Quantitative analysis of BPO additive in flour via Raman hyperspectral imaging technology
文献类型: 外文期刊
第一作者: Wang, Xiaobin
作者: Wang, Xiaobin;Huang, Wenqian;Zhao, Chunjiang;Wang, Qingyan;Liu, Chen;Yang, Guiyan;Wang, Xiaobin;Huang, Wenqian;Zhao, Chunjiang;Wang, Qingyan;Liu, Chen;Yang, Guiyan;Wang, Xiaobin;Huang, Wenqian;Zhao, Chunjiang;Wang, Qingyan;Liu, Chen;Yang, Guiyan;Wang, Xiaobin;Huang, Wenqian;Zhao, Chunjiang;Wang, Qingyan;Liu, Chen;Yang, Guiyan;Wang, Xiaobin;Zhao, Chunjiang
作者机构:
关键词: Raman hyperspectral imaging technology;Flour;BPO additive;Quantitative analysis
期刊名称:EUROPEAN FOOD RESEARCH AND TECHNOLOGY ( 影响因子:2.998; 五年影响因子:3.005 )
ISSN:
年卷期:
页码:
收录情况: SCI
摘要: Raman hyperspectral imaging technology not only can acquire the image information of the sample; it also contains the Raman spