves threshold determination 10 cycles (~10 min) faster than conventional qPCR. When integrated with rapid DNA extraction protocols, the entire workflow is completed within 40 min, improving detection efficiency. This study provides a molecular tool for the precise monitoring of M. enterolobii, offering critical support for formulating targeted control strategies. Full article

Previous research has shown that fractions outperformed extracts in pharmacological activity and safety. This study assessed the total phenol and flavonoid content, as well as antioxidant and xanthine oxidase inhibitory (XOI) activities, of purple-orange sweet potato extracts, fractions, and subfractions. Using UV-visible spectrophotometry, the leaves showed the highest values for total phenol, flavonoid, 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Antioxidant Power (FRAP), Cupric Ion Reducing Antioxidant Capacity (CUPRAC) assays, and XOI activity. The sequential extraction of the leaves yielded ethyl acetate extract as the most potent, with a yield of 10.4%, a DPPH assay result of 511.212 ± 0.416 mg ascorbic acid equivalent antioxidant capacity (AEAC)/g, and XOI activity of 45.192 ± 4.981 mg allopurinol equivalent xanthine inhibitory capacity (AEXIC)/g. CF5 had the greatest DPPH assay (158.475 ± 0.170 mg AEAC/g), FRAP assay (86.849 ± 0.048 mg AEAC/g), CUPRAC assay (1008.892 ± 1.620 mg AEAC/g), and XOI activity (6.062 ± 1.730 mg AEXIC/g) values. Subfraction CSF3 from fraction CF5 was analyzed using UPLC-MS/MS and revealed the presence of compounds such as cholest-4-en-3-one, 4-hydroxy-6-[2-(2-methyl-1,2,4a,5,6,7,8,8a-octahydronaphthalen-1-yl) ethyl] oxan-2-one, tangeritin, 4-methyl benzophenone, benzophenone, (+)-ar-turmerone, 4-methoxycinnamic acid, and ricinine. This study was the first to report xanthine oxidase inhibitory activity in allopurinol equivalence. The leaves of the purple-orange sweet potato showed potential as a source of natural antioxidants. Full article

Sweet potatoes play an important role in the global food supply, as they are rich in bioactive components and have numerous health benefits. Their minimally processed, ready-to-eat form is increasingly popular among consumers; however, discoloration and microbiological problems threaten the safety of these products. The aim of this study is to investigate the shelf life of cleaned, cut, ready-to-eat, vacuum-packed, and refrigerated Bonita (white) and Covington (orange) varieties of sweet potatoes after soaking in apple and chokeberry pomace extracts and treatment with citric and ascorbic acids. A series of microbiological and analytical tests was conducted during the storage period. The microbiological tests included the enumeration of cells of mesophilic aerobic and facultative anaerobic microbes, as well as lactobacilli, lactococci, Enterobacteriaceae, yeasts, and moulds. The analytical tests encompassed the determination of the total phenolic content, antioxidant capacity, water-soluble solid content, and pH value. The prevalent microbial groups detected in the examined sweet potato varieties were lactic acid bacteria, which were present in both fresh samples and following storage. This study established that low-temperature refrigeration (5 °C), vacuum packaging, and organic acid treatment can effectively control lactic acid bacteria, which are pivotal to spoilage. The combination of preservation steps is of particular significance for ready-to-cook sweet potatoes, as this approach effectively extends the shelf life of these products. Full article

This study aims to optimize the parameters for the ultrasound-assisted extraction of cellulose nanocrystals (CNCs) from scented pandan leaf waste and to enhance the properties of edible films reinforced with CNC. The CNC extraction conditions were optimized using response surface methodology (central composite design) by varying two independent variables, including amplitude (25.86% to 54.14%) and ultrasonication time (11.89 min to 33.11 min). The optimal extraction conditions were 50% amplitude and 30 min ultrasonication, providing CNCs with the highest extraction yield (29.85%), the smallest crystallite size (5.85 nm), and the highest crystallinity index (59.32%). The extracted CNCs showed favorable physicochemical properties, including a zeta potential of −33.95 mV, an average particle diameter of 91.81 nm, and a polydispersity index of 0.26. Moreover, sweet potato starch (SPS)-based films incorporating various CNC concentrations (0, 2, 4, 6, and 8%) were fabricated. Increasing CNC concentrations improved key film properties, including thickness, moisture content, water vapor permeability, tensile strength, light transmittance, and color. Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM) analyses confirmed hydrogen bonding, crystallinity, and uniform CNC distribution within the film as CNC content increased. These findings highlight ultrasound-assisted extraction as an efficient method for producing high-quality CNCs from pandan leaf waste, offering sustainable nanofillers to enhance biodegradable edible films. Full article

Bioactive phytochemicals, such as polyphenols, play vital roles in human health, but conventional extraction methods rely on hazardous solvents. This study establishes natural deep eutectic solvents (NADESs) as versatile and environmentally friendly alternatives for recovering a variety of bioactive compounds from plant materials. Five choline chloride-based NADESs were evaluated for their effectiveness in extracting betalains (from beetroot), carotenoids (from carrot and sweet potato), anthocyanins (from chokeberry pomace and red onion), and polyphenols (from Lonicera japonica flowers, hop cones, rowan berries, and spent coffee grounds). Notably, NADES2 outperformed water in betalain recovery (179.86 mg of betanin/100 g of beetroot), while NADES4 (choline chloride-urea, 1:2 molar ratio) matched the polyphenol extraction efficiency of ethanol. Using L. japonica flowers as a model for optimization, Response Surface Methodology (RSM) identified the solvent ratio and temperature as critical extraction parameters, using high ratios (12:1–15:1 v/w) and moderate heat (55–75 °C) to maximize recovery. NADES4 emerged as a high-performing solvent, achieving a total phenolic content (TPC) of 75.94 mg chlorogenic acid/g and antioxidant activity of 451.00 µmol Trolox/g under the following conditions: 60% aqueous dilution, 15:1 solvent ratio, and 80 °C, 30 min. These findings highlight NADESs as a green, tunable solvent system for phytochemical extraction across plant species, offering enhanced efficiency, reduced environmental impact, and alignment with sustainable practices. Full article

This study investigates the early detection of sweet potato scab by using hyperspectral imaging and machine learning techniques. The research focuses on developing an accurate, economical, and non-destructive approach for disease detection and grading. Hyperspectral imaging experiments were conducted on two sweet potato varieties: Guangshu 87 (resistant) and Guicaishu 2 (susceptible). Data preprocessing included denoising, region of interest (ROI) selection, and average spectrum extraction, followed by dimensionality reduction using principal component analysis (PCA) and random forest (RF) feature selection. A novel dynamic grading method based on spectral-time data was introduced to classify the early stages of the disease, including the early latent and early mild periods. This method identified significant temporal spectral changes, enabling a refined disease staging framework. Key wavebands associated with sweet potato scab were identified in the near-infrared range, including 801.8 nm, 769.8 nm, 898.5 nm, 796.4 nm, and 780.5 nm. Classification models, including K-nearest neighbor (KNN), support vector machine (SVM), and linear discriminant analysis (LDA), were constructed to evaluate the effectiveness of spectral features. Among these classification models, the MSC-PCA-SVM model demonstrated the best performance. Specifically, the Susceptible Variety Disease Classification Model achieved an overall accuracy (OA) of 98.65%, while the Combined Variety Disease Classification Model reached an OA of 95.38%. The results highlight the potential of hyperspectral imaging for early disease detection, particularly for non-destructive monitoring of resistant and susceptible sweet potato varieties. This study provides a practical method for early disease classification of sweet potato scab, and future research could focus on real-time disease monitoring to enhance sweet potato crop management. Full article

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases. Full article

Accurate prediction of sweet potato yield is crucial for effective crop management. This study investigates the use of vegetation indices (VIs) extracted from multispectral images acquired by a small unmanned aerial vehicle (UAV) throughout the growing season, along with in situ-measured plant physiological parameters, to predict sweet potato yield. The data acquisition process through UAV field imaging is discussed in detail along with the extraction process for the multispectral bands that we use as features. The experiment is designed with a combination of different nitrogen application rates and cover crop treatments. The dependence of VIs and crop physiological parameters, such as leaf chlorophyll content, plant biomass, vine length, and leaf nitrogen content, on yield is evaluated through feature selection methods and model performance. Classical machine learning (ML) approaches and tree-based algorithms, like XGBoost and Random Forest, are implemented. Additionally, a soft-voting ML model ensemble approach is employed to improve performance of yield prediction. Individual models are trained and tested for different cover crop and nitrogen treatments to capture the relationships between the treatments and the target yield variable. The performance of the ML algorithms is evaluated using various popular model performance metrics like R², RMSE, and MAE. Through modelling the data for cover crops and nitrogen treatment rates using individual models, the relationships and effects of different treatments on yield are explored. Important VIs useful for the study are identified through feature selection and model performance evaluation. Full article

Sweet potato leaves (SPL), an agricultural byproduct, hold significant potential in dermatological applications due to their bioactive compounds. This study evaluates the phenolic profile of SPL extracts and investigates their biological activities relevant to skin health. Extract fingerprinting, through uHPLC-DAD and LC–MS, identified phenolic acids and flavonoids, with cynarin, neochlorogenic acid, and spiraeoside being predominant. The presence of hyperoside was detected. From a biological standpoint, SPL demonstrated notable antimicrobial activity, with MICs ranging from 2.5 to 5 mg/mL against various bacterial strains, such as MRSA and P. aeruginosa, and effective antibiofilm activity, as it reduced biofilm formation by over 80% for most tested strains. When evaluating its effect upon keratinocytes, the cytotoxicity assessment revealed safe usage concentrations at 111 µg/mL and immunomodulatory capacities, as it increased IL-6 production in unchallenged cells but had no synergistic effects under inflammatory stimuli. While preliminary, and with more assays being necessary, these findings highlight SPL’s potential as a natural agent for antimicrobial and anti-inflammatory applications in skin-related applications and open a new avenue for a possible added value application of SPL. Full article

Peanut skin is a potential medicinal material. The adulteration of peanut skin samples with starchy substances severely affects their medicinal value. This study aimed to quantitatively analyze the adulterants present in peanut skin using Fourier transform near-infrared (FT-NIR) spectroscopy. Two adulterants, sweet potato starch and corn starch, were included in this study. First, spectral information of the adulterated samples was collected for characterization. Then, the applicability of different preprocessing methods and techniques to the obtained spectral data was compared. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to extract effective variables from the preprocessed spectral data, and Partial Least Squares Regression (PLSR), a Support Vector Machine (SVM), and a Black Kite Algorithm-Support Vector Machine (BKA-SVM) were employed to predict the adulterant content in the samples, as well as the overall adulteration level. The results showed that the BKA-SVM model performed excellently in predicting the content of sweet potato starch, corn starch, and overall adulterants, with determination coefficients ($R_P^2$) of 0.9833, 0.9893, and 0.9987, respectively. The experimental results indicate that FT-NIR spectroscopy combined with advanced machine learning techniques can effectively and accurately detect adulterants in peanut skin, providing a reliable technological support for food safety detection. Full article

Edible sweet potato leaf can be exploited in the management and treatment of insulin resistance. This study investigated the effects of three sweet potato leaf extracts (SPLEs) and chlorogenic acid (CGA) on glucose uptake (2-NBDG uptake and GLUT4 abundance) and expression of their related regulatory factors (such as IR, IRS-1, p-Akt1, and p-AMPKα1 abundances) using Western blot analysis in insulin-treated insulin-resistant C2C12 cells. The results show that both purple and green SPLEs improved glucose (2-NBDG) uptake efficacy with insulin treatments, and both SPLEs also increased GLUT4 and IR abundances via activation of p-Akt in the PI3K/Akt pathway, whereas the IR abundance efficacy influence was the same as in the insulin-treated group. The yellow SPLE and CGA have higher protein abundances of IR and IRS-1, while CGA (20 μg/mL) exhibits the highest abundance of p-Akt1 and p-AMPKα1. These results suggest potential benefits of purple and green SPLEs in promoting glucose uptake, possibly through modulation of insulin signaling pathways. Full article