FOOD HYDROCOLLOIDS
·
2019
·
IF 5.839
Cen Zhang,Yang Li,Peng Wang,Aiping Zhang,Fengqin Feng,Hui Zhang
In this work, the uniform core-shell nanofibers were fabricated by electrospinning of bilayer emulsions, which were formed by the addition of gum arabic with opposite charges into the gelatin-stabilized emulsions. The increased weight ratios of gum arabic enhanced the emulsion stability by progressively increasing the viscosity of bilayer emulsions, which contributed to the formation of bead-free and smooth electrospun fibers with the increasing diameters. Transmission electron microscopy and confocal laser scanning microscopy confirmed that corn oil was evenly distributed in the bilayer emulsion-based fibers. These fibers with various amounts of gum arabic showed a high thermal decomposition stability upon heating to 250 ° C, as hydrogen bonds played an important role in the binding of gum arabic to gelatin. The fiber mats showed high oil encapsulation efficiency and good storage stability during 5 days. These results may provide a new strategy to develop bilayer emulsion-based electrospun fibers for bioactive encapsulation in food applications.
研究领域:
食品科学
·
材料科学
✓ 已验证
产品关联
DOI
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
·
2024
·
IF 6
Kexin Qu,Jing Ma,Hua Zhang,Xuanjun Li
The combination of oleogels has recently begun to receive scientific attention because a single oleogels may not adequately compensate for the multiple roles of solid fat in complex food systems. In this study, corn oleogels were prepared from candelilla wax (CLW), beeswax (BW), carnauba wax (CW), and ethyl cellulose (EC) with different mass ratios, and their physicochemical properties were characterized in terms of heat, rheology and microstructure. The strength of oleogel was CLEOs > BEOs > CWEOs. With the increase of the amount of wax in the oleogelators, the color L* value, the ability to bind oil, the melting temperature, the hardness and the viscosity and strength of the oleogels were increased. Microscopic analysis showed that the gel network was formed based on the crystallization and self-organization of gel molecules, and the two oleogelators showed independent crystallization behavior in the oleogel. Furthermore, the FTIR spectra showed that noncovalent bonds of van der Waals forces played a dominant role in the composite oleogel network formed through physical entanglement as compared to hydrogen bonds. XRD results showed higher lateral packing of molecular layers in oleogels prepared from combined EC and wax compared to oleogels prepared from a single oleogelator. This study provides a reference for the development of a new composite oleogel system.
研究领域:
食品科学
·
材料科学
✓ 已验证
DOI
高影响因子
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
·
2018
·
IF 3.412
Cen Zhang,Hui Zhang
Core–shell nanofibers were fabricated by electrospinning of gel-like corn oil emulsions stabilized by gelatin. The oil-in-water (O/W) emulsions satisfied the Herschel–Bulkley rheological model and showed shear-thinning and predominantly elastic gel behaviors. The increasing oil fractions (φ) ranging from 0 to 0.6 remarkably increased the apparent viscosity and then led to an increase in the average diameter and encapsulation efficiency of electrospun fibers. Core–shell structured fibers by emulsion electrospinning were observed in transmission electron microscopy (TEM) images. The encapsulated oil was found to randomly distribute as core, especially inside the beads. The binding of corn oil to gelatin was mainly driven by noncovalent forces. These core–shell fibers at various φ values (φ = 0.2, 0.4, 0.6, and 0.8) showed a high thermal decomposition stability upon heating to 250 °C, and the denaturation temperatures were 85.32 °C, 77.97 °C, 82.99 °C, and 87.25 °C, respectively. The corn oil encapsulated in emulsion-based fiber mats had good storage stability during 5 days. These results contributed to a good understanding of emulsion electrospinning of food materials for potential applications in bioactive encapsulation, enzyme immobilization, and active food packaging.
