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6. Contribution of Stems and Leaves to the Quality of Keemun Black Tea

Author

ZHOU Hanchen, YANG Jihong, LIU Yaqin, WANG Hui, XU Yujie, HUANG Jianqin, LEI Pandeng

Subjects
keemun black tea, sensory evaluation, flavor compounds, volatile compounds, glycosisde compounds, Food processing and manufacture, TP368-456
Abstract

In order to understand the contribution of stems and leaves to the quality of Keemun black tea, this study analyzed the differences in aroma, taste quality and metabolites among black tea manufactured from tender leaves, tender stems, single buds and intact buds with leaves (mainly one bud with two leaves) from the tea variety ‘Fuzao 2’ by gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), sensory evaluation, and preference evaluation. The results showed that all four black teas were sweet aroma type, and the bud-leaf tea had a strong sweet aroma. The single-bud tea had a fresh and brisk taste. The tender-stem tea scored highest in preference evaluation. Metabolite analysis showed the content of catechins was the highest in the single-bud black tea and the lowest in the tender-stem black tea, while free amino acids were significantly enriched in the tender-stem black tea, reaching up to 80 mg/g. The contents of quercetin and rutin were higher in the stem black tea, while the contents of kaempferol-3-O-glucoside and kaempferol-3-O-rutinoside were the highest in the leaf black tea. The ratio of thearubigins to theaflavins was 11.6, 9.4, 14.6, and 8.2 in black tea made from intact buds with leaves, buds, leaves, and tender stems, respectively. The total amount of volatile compounds in the tender-stem black tea (32.37 μg/g) was significantly lower than that in the bud (100.01 μg/g), leaf (95.67 μg/g), and bud-leaf black tea (92.42 μg/g). The contents of benzaldehyde, benzeneacetaldehyde and indole were higher in the tender stem black tea, while the contents of β-ionone, α-ionone and linalool oxide in the leaf black tea were higher than those in the other three black teas. This study shows that high contents of theanine in tender stems and glycoside aroma precursors in tea leaves contribute primarily to the formation of the quality of Keemun black tea.

Published
2023
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8. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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14. An economist's quest to 'reel in' illegal fishing

Author

Liu, Yaqin

Subjects
Fishing -- Economic aspects, Environmental economics -- Economic aspects, Environmental issues, Earth sciences
Abstract

I GREW UP IN AN EXTREMELY RURAL VILLAGE IN HUBEI, CHINA--a community filled with traditional Chinese culture and natural beauty. In college, I became fascinated with environmental economics and was [...]

Published
2024

15. Ultra-high Performance Liquid Chromatography-Tandem Mass Spectrometry Analysis of Dynamic Changes in Non-volatile Compounds in Green Tea during Storage at Ambient Temperature

Author

ZHOU Hanchen, WANG Hui, LIU Yaqin, LEI Pandeng

Subjects
green tea, storage, non-volatile compounds, ultra-high performance liquid chromatography-tandem mass spectrometry, flavonoids, lipids, Food processing and manufacture, TP368-456
Abstract

Herein, we used an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) system to investigate changes in the profile of non-volatile metabolites in green tea after different periods (15, 40, 80 and 120 days) of storage at 37 ℃. The color of green tea and tea infusion changed after 40 days of storage at 37 ℃ when compared with the control (stored at −80 ℃). During 40 days of storage, the content of catechins did not significantly change, but the content of free amino acids significantly decreased. A total of 684 non-volatile components were identified, 77 of which were found to be characteristic differential metabolites, the major ones being polyphenols, lipids and organic acids. The content of octadecadien-6-ynoic acid, an umami-related compound, significantly decreased after 40 days and was undetected after 80 days. The content of theaflavine-3-catechin, related to the color and taste of tea infusion, significantly increased with storage time. Similarly, the content of flavone glycoside increased with storage time. Moreover, the content of lipids such as triacylglycerols (TAGs) and diacylglycerol (DAGs) changed significantly after 15 days, and the content of free fatty acids increased with storage time. The findings from this study showed that the contents of lipids and flavonoids in green tea significantly changed during storage, which played an important role in the quality deterioration of green tea.

Published
2023
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16. Identification of Volatile and Non-Volatile Components in Taiping Houkui Tea Made from ‘Shidacha’ and Non-‘Shidacha’ Tea Varieties and Phylogenetic Analysis

Author

ZHOU Hanchen, LIU Yaqin, WANG Hui, YANG Jihong, XU Yujie, LEI Pandeng

Subjects
‘shidacha’, taiping houkui green tea, volatile components, non-volatile components, rna sequencing, Food processing and manufacture, TP368-456
Abstract

Taiping Houkui green tea is mainly made from the young shoots of the sextual tea line ‘Shidacha’. In the present study, the differences in the quality and genetic evolution of green tea made from the young shoots of five ‘Shidacha’ varieties (Gaojiazao, Huangzhong, Shidacha No.2, Shidacha No.6, and Guangyangzao) and two non-‘Shidacha’ tea varieties (Fuzao No.2 and Shuchazao) were explored. The volatile and non-volatile metabolites of the seven tea samples were analyzed, and the differences in genome-wide transcriptional levels and genetic background among the seven tea varieties were analyzed using RNA sequencing (RNA-seq). No significant differences in catechin content or the total content of free amino acids were found among all tea samples except Shuchazao, whereas caffeine content differed significantly among all samples. The contents of floral aroma compounds such as linalool, linalool oxide, geraniol and jasmone were relatively high in the infusion of ‘Shidacha’ green tea. RNA sequencing results showed that the genome-wide transcriptional level of Guangyangzao was close to that of the non-‘Shidacha’ tea varieties. In principal component analysis (PCA), Guangyangzao and the other ‘Shidacha’ varieties were distributed in different quadrants. Phylogenetic analysis showed that the non-‘Shidacha’ tea varieties had a close genetic relationship with each other as well as Gaojiazao and Huangzhong, but was far away from the other ‘Shidacha’ varieties. The transcriptional levels of most of the key enzyme genes involved in the biosynthesis of linalool, indole, nerolidol and jasmone lactone were higher in the ‘Shidacha’ tea varieties than in the non-‘Shidacha’ ones. This study provides important theoretical support for the breeding of excellent ‘Shidacha’ varieties and provides a theoretical basis for follow-up research on the formation mechanism of floral aroma compounds in tea leaves.

Published
2023
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17. Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation

Author

Zhou, Yujia, Pang, Shumao, Cheng, Jun, Sun, Yuhang, Wu, Yi, Zhao, Lei, Liu, Yaqin, Lu, Zhentai, Yang, Wei, and Feng, Qianjin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep learning registrations do not consider the problem that when the receptive field cannot cover the corresponding features in the moving image and the fixed image, it cannot output accurate displacement values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution. Multi-resolution and multi-convolution techniques can improve but fail to avoid this problem. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale, called the Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The "total" deformation field at each scale was computed by upsampling and weighted summing all the "residual" deformation fields at all its previous scales, which can effectively and accurately transfer the deformation fields from low resolution to high resolution and is used for warping the moving features at each scale. Simulation and real brain data results show that our method improves the accuracy of the registration and the rationality of the deformation field.

Published
2020

21. Experimental study on the preparation of ferrophosphorus alloy using dephosphorization furnace slag by carbothermic reduction

Author

He Sai, Lin Lu, Liu Yaqin, Feng Menglong, Hou Zhongxiao, and Wu Wei

Subjects
dephosphorization furnace slag, valuable element recovery, carbothermic reduction, phosphorus, Technology, Chemical technology, TP1-1185, Chemicals: Manufacture, use, etc., TP200-248
Abstract

With the development of the duplex converter process, the amount of dephosphorization furnace slag is increasing, and it has relatively low basicity. It has been indicated that the recovery of valuable elements from dephosphorization slag by carbothermic reduction has great environmental and economic benefits due to its high content of P, Fe, and Mn. In this work, the experimental slag was reduced by carbon powder in a resistance furnace at 1,550°C. The results show that 76.82% of P, 99.60% of Fe, and 11.82% of Mn in the slag are recycled at basicity 1.0, and the recovery ratios of P, Fe, and Mn are 34.58, 78.89, and 13.85% at basicity 2.0, respectively. The ferrophosphorus obtained from the reduction of the dephosphorization furnace slag can be used as a raw material for steel-making alloy, and the residual steel slag can be used as cement and other building materials. This work provides a reference for recycling and resource utilization of dephosphorization furnace slag.

Published
2022
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34. 2-Nitro-4-Carboxyphenylhydrazine and 2,4-Dicarboxylphenylhydrazine as a Pair of Novel Reactive MALDI Matrices for Rapid and Accurate Profiling of N-Glycome in Dual Ion Modes

Author

Liao, Jiancong, Wang, Huiwen, Zhou, Shiwen, Feng, Hongru, Liu, Yaqin, Zhao, Xiaoyong, and Pan, Yuanjiang

Abstract

N-glycosylation is closely linked to a wide range of biological functions in organisms. Owing to the constriction of awful crystals formed by conventional MALDI matrices and the extremely inferior ionization efficiency of N-glycans, the traditional direct detection of N-glycans by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been gradually replaced by postderivatization detection using reactive matrices. Nevertheless, the laborious identification of complex spectral peaks remains the major difficulty in N-glycan profiling. Hence, we logically designed and synthesized two novel reactive matrices, 2-nitro-4-carboxylphenylhydrazine (NCPH) and 2,4-dicarboxylphenylhydrazine (DCPH), and separately combined them with the acidic matrix 2,5-dihydroxybenzoic acid (DHB) to constitute two composite matrices with high on-target derivatization efficiency and significant promotion of N-glycan ionization for productive MALDI analysis in dual ion modes. Using both composite matrices, we can actualize MALDI-MS and MS2mass calibration in dual ion modes by postderivatization detection and fragmentation of dextrans and selectively enhance the ionization effect of oligosaccharides in mixed systems. Quite homogeneous cocrystals can ensure N-glycan quantification with decent linearity and reproducibility. A fixed mass difference derived from the identical N-glycan in two ion modes is available for rapid identification in complex biological samples. Ultimately, the developed strategy was triumphantly employed to identify and quantify the relative content and alteration tendency of peach N-glycans, which can be referable to the latent correlation between N-glycan expression and peach ripening.

