Journal of Food Composition and Analysis最新文献

{"title":"Design and application of magnetic deep eutectic solvent-based spontaneous liquid-liquid microextraction for the determination of triazole fungicides in water, juice, and wine","authors":"","doi":"10.1016/j.jfca.2024.106796","DOIUrl":"10.1016/j.jfca.2024.106796","url":null,"abstract":"<div><div>In this study, a simple, rapid, and eco-friendly technique, magnetic deep eutectic solvent-based spontaneous liquid-liquid microextraction (MDES-SLLME), was developed to detect triazole fungicides in water, juice, and wine by UHPLC. The unreported SLLME involves injecting the extractant in the whole liquid-phase microextraction, shortening the extraction and separation time without device assistance. MDES produced by heating bio-derived solvent anisole and metal chloride ferric chloride was used as the extractant to reduce the consumption of toxic solvents. Based on the properties of anisole, MDES spontaneously formed small droplets in samples to complete extraction. Based on the properties of ferric chloride, MDES achieved magnetic separation under an external magnetic field. The method was applied to three matrices (water, juice, and wine). The concentration range was 0.007–0.7 mg L⁻¹, and the matrix effect range was 87.4 %–94.9 %. The limit of detection was 0.002 mg L⁻¹. The spiked recoveries ranged from 85.6 % to 94.7 %. The relative standard deviation ranged from 1.7 %–6.3 %. The SLLME requires attention as a potential complement to single-drop microextraction, hollow fiber liquid-phase microextraction, dispersive liquid-liquid microextraction, homogeneous liquid-liquid microextraction, and emulsive liquid-liquid microextraction.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of moisture and size of winter melon seeds based on hyperspectral imaging and convex polygon size measurement","authors":"","doi":"10.1016/j.jfca.2024.106789","DOIUrl":"10.1016/j.jfca.2024.106789","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The level of moisture content and size of winter melon seeds affect their storage, germination and growth processes. Moderate moisture and size are essential for seed germination and growth. Therefore, detecting the moisture and size of winter melon seeds is beneficial to improve their germination rate. Traditional seed moisture testing methods are complex to operate and require destructive sample preparation or chemical treatment. Common dimensional measurement methods are also time-consuming and laborious. Hyperspectral imaging technology can acquire information about the surface and internal structure of the target separately, it can be used to quickly and non-destructively detect the moisture and size of winter melon seeds. In this study, partial least squares regression (PLSR) and partial least squares support vector machine (LSSVM) models were established to predict the moisture content of winter melon seeds by using reflection and transmittance spectral data. The models were optimized using five variable selection methods. The optimal performance of the LSSVM model based on the CARS algorithm was achieved in both single reflection and transmission spectra. The &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; and RMSEP of the model based on the reflection spectra were 0.9667 % and 0.0215 %, respectively. The &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; and RMSEP of the model based on the transmission spectra were 0.9644 % and 0.0223 %, respectively. In low-level data fusion, the LSSVM model based on the CARS algorithm also achieved optimal performance, but with only a little improvement compared to a single model (reflection spectra or transmission spectra), with &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; and RMSEP of 0.9679 % and 0.0212 %, respectively. In the mid-level data fusion, the LSSVM model also based on the CARS algorithm achieved the optimal performance, and the performance of the model was further improved. The &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; and RMSEP of the model were 0.9738 % and 0.0191 %, respectively. Finally, the image processing algorithm and the convex polygon size measurement method was proposed to measure the size of winter melon seeds. The absolute error between the calibrated winter melon seed length and width and the true length and width was less than 0.22 mm, and the relative error was less than 2 %. The results show that hyperspectral imaging technology can accurately detect the water content of winter melon seeds. Data fusion method and