Food Analytical Methods最新文献

{"title":"One-step Magnetic SERS Platform for Rapid Capture and Sensitive Quantification of Sudan I–IV in Chili Powder and Tomato Sauce Samples","authors":"Guican Deng,&nbsp;Yonghua Bao,&nbsp;Yanping Hong,&nbsp;Jiaxin Hong,&nbsp;Luying Duan,&nbsp;Huaying Du,&nbsp;Zhimei Tan,&nbsp;Chunrong Wang","doi":"10.1007/s12161-026-03314-2","DOIUrl":"10.1007/s12161-026-03314-2","url":null,"abstract":"<div><p>As illegal synthetic colorants in food, Sudan dyes possess carcinogenic potential and can severely threaten human health following long-term intake. Rapid and sensitive detection of Sudan dyes in complex food matrices (e.g., chili powder and tomato sauce) remains a critical challenge for food safety supervision. Herein, a facile and sensitive detection strategy targeting Sudan I, II, III, and IV was established by combining magnetic substrates with surface-enhanced Raman spectroscopy (SERS). The influence of Au NPs loading on SERS analytical performance was systematically investigated, and FSA-3 was identified as the optimal substrate. Under optimized conditions, the limits of detection (LODs) for Sudan I–IV in chili powder and tomato sauce were determined to be 0.75–11.5 μg/kg, with satisfactory recoveries ranging from 84.5% to 110%. Compared with previously reported SERS methods and conventional analytical techniques, the proposed method exhibits excellent resistance to matrix interference capability and enables sensitive and efficient detection. This work provides a reliable and efficient alternative for the rapid screening of Sudan dye residues in complex food matrices, holding great promise for practical food safety applications.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149176565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facile Synthesis of a Magnetic Covalent Organic Framework for Highly Efficient Simultaneous Magnetic Solid-Phase Extraction of Triazole Fungicide Residues","authors":"Teeraparb Jullee,&nbsp;Nattiya Duangdee,&nbsp;Dararat Saenkam,&nbsp;Phatchara Rattanaphonsaen,&nbsp;Wannipha Khiaophong,&nbsp;Chutharat Maokam,&nbsp;Rawikan Kachangoon,&nbsp;Siriboon Mukdasai,&nbsp;Jitlada Vichapong","doi":"10.1007/s12161-026-03306-2","DOIUrl":"10.1007/s12161-026-03306-2","url":null,"abstract":"<div><p>In this study, an efficient magnetic solid-phase extraction (MSPE) method was established using a magnetic covalent organic framework (mCOF)-based sorbent for the determination of triazoles prior to high-performance liquid chromatography (HPLC) analysis. A magnetic covalent organic framework (mCOF) nanocomposite was fabricated via a rapid, facile, room-temperature synthesis strategy. By functionalizing covalent organic frameworks (COFs) with magnetic nanoparticles, the resulting composite effectively pairs superior adsorptive performance with the convenience of rapid magnetic separability. The structural, morphological, and magnetic properties of the synthesized mCOF were systematically characterized. Due to various interactions (π-π interaction, hydrophobic effects, and hydrogen bonding), m-COF exhibited higher adsorption capacities for triazoles. Key experimental variables influencing extraction performance were systematically optimized to ensure high enrichment efficiency. Under the optimized conditions, the method exhibited a wide linear range with coefficients of determination (<i>R</i>²) greater than 0.99. Low limits of detection quantification were achieved. High enrichment factors reaching up to 32.16 were obtained for all analytes. This work offers a powerful, rapid, and reusable platform for trace pesticide residue analysis in complicated food and environmental matrices.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Microwave-Assisted Extraction of Bioactive Phenolics and Saponins from Curculigo orchioides through Thermal Ethanolic Pretreatment","authors":"Chi Hai Tran,&nbsp;Thanh Sang Nguyen,&nbsp;Van Man Phan","doi":"10.1007/s12161-026-03302-6","DOIUrl":"10.1007/s12161-026-03302-6","url":null,"abstract":"<div><p>The rhizomes of <i>Curculigo orchioides</i> contain diverse phytochemicals associated with antioxidant and antidiabetic activities; however, efficient extraction of these compounds remains a challenge. In this study, a thermal ethanolic pretreatment step was introduced before microwave-assisted extraction (MAE) to facilitate solvent penetration, tissue hydration, and partial disruption of the plant matrix, thereby improving the subsequent release of intracellular metabolites during microwave irradiation. The extraction parameters, including solvent-to-solid ratio, microwave power, and extraction time, were optimized using response surface methodology based on a Box–Behnken experimental design with total phenolic content (TPC) and total saponin content (TSC) as response variables. The optimized MAE conditions were identified at approximately 40 mL/g, 300 W, and 20 min, resulting