Published
2024
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39. Bridging the Intention–Behavior Gap in Organic Food Consumption: Empirical Evidence from China.

Author

Liu, Yaqin, Wang, Peng, Zhang, Mengya, Chen, Xi, Li, Ke, and Qu, Jianying

Subjects
DIETARY patterns, CONSUMER behavior, STRUCTURAL equation modeling, SUSTAINABLE consumption, PLANNED behavior theory
Abstract

The transition to a low-carbon economy emphasizes the importance of green and low-carbon consumption; yet, there is often a discrepancy between consumers' intentions and their actual behavior regarding organic foods. This study aims to identify strategies to bridge this gap. The research model of organic food consumption intention and behavior is constructed, and a structural equation model is used to test the research hypotheses based on a valid sample of 480 residents of Guangdong Province through an online questionnaire survey. Further, the intention‒behavior gap is defined and its determinants are investigated through multiclass logistic regression. Finally, we categorize and forecast the alignment between consumption intentions and behaviors using machine learning algorithms. The results reveal that attitudes, social interactions, and cognitive information play crucial roles in aligning intentions with behaviors. By enhancing social information exchange or improving cognitive understanding, consumers can reduce their intention‒behavior discrepancy. This research offers valuable policy recommendations for fostering green consumption among residents from various perspectives. [ABSTRACT FROM AUTHOR]

Published
2024
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40. The Impact of Consumer Sentiment on Sales of New Energy Vehicles: Evidence from Textual Analysis.

Author

Liu, Yaqin, Zhang, Mengya, Chen, Xi, Li, Ke, and Tang, Liwei

Subjects
ELECTRIC vehicles, CONSUMER behavior, WORD frequency, SENTIMENT analysis, ENERGY shortages, ELECTRIC charge
Abstract

The advancement of new energy vehicles (NEVs) represents a strategic initiative to combatting climate change, mitigating the energy crisis, and fostering green growth. Using provincial panel data from China between 2017 and 2022, in this study, we applied machine learning techniques for sentiment analysis of textual reviews, used word frequency statistics to explore consumers' views on the attributes of new energy vehicles, and constructed a consumer sentiment index to study the impact of consumer sentiment on NEV sales. Considering the dependence of NEVs on a charging station, this paper explores the nonlinear impact of the popularity of charging stations on the relationship between consumer sentiment and sales of new energy vehicles. The findings indicate the potential for enhancement in the areas of space, interior design, and comfort of NEVs. Additionally, consumer sentiment was found to facilitate the diffusion of NEVs, with this effect being heterogeneous across different educational backgrounds, income levels, and ages. Furthermore, the availability of per capita public charging stations was shown to significantly reduce range anxiety and encourage consumer purchasing behavior. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Transcriptomics and metabolomics reveal the mechanism of metabolites changes in Cymbidium tortisepalum var. longibracteatum colour mutation cultivars.

Author

Jiang, Yu, Liu, Yaqin, Lin, Yang, Tu, Xunliang, and He, Junrong

Subjects
*METABOLOMICS, *TRANSCRIPTOMES, *METABOLITES, *GENETIC mutation, *COLOR, *FLAVONOIDS
Abstract

Background: Foliage color is considered an important ornamental character of Cymbidium tortisepalum (C. tortisepalum), which significantly improves its horticultural and economic value. However, little is understood on the formation mechanism underlying foliage-color variations. Methods: In this study, we applied a multi-omics approach based on transcriptomics and metabolomics, to investigate the biomolecule mechanisms of metabolites changes in C. tortisepalum colour mutation cultivars. Results: A total of 508 genes were identified as differentially expressed genes (DEGs) between wild and foliage colour mutation C. tortisepalum cultivars based on transcriptomic data. KEGG enrichment of DEGs showed that genes involved in phenylalanine metabolism, phenylpropanoid biosynthesis, flavonoid biosynthesis and brassinosteroid biosynthesis were most significantly enriched. A total of 420 metabolites were identified in C. tortisepalum using UPLC-MS/MS-based approach and 115 metabolites differentially produced by the mutation cultivars were identified. KEGG enrichment indicated that the most metabolites differentially produced by the mutation cultivars were involved in glycerophospholipid metabolism, tryptophan metabolism, isoflavonoid biosynthesis, flavone and flavonol biosynthesis. Integrated analysis of the metabolomic and transcriptomic data showed that there were four significant enrichment pathways between the two cultivars, including phenylalanine metabolism, phenylpropanoid biosynthesis, flavone and flavonol biosynthesis and flavonoid biosynthesis. Conclusion: The results of this study revealed the mechanism of metabolites changes in C. tortisepalum foliage colour mutation cultivars, which provides a new reference for breeders to improve the foliage color of C. tortisepalum. [ABSTRACT FROM AUTHOR]

Published
2024
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