LSSVM model based on CARS algorithm can detect the water content of winter melon seeds more accurately. The image processing algorithm combined with the convex polygon size measuremen","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time monitoring of cattle (Bos taurus) tissue using a novel point-of-care (POC) polymerase spiral reaction (PSR) colorimetric assay","authors":"","doi":"10.1016/j.jfca.2024.106773","DOIUrl":"10.1016/j.jfca.2024.106773","url":null,"abstract":"<div><div>Adulteration of cattle meat with other species raises critical public health concerns, as existing detection methods lack sensitivity, specificity and practicality. We address this gap by introducing a novel Point-of-Care (POC) polymerase spiral reaction (PSR) assay. This rapid, on-site tool enables highly specific and sensitive identification of cattle tissue, even in resource-limited settings. This innovation paves the way for improved food safety and consumer protection. The cattle-specific PSR assay employs <em>COX3</em> gene-targeting primers and a chromogenic indicator triad (HNB, Malachite Green and Phenol Red) for rapid visual detection. Cross-amplification absence with other meats (buffalo, sheep, goat and pig) and concordance with SYBR Green I and agarose gel electrophoresis confirm specificity. PSR at 62 °C for 60 minutes exhibits a 100 fg detection limit and identifies 0.1 % meat admixing. Efficacy is demonstrated across a diverse range of cattle meat samples, blind samples and processed cattle muscle samples. Compatibility with both conventional (snap chill) and commercial DNA extraction kits enables on-site analysis within 90 minutes, facilitating rapid field sample analysis.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilayer spatial-spectral segmentation network for detecting AFB1","authors":"","doi":"10.1016/j.jfca.2024.106790","DOIUrl":"10.1016/j.jfca.2024.106790","url":null,"abstract":"<div><div>Aflatoxin, a toxin produced by <em>Aspergillus flavus</em>, is commonly found in peanuts and poses a significant threat to human health. In this paper, we propose an adjustable Mul-U-Net model, capable of learning both spectral and spatial information in parallel, facilitating rapid detection of aflatoxins by adjusting the number of central spatial U units. First, AFB₁ was semantically segmented by inputting hyperspectral image cubes into six semantic segmentation models: Mul-U-Net, U-Net, FCN, SegNet, CNN, and RefineNet. Experimental results demonstrate that Mul-U-Net outperformed the other models, achieving an overall accuracy of 95.64 % and an RMSE of 0.2079. We further examined the influence of different spatial U units on model performance and found that the optimal spatial U count matched the number of labels. In the aflatoxin dataset, the Mul-2 U-Net model achieved the highest accuracy of 96.12 %. Further validation on the Indian Pines and Pavia University datasets showed that Mul-16 U-Net and Mul-9 U-Net achieved the highest accuracies of 98.12 % and 98.01 %, respectively. This study presents a robust, high-performance, and tunable Mul-U-Net semantic segmentation network, offering a valuable new approach for the identification of aflatoxins in peanuts.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical sensor based on Fe3O4@Au/MOF-P2W17V composite modified glassy carbon electrode for food nitrite detection","authors":"","doi":"10.1016/j.jfca.2024.106792","DOIUrl":"10.1016/j.jfca.2024.106792","url":null,"abstract":"<div><div>Excessive consumption of nitrites can be harmful to human health. Therefore, the design of novel electrocatalysts for efficient electrochemical quantification of nitrite is important. In this paper, a magnetic metal-organic framework (Fe₃O₄@Au/MOF) was synthesized by a self-assembly method using waste orange peel as the reducing agent, and vanadium-substituted tungsten phosphate (K₇P₂W₁₇VO₆₂-18H₂O, P₂W₁₇V) and Fe₃O₄@Au/MOF were loaded on glassy carbon electrodes for the first time by layer-by-layer self-assembly and electrodeposition. Then, a nitrite sensor based on Fe₃O₄@Au/MOF-P₂W₁₇V nanocomposites was prepared. The composites' characterization reveals that the combination of P₂W₁₇V with Fe₃O₄@Au/MOF not only inhibits nanoparticle agglomeration but also offers high catalytic activity and electrical conductivity when compared to pure Fe₃O₄@Au/MOF and P₂W₁₇V. Under the optimal conditions, the Fe₃O₄@Au/MOF-P₂W₁₇V sensor's linear range for detecting nitrite was 0.01–100 mM, with a sensitivity of 11.682 μA·μM⁻¹·cm⁻² and a detection limit as low as 0.532 μM. In 100 cycle stability trials and 30 d reproducibility experiments, the current peak retained more than 95 % of its initial value. Furthermore, the sensor demonstrated good selectivity and has been successfully applied to detect nitrite in ham sausage, squash, milk and brined quail eggs, and the results were basically consistent with those of the naphthylenediamine hydrochloride method. The effective fabrication of Fe₃O₄@Au/MOF-P₂W₁₇V opens up a new avenue for determining low amounts of nitrite in analytical applications that is viable for practical use.