in TPC and TSC values of 73.15 mg GAE g⁻¹ and 55.61 mg AE g⁻¹, respectively. Extracts obtained under these conditions showed strong radical-scavenging activity in DPPH and ABTS assays together with considerable α-glucosidase inhibitory activity. Microscopic observations indicated that pretreatment combined with microwave irradiation markedly disrupted the plant cellular matrix, which may facilitate the release of bioactive constituents. Comprehensive metabolite profiling using LC–MS/MS followed by GNPS-based molecular networking allowed the tentative annotation of 44 metabolites, including phenolic acids, phenolic glycosides, fatty acid derivatives, amino acid-related compounds, and several curculigoside-type metabolites commonly reported in <i>C. orchioides</i>. These results provide insight into the phytochemical composition of the extract and demonstrate the effectiveness of pretreatment-assisted MAE for recovering bioactive compounds from this medicinal plant.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Smart Shelf: AI-Driven Food Shelf Life and Quality Prediction","authors":"Aishwarya R.,&nbsp;Anushree B.,&nbsp;Archana E.,&nbsp;Sirisha M.,&nbsp;Prabadevi B.","doi":"10.1007/s12161-026-03295-2","DOIUrl":"10.1007/s12161-026-03295-2","url":null,"abstract":"<div><p>The accurate and timely determination of the freshness and shelf life of food helps to prevent food waste and increase food safety. Currently used methods are mainly based on manual visual evaluation and lab tests which may be subjective and time-consuming and difficult for constant monitoring. In this paper, we propose Interpretable SmartShelf—a non-invasive machine learning algorithm for food classification, freshness detection, and shelf-life estimation based on RGB images. For classification and regression tasks we use a multi-task learning method with food-type-dependent convolutional network architectures—EfficientNet-B4 for meat and seafood, EfficientNet-B0 for vegetables, MobileNetV2 for fruits, and a customized multi-scale CNN for bakery products. We use visual features like color differences, texture modifications, surface appearance, and signs of food spoilage to classify images and estimate their shelf life. To achieve the transparency of the developed algorithm, Explainable Artificial Intelligence techniques, such as Grad-CAM and LIME are used to detect the regions of input images responsible for the output of our models. The results show the effectiveness of the application of RGB-based multi-task deep learning model to automatically estimate the quality of the food and its shelf life using no dedicated imaging or sensor equipment. Moreover, the suggested approach provides the user with an interpretable interface to visualize the predictions.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-Based Food Quality Monitoring Using an Improved SqueezeNet Model with Enhanced Feature Extraction","authors":"Vikas Malik,&nbsp;Kumar Rahul,&nbsp;Ramji Parsad Gupta","doi":"10.1007/s12161-026-03292-5","DOIUrl":"10.1007/s12161-026-03292-5","url":null,"abstract":"<div><p>Internet of Things (IoT)-based food quality monitoring systems are crucial in ensuring food safety and maintaining high-quality standards throughout the supply chain. By utilizing real-time data collection from various IoT devices, these systems enable accurate evaluations of food quality across its lifecycle. This proposed work presents a new method that utilizes an improved SqueezeNet prediction model for the food quality monitoring system. Here, the Milk Grading dataset is used in this work. The proposed framework integrates improved normalization, informative feature extraction, and an enhanced SqueezeNet architecture with improved batch normalization for accurate food quality classification. Therefore, the experimental outcomes demonstrate that the efficiency of the system in food quality classification is superior when compared to conventional models. This proposed work advances the food quality monitoring field and sets the stage for future innovations in IoT applications within the food industry, highlighting the critical role of advanced data analytics and machine learning methods in ensuring food safety and quality. The improved SqueezeNet approach obtained the highest accuracy of 0.962, sensitivity of 0.930, and F-measure of 0.944.