研究领域:
食品科学
·
材料科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
·
2023
·
IF 8.8
Ge Bai,Chuan-Guo Ma,Yu-Yuan Hu,Shu-Jing Guo,Tong Wang
Phytosterols have health benefits; however, they are partially removed during the bleaching of corn oil. We evaluated the chemical conversion of free phytosterols (FPs) during bleaching. FP degradation accelerated with increased time and temperature, following a first-order kinetic model. In the n -heptane system, air and activated clay promoted the chemical conversion of the FPs. Sterenes formation was analysed under different conditions using a zero-order kinetic model. The apparent activation energies revealed sterene formation decreasing in the following order: campesta-3,5-diene ≈ stigmasta-3,5,22-triene > stigmasta-3,5-diene. Isomers of the above were not detected, indicating that these sterenes were the only primary products of FPs. The desorption test indicated that the FP loss from corn oil was not only due to FPs being adsorbed the activated clay, but also FPs adsorbed at acidic activated sites being degraded. This study presents a vital scientific foundation for retaining FPs to develop healthier and more nutritious oils.
研究领域:
食品科学
·
化学工程
✓ 已验证
产品关联
DOI
PubMed
高影响因子
Foods
·
2023
·Vol. 12
·期 12
·2402
·
IF 5.2
Yingchao Xue,Hui Jiang
This study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through Raman spectroscopy using a combined long short-term memory network (LSTM) and convolutional neural network (CNN) architecture. The QE Pro Raman+ spectrometer was employed to collect Raman spectra of corn oil samples with varying concentrations of chlorpyrifos residues. A deep-learning model based on LSTM combined with a CNN structure was designed to realize feature self-learning and model training of Raman spectra of corn oil samples. In the study, it was discovered that the LSTM-CNN model has superior generalization performance compared to both the LSTM and CNN models. The root-mean-square error of prediction (RMSEP) of the LSTM-CNN model is 12.3 mg·kg−1, the coefficient of determination (RP2) is 0.90, and the calculation of the relative prediction deviation (RPD) results in a value of 3.2. The study demonstrates that the deep-learning network based on an LSTM-CNN structure can achieve feature self-learning and multivariate model calibration for Raman spectra without preprocessing. The results of this study present an innovative approach for chemometric analysis using Raman spectroscopy.Keywords:Raman spectroscopy;corn oil;deep learning;chlorpyrifos;quantitative analysis
研究领域:
食品安全
·
光谱学
·
人工智能
✓ 已验证
产品关联
DOI
PubMed
高影响因子
INTERNATIONAL JOURNAL OF FOOD PROPERTIES
·
2021
·
IF 2.727
Lipeng Han,Jiahui Li,Shujie Wang,Weiwei Cheng,Lukai Ma,Guoqin Liu,Dongxue Han,Li Niu
Glycidyl ester (GE) has attracted worldwide attention due to its potential harm to human health. The GE in edible oil forms during the deodorization process of the oil refining, where antioxidants are used to inhibit its formation. To replace unsafe conventional synthetic antioxidants with natural antioxidants, sesame oil (SO) was used to inhibit the formation of GE in corn oil (CO), palm oil (PO), and rice bran oil (RO) during the deodorization process. The results showed that the content of GE in SO, CO, PO and RO followed the decreasing order of RO (1395.88 μg/kg) > PO (376.84 μg/kg) > CO (303.24 μg/kg) > SO (133.19 μg/kg). The concentration of diacylglycerol (DAG) in SO, CO, PO and RO followed the decreasing order of RO (11.64%) > PO (7.56%) > CO (4.11%) > SO (2.59%). The content of GE in oil could be affected by at least three factors: (i) unsaturated fatty acids could promote the formation of GE in oil; (ii) antioxidants in oil could inhibit the formation of GE by scavenging free radicals; and (iii) DAG, as a precursor, could lead to the formation of GE. This result suggested that SO could inhibit the formation of GE in CO, PO and RO. This indicated that when SO was added at 50%, its inhibitory effect was equivalent to that of tert-butylhydroquinone added at 0.02%.
研究领域:
食品科学
·
健康与医学
✓ 已验证
产品关联
DOI
高影响因子
JOURNAL OF FOOD SCIENCE
·
2019
·Vol. 84
·期 6
·1447-1455
·
IF 2.081
Jingdong Xiao,Ming Zhang,Wenhang Wang,Anguo Teng,Anjun Liu,Ran Ye,Yaowei Liu,Kun Wang,Junsheng Ding,Xiaomeng Wu
Oleogel with tailored viscoelasticity is a great interest for food structuring, while its potential benefits for edible film performance are not clear. In this study, β-sitosterol (0, 5, 10, 15, and 20 wt%)-corn oil oleogel was developed and used in the formation of gelatin-based films. Importantly, adding oleogel significantly decreased water vapor permeability of the gelatin films, however, it had little negative influence on film strength. In addition, the results of this study demonstrated that increasing the sitosterol in oleogels led to an increasing number of ordered crystals formed in the oleogel, which contributed to compact and smooth surface of the film. Moreover, the incorporation of oleogel also caused some changes in molecule conformation and film barrier property. Therefore, the superior mechanical performance and moisture resistance properties of the film were obtained when 15% β-sitosterol was used to prepare oleogel. Practical Application Corn oil oleogels β-sitosterol was incorporated with gelatin to prepare the gelatin film aiming to improve the water resistance of the films for its variety of practical production. The enhanced vapor permeability and accepted strength of the emulsion film indicated the potential application of it with a variety of edible packaging forms, such as films, pouch and sachet in medium and high humid condition.
研究领域:
食品科学
·
材料科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
LWT-FOOD SCIENCE AND TECHNOLOGY
·
2021
·
IF 4.952
Huan Tan,Liyang Han,Chen Yang
The type of oil used and the introduction of bioactive compounds could strongly influence the microstructure and physicochemical properties of Pickering emulsions, which have yet to be fully understood. Herein, oil-in-water (O/W) Pickering emulsions were stabilized by autofluorescent gelatin nanoparticles with either corn oil or medium chain triglyceride (MCT) oil as references for commonly studied edible oils . The microstructure of the emulsions with changes in nanoparticle concentration, oil fraction (Φ), and oil type was investigated with confocal laser scanning microscopy (CLSM). An increase in nanoparticle concentration resulted in an increased thickness of the adsorption layers and number of droplets. Increasing Φ helped to form compact gel-like emulsions and improve the oxidation stability of the emulsions. The high oil-water interfacial tension and surface-active long-chain fatty acids of corn oil caused sparse packing droplet interfaces. Hence, corn oil emulsions were generally less stable than MCT oil emulsions, which was further demonstrated through studies of water holding capacity (WHC), long-term storage stability, and rheology. Furthermore, the incorporation of β-carotene had negative effects on the formation and stability of the emulsions, especially for corn oil emulsions. The findings in this work may have important implications for exploiting Pickering emulsion formulations in real food products.
研究领域:
食品科学
·
生物化学
✓ 已验证
产品关联
DOI
高影响因子
Food Biophysics
·
2018
·Vol. 13
·期 4
·362-373
·
IF 2.051
Yang Shu,Zhu Minpeng,Wang Na,Cui Xiaotong,Xu Qing,Saleh Ahmed S. M.,Duan Yumin,Xiao Zhigang
Oleogels were prepared from extra virgin olive oil, corn oil, sunflower oil, and flaxseed oil with a mixture of β-sitosterol and stearic acid (Sit1:SA4, w /w) at concentrations of 15 and 20 g/100 g oil. The prepared oleogels were characterized by different methods to study the influence of oil type on the oleogel properties. The oil type influenced the colour and appearance of the oleogel. The flaxseed oil based oleogel showed lower oil loss and higher firmness than those of other oils based oleogels. The increase of gelator mixture from 15 to 20 g/100 g oil reduced the oil loss and improved the firmness of oleogel samples. The microscopy and small-angle x-ray scattering analyses showed different microstructures and crystallographic reflections for oleogels prepared from different oil types. Also, the oil type and concentration of gelator mixture influenced the melting and crystallization enthalpies of oleogel. Furthermore, different oils based oleogels showed varying values of viscosity, storage modulus (G’), and loss modulus (G”). Therefore, it can be concluded that the oil type and concentration of gelator influence the functional properties of oleogel and the flaxseed oil resulted in oleogel with good properties compared with other oils used in the study.
研究领域:
食品科学
·
化学工程
✓ 已验证
产品关联
DOI
高影响因子
FOOD HYDROCOLLOIDS
·
2022
·
IF 11.504
Song Cui,Zhenyu Yang,David Julian McClements,Xingfeng Xu,Xin Qiao,Liyang Zhou,Qingjie Sun,Bo Jiao,Qiang Wang,Lei Dai
Pickering emulsions are increasingly being explored for their potential applications within the food industry. However, the stability of these emulsions is often limited when stabilized by single colloidal particles. Herein, we examined the possibility of improving the performance of Pickering emulsions by using a combination of colloidal particles (zein nanoparticles , Z) and small molecular emulsifiers (quillaja saponin , Q). The stability mechanism of Pickering emulsions was explored from microscopic to macroscopic scales using QCM-D, microrheology, and Cyro-SEM, et. Corn oil-in-water Pickering emulsions were prepared by adding the Z and Q in different sequences: (i) mix Z and Q and then homogenize (“ZQ-P”); (ii) add Z, homogenize, and then add Q (“Z-QP”); (iii) add Q, homogenize, and then add Z (“Q-ZP”). The sequence significantly influenced the interfacial structure and properties of Pickering emulsions. Macroscopic rheology and microrheology showed that the Pickering emulsions formed an elastic-like structure (G′>G″) and their viscoelastic properties increased over time. QCM-D analysis showed that the properties of the interfacial layers depended on the emulsifier addition sequence used to form the Pickering emulsions. The interfacial membranes formed using the Q-ZP sequence were the thickest (5.8 nm). Cyro-SEM imaging showed that nanoparticle bridges formed between neighboring droplet surfaces, which led to the formation of a 3D network that provided mechanical strength. Overall, this study showed that the Q-ZP addition sequence was the most effective at forming stable Pickering emulsions. It provides new insights that may facilitate the formation of plant-based Pickering emulsions with improved functional performance in food applications.
研究领域:
食品科学
·
胶体化学
✓ 已验证
产品关联
DOI
高影响因子
FOOD CHEMISTRY
·
2023
·
IF 8.8
Andi Suo,Gongjian Fan,Caie Wu,Tingting Li,Kaiping Cong
The valorization of waste apricot flesh (WAF) can solve environmental and economic problems, and also meets the demand for natural pigments. Therefore, the aim of this paper was to recover carotenoids from WAF, to determine the type and content of carotenoids in the extracts and to explore the potential of the extracts for food industry applications. The extraction conditions were optimized. The total carotenoids content (TCC) was 42.75 mg/100 g dried weight under the optimized conditions: Time: 60 min, Temperature: 41.53℃, Power: 200 W, Liquid to solid (LS) ratio: 0.10 g/mL. The highest content of carotenoids in the corn oil extracts (COE) was phytoene. Color of COE under high temperature was investigated. Carotenoids in the COE were degraded at high temperatures. The a* of fries fried by COE was 7.31 times higher than that of corn oil. This study provides guidance for the green recovery of carotenoids and valorization of WAF.
研究领域:
食品科学
·
植物化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
INFRARED PHYSICS & TECHNOLOGY
·
2023
·
IF 3.3
Yingchao Xue,Chengyun Zhu,Hui Jiang
This study introduces a fast analytical approach for detecting chlorpyrifos residues in corn oil, based on a one-dimensional convolutional neural network (1D-CNN) structure for near-infrared (NIR) spectral deep learning models. Data augmentation algorithms were introduced to effectively expand the database of corn oil samples obtained in the experiment, ensuring sufficient training of the deep learning network. A well-designed 1D-CNN structure was implemented to create a multivariate calibration model. This model can be used to quantify chlorpyrifos residues in corn oil. The study found that using data augmentation with a 1D-CNN model led to improved performance compared to using the original spectra calibration in the model. Particularly, the 1D-CNN model’s best overall performance was achieved using data augmented with varying degrees of signal-to-noise ratio (SNR) algorithms, with a prediction coefficient of determination ( R P 2 ) of 0.9492 and a relative percent deviation (RPD) of 4.4569. The study demonstrates the feasibility of using NIR spectral deep learning models for quantitative analysis of pesticide residues in edible oils. Furthermore, data augmentation methods can effectively improve the accuracy of deep learning algorithms in NIR spectral chemometrics model calibration.
研究领域:
食品科学
·
分析化学
·
人工智能
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）