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method for rice identification: Coupling Raman spectroscopy with Fourier spectrum and analyzing with deep learning","authors":"","doi":"10.1016/j.jfca.2024.106793","DOIUrl":"10.1016/j.jfca.2024.106793","url":null,"abstract":"<div><div>The development of detection methods for food adulteration is of great significance for promoting innovation and development in food safety supervision technology. In this study, we proposed a novel model: Raman-Fourier-BiLSTM-CNN (RFBC), which combines Raman spectroscopy with deep learning to achieve precise identification of seven brands of japonica rice in the northeastern region of China. This model focuses on revealing the worth of the Fourier spectrum of Raman spectra. The raw Raman spectra and their Fourier spectra are cleverly coupled through a Bi-directional Long Short-Term Memory (BiLSTM) connection structure, and the resulting composite features are deeply analyzed by a Convolutional Neural Network (CNN). Compared to traditional algorithms, RFBC exhibits superior feature extraction capabilities. To further evaluate the recognition performance of RFBC, this study compared it with the RFBC model containing only the original Raman spectrum branch, as well as with machine learning models based on Principal Component Analysis (PCA), including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). The results show that, compared with the other five models, RFBC can accurately identify different brands of japonica rice, with significant advantages in accuracy, precision, recall, and other aspects. RFBC achieves a classification accuracy of 97.1 %, macro precision of 97.2 %, macro recall of 97.1 %, and macro F1-score of 97.2 %. The system proposed in this study can more accurately identify brands of japonica rice, providing strong technical support for combating counterfeit japonica rice products.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compliance with voluntary nutritional labelling on alcoholic beverages in Spain","authors":"","doi":"10.1016/j.jfca.2024.106791","DOIUrl":"10.1016/j.jfca.2024.106791","url":null,"abstract":"<div><div>Access to nutritional information is a fundamental consumer´s right, as it facilitates informed decision-making regarding dietary choices. However, excluding a recent directive concerning wine products, European Union regulations grant exemption to beverages with an alcohol content exceeding 1.2 % from the requirement of disclosing nutritional values. The purpose of this study was to evaluate the industry´s compliance with voluntary commitments for nutritional labelling of alcoholic beverages in Spain. A cross-sectional study was conducted in the municipality of Madrid (Spain) during March and April 2023. Data on 627 alcoholic beverages were collected. We considered that a product label displayed complete nutritional data when it showed the energy value per 100 mL as well as the corresponding macronutrients (fats, carbohydrates, and proteins). Only 22.2 % of these products provided any nutritional information, with beers leading at 56.5 %, followed by spirits (26.5 %), vermouths and aperitifs (16.7 %), and wines and sparkling wines significantly fewer at 2.9 %. Only 2.4 % of the sampled beverages provided comprehensive nutritional information including energy and macronutrients. These findings highlight a failure to meet voluntary labelling commitments in Spain, with significant variations observed across sectors. The government should ensure consumers´ access to readily available and accurate information regarding the nutritional composition of alcoholic beverages.