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dithizone-Modified Fe₃O₄ Nanoparticles as a Magnetic Sorbent for Extraction of Pesticides from Rose Waters by MDSPE-DLLME-GC-FID","authors":"Armin Moazzen Forough,&nbsp;MirAli Farajzadeh,&nbsp;Sanaz Barazandeh,&nbsp;Mohammad Reza Afshar Mogaddam","doi":"10.1007/s12161-026-03245-y","DOIUrl":"10.1007/s12161-026-03245-y","url":null,"abstract":"<div><p>In order to extract certain pesticides, such as haloxyfop-R-methyl, oxadiazon, diniconazole, and difenoconazole, magnetic Fe₃O₄ nanoparticles were modified with dithizone and used as a sorbent for the extraction of the target compounds in some <i>Rosa damascene </i>Mill.hydrosol samples. The synthesized sorbent was characterized by vibrating sample magnetometry, scanning electron microscopy, energy dispersive X-ray spectroscopy, Fourier transform infrared spectrometry, Brunauer–Emmett–Teller nitrogen sorption/desorption, and X-ray diffraction techniques. The investigated pesticides were adsorbed onto the sorbent surface from a solution containing dissolved Na₂SO₄. The analytes were desorbed from the sorbent surface using a water/acetonitrile mixture. After that, the eluate was mixed with 1.2-dibromo ethane (at µL-level) and injected into deionized water. After centrifuging the obtained cloudy solution, an aliquot of the sedimented phase was injected into a gas chromatograph equipped with a flame ionization detector. The approach exhibited acceptable calibration curve linearities (<i>r</i>^<i>2</i> ≥ 0.980), high enrichment factors (203–424) and satisfactory extraction recoveries (44–84%). The detection and quantification limits fell between 0.86–5.7 and 2.8–18.8 µg L⁻¹, respectively.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Field NMR T2 Relaxation Signal–Based Quantitative Determination of n-Hexane in Vegetable Oils with Feature Selection Algorithms","authors":"Guangjie Zhang,&nbsp;Qianwen Zhang,&nbsp;Yuanqing Xi,&nbsp;Keke Du,&nbsp;Jingnan Chen,&nbsp;Jun Li,&nbsp;Dan Peng","doi":"10.1007/s12161-026-03289-0","DOIUrl":"10.1007/s12161-026-03289-0","url":null,"abstract":"<div><p>In vegetable oil production, accurately monitoring residual n-hexane during the desolventizing process is essential for ensuring food safety and improving process efficiency. Although conventional analytical techniques provide high sensitivity, their complex operation procedures and lengthy analysis times limit their applicability for rapid, on-site detection in industrial environments. Using low-field nuclear magnetic resonance (LF-NMR), this study compared the T₂ relaxation characteristics of seven common vegetable oils and n-hexane. Results showed that the relaxation signals of different vegetable oils were highly similar but distinctly different from those of n-hexane. Accordingly, soybean oil was selected as a representative system to investigate the influence of varying n-hexane levels on T₂ relaxation behavior, and a quantitative prediction model was constructed using multiple preprocessing and feature-selection strategies. As n-hexane concentration increased, the T₂ signal attenuation rate decreased and the transverse relaxation time significantly prolonged, exhibiting a strong linear correlation with solvent concentration. The model combining Savitzky–Golay smoothing with genetic-algorithm-based partial least squares regression achieved the best performance, with a root mean square error of prediction of 0.1699 and a relative prediction deviation of 59.49. It demonstrated excellent predictive accuracy and robustness while substantially reducing data dimensionality. Overall, this study provides a feasible and efficient technical approach for real-time monitoring and online control of industrial oil desolventizing.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bismuth-Doped Carbon Dot Nanozyme for Three-Mode Glutathione Detection in Food","authors":"Ziting Zhang,&nbsp;Zhefeng Fan","doi":"10.1007/s12161-026-03300-8","DOIUrl":"10.1007/s12161-026-03300-8","url":null,"abstract":"<div><p>Accurate glutathione (GSH) detection in food is of great importance for protecting human health. Bismuth-doped carbon dots (Bi-CDs) with blue fluorescence and photoresponsive oxidase activity were synthesized using citric acid, urea, and bismuth nitrate as raw materials. On the basis of the excellent enzyme activity and optical characteristics of Bi-CDs, a colorimetric–fluorescent–photothermal triple signal sensing mode was developed for GSH detection in food. Under the irradiation of a 365 nm UV lamp, Bi-CDs could catalyze 3,3′,5,5′-tetramethylbenzidine (TMB) to form blue oxidized TMB (oxTMB). A significant UV absorption peak appeared at 652 nm. Owing to the mechanism of photoinduced electron transfer (PET), the generated oxTMB could quench the fluorescence of Bi-CDs. In the presence of GSH, oxTMB was reduced to TMB again, which restored the fluorescence of Bi-CDs, and the UV absorption peak at 652 nm gradually decreased. oxTMB showed a photothermal effect under near-infrared laser irradiation. Its photothermal temperature decreased with the increase in GSH concentration, thus realizing photothermal GSH detection. Experimental results show that the detection ranges of colorimetric, fluorescence, and photothermal modes were 0–90 μM, 0–95 μM, and 5–95 μM, respectively, with limits of detection of 0.64 μM, 0.54 μM, and 2.79 μM, respectively (Scheme 1). The integrated photothermal detection method effectively overcomes the limitations of traditional optical methods in analyzing colored or autofluorescence samples, and the cross-validation ability of the multimode sensing platform further ensures the reliability and stability of detection results. This collaborative strategy provides a comprehensive and promising solution for accurate GSH detection in a variety of complex food matrices.