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采用酶辅助三相分离法（EATPP）同时提取亚麻籽中的油脂、蛋白质和胶，以亚麻籽油提取率为指标，通过单因素实验和响应面实验优化EATPP工艺条件。结果表明，最优EATPP工艺条件为酶解时间2 h，酶添加量433 U/g，叔丁醇用量3.9 mL/g，硫酸铵用量2.1 g/g，三相提取温度45 ℃，三相提取时间4 h。在最优工艺条件下，3个品种亚麻籽的亚麻籽油提取率为84.47%~89.03%，亚麻籽蛋白提取率为50.58%~55.69%，亚麻籽胶提取率为31.62%~35.61%。采用EATPP不仅能够同时分离亚麻籽中脂肪、蛋白质和胶，而且能够降低成本和提高亚麻籽的利用率。 Enzyme-assisted three phase partitioning (EATPP) was used to simultaneously extract oil, protein and gum from flaxseed, and the extraction rate of flaxseed oil was used as an indicator to optimize the process conditions of EATPP by single factor experiment and response surface methodology. The results showed that the optimal EATPP process conditions were obtained as follows: enzymatic hydrolysis time 2 h, enzyme dosage 433 U/g, tert-butanol dosage 3.9 mL/g, ammonium sulfate dosage 2.1 g/g, three phase extraction temperature 45 ℃, three phase extraction time 4 h. Under the optimal process conditions, the extraction rate of the flaxseed oil from three varieties reached 84.47%-8903%, the extraction rate of flaxseed protein reached 50.58%-55.69%, and the extraction rate of flaxseed gum reached 31.62%-35.61%. Using EATPP can not only simultaneously extract oil, protein and gum from flaxseed, but also can reduce cost and improve the utilization rate of flaxseed.

Existing face super-resolution methods are mainly based on an assumption that the input images are noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. To solve this problem, a multi-information fusion network was proposed, which can reconstruct high-resolution face images with rich details by using two prior information of face parsing image and face attributes. In this method, a coarse SR network was constructed to recover a coarse high-resolution image. Then, the prior information was modeled by an analytic graph estimation network and an attribute analysis and reconstruction network. The reconstruction of high-resolution face images was constrained in pixel space and semantic space. Experimental results show that the proposed method can effectively improve the robustness of the model to noise. On the CelebA data set, the reconstructed image of the proposed method not only have better subjective quality, but also have a peak signal to noise ratio( PSNR) improvement of about 0.2 dB compared with the mainstream methods [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Chinese Institute of Food Science & Technology is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)