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Removal residual, commercial processing factor and risk assessment of four fungicides in orange fruit and various processing by-products","authors":"","doi":"10.1016/j.jfca.2024.106786","DOIUrl":"10.1016/j.jfca.2024.106786","url":null,"abstract":"<div><div>The residual behaviors and processing factors of four fungicides (prochloraz, kasugamycin, oxine-copper and fenaminstrobin) in orange fruit and various processing by-products were evaluated. The residues of kasugamycin, oxine-copper and fenaminstrobin were detected using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) combined with QuEChERS pretreatment method and that of prochloraz was monitored using gas chromatography with an electron capture detector (GC-ECD). The results showed washing processing reduced residues by 8.6 %-33.9 %. Juicing was an effective process to reduce pesticide residues by 65.6 %-98.7 % with processing factor (<em>PF</em>) of 0.021–0.339. However, high levels of residual prochloraz, oxine-copper and fenaminstrobin (from 5.777 to 51.533 mg/kg) were detected in orange essential oils, with <em>PF</em> of 8.720–14.558. Prochloraz was more easily enriched in wet pomace compared with the other three fungicides. When spraying was conducted in accordance with good agricultural practices and using the recommended highest pesticide application dosage and frequency, the dietary intake exposure risks of the four residual fungicides in orange and its processed juices in China were less than 100 % for different age groups and caused no unacceptable risks to human health.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct quadruplex real-time VPCR for simultaneous rapid detection of Pinelliae Rhizoma, Rhizoma Pinelliae Pedatisectae and Rhizoma Typhonii Flagelliformis","authors":"","doi":"10.1016/j.jfca.2024.106761","DOIUrl":"10.1016/j.jfca.2024.106761","url":null,"abstract":"<div><div>Known for its health benefits, <em>Pinelliae Rhizoma</em> is frequently utilized in many botanical dietary supplements and herbal products. However, the substantial demand has led to the frequent adulteration with morphologically similar species, such as <em>Rhizoma Pinelliae Pedatisectae</em> and <em>Rhizoma Typhonii Flagelliformis</em>. Addressing this challenge, a direct quadruplex real-time VPCR approach has been developed in this study. Firstly, a direct lysis protocol has been implemented to facilitate the rapid release of genomic DNA within only 2 min. Secondly, specific primers and probes were designed for <em>Pinellia ternata</em> (Thunb.) Breit., <em>Pinellia pedatisecta</em> Schott and <em>Typhonium flagelliforme</em> (Lodd.) Blume, respectively. A conserved sequence on the ITS was also included as an internal control to mitigate the potential false negatives arising from DNA extraction or DNA amplification errors. Furthermore, the amplification process has been refined through a VPCR heating model, curtailing the detection to a scant 30 min. After a series of optimizations, the developed direct quadruplex VPCR allows for the simultaneous detection of these three plant materials, with each material having a detection sensitivity as low as 1 pg/µL. At last, the method's applicability was validated by using several commercial samples previously identified by Sanger sequencing.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of roasting time on the color, protein, water distribution and key volatile compounds of pork","authors":"","doi":"10.1016/j.jfca.2024.106787","DOIUrl":"10.1016/j.jfca.2024.106787","url":null,"abstract":"<div><div>The study aimed to determine the changes in physical and chemical indicators in pork during the roasting process and establish predictive models for industrial intelligent production in the future, the effects of roasting times (0–15 min) on the dynamic changes in the surface color, water, protein and the volatile compounds in pork were investigated. The results showed that during the roasting process, the <em>L</em>* value increased firstly (0–3 min) and then decreased (3–15 min), the <em>a</em>* value decreased firstly (0–12 min) and then increased (12–15 min), while the <em>b</em>* value increased (0–15 min). A total of 29 volatile compounds were identified by gas chromatography–mass spectrometry, 11 volatile differentiators were identified by partial least squares-discriminant analysis, and 14 key volatile compounds were selected by odor activity values analysis. The total water content significantly decreased and the water in meat migrated from high degrees of freedom to low degrees of freedom. Roasting for 6 min was a key turning point in the changes of the <em>α</em>-helix, β-sheet and random coil. In addition, SDS-PAGE analysis, and the changes in the contents of carbonyl, total and free sulfhydryl verified proteins oxidation in the pork during the roasting process.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}