\u0000</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectroradiometer-Assisted Quantitative Evaluation of Metanil Yellow Adulteration in Arhar Dal and Flour Using Partial Least Squares Regression","authors":"Asritha Dharni,&nbsp;Sandhya Singh,&nbsp;Dhritiman Saha,&nbsp;Derminder Singh,&nbsp;Manpreet Kaur Saini","doi":"10.1007/s12161-026-03297-0","DOIUrl":"10.1007/s12161-026-03297-0","url":null,"abstract":"<div><p>Adulteration of food products with non-permitted dyes such as metanil yellow poses a significant threat to consumer health and food authenticity, particularly in arhar dal and flour. This study was aimed at developing a rapid, non-destructive, spectroradiometer-based method for quantifying metanil yellow adulteration in these matrices. Samples containing 0–2% (w/w) metanil yellow were prepared, with 20 independently prepared physical samples for each matrix. Reflectance spectra were acquired over 350–2500 nm. Five replicate spectra from each of 30 measurement positions were averaged, followed by averaging the 30 position-level spectra to obtain one representative spectrum per sample. Representative spectra were divided into calibration and prediction subsets using the Kennard–Stone algorithm (80:20). Spectral preprocessing combinations and latent variables were optimised using five-fold venetian blinds cross-validation on the calibration set, with the prediction set held out. Based on the lowest RMSECV, SNV with four latent variables was selected for arhar flour, whereas smoothing + MSC (median) + OSC with one latent variable was selected for arhar dal. The flour model achieved <i>R</i>²_cv = 0.842, RMSECV = 0.126%, and <i>R</i>²_p = 0.927, while the dal model achieved <i>R</i>²_cv = 0.899, RMSECV = 0.146%, and <i>R</i>²_p = 0.951. Independent hold-out validation yielded RMSEP values of 0.123% for flour and 0.150% for dal, with biases of 0.0002% and 0.005%, respectively. Method-specific LOD and LOQ estimates based on the integrated 500–600 nm reflectance response were 0.08375% and 0.25377% for flour and 0.25906% and 0.78499% for dal, respectively. Low-concentration analysis (0–0.20%) showed increased prediction uncertainty. Overall, the results demonstrate the preliminary feasibility of spectroradiometry combined with PLSR for metanil yellow estimation under controlled conditions. Further validation using larger, multisource datasets and diverse adulteration conditions is required.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Green Solvent Extraction of Macaúba (Acrocomia aculeata) Pulp Oil: Yield, Bioactive Compounds, and Fatty Acid Composition","authors":"Luisa Neves Duarte Martins,&nbsp;Jairo Neves de Jesus Santos,&nbsp;Evandro Galvão Tavares Menezes","doi":"10.1007/s12161-026-03293-4","DOIUrl":"10.1007/s12161-026-03293-4","url":null,"abstract":"<div><p>The macaúba palm (<i>Acrocomia aculeata</i>), widely distributed throughout Brazil, has a pulp rich in oil and bioactive compounds, demonstrating its potential for food and industrial applications. The lipid fraction can be extracted by different methods, with hexane being the most used solvent. Considering the environmental and safety concerns associated with hexane, this study evaluated green solvents (ethanol, 95% ethanol, 90% ethanol, and isopropanol) for the solid–liquid extraction of oil from macaúba pulp, assessing the effects of temperature (35, 45, and 55 °C) and solvent-to-solid ratio (5:1 and 10:1). The response variables analyzed were oil yield, retention index, total phenolic content, α-tocopherol content, antioxidant activity, and fatty acid composition. Hexane showed the highest oil yields and the lowest retention index. However, polar solvents, particularly hydrated ethanolic systems, increased the recovery of phenolic compounds and antioxidant activity. Absolute ethanol and isopropanol achieved competitive yields under more intensified conditions (55 °C and a solvent-to-solid ratio of 10:1). The fatty acid composition remained practically stable under all extraction conditions, with oleic acid predominating. Principal component analysis confirmed that non-polar solvents favor oil yield, while polar solvents promote the recovery of bioactive compounds. These results demonstrate that green solvents represent a promising alternative to hexane when both extraction efficiency and functional quality are considered.</p></div>","PeriodicalId":561,"journal":{"name":"Food Analytical Methods","volume":"19 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12161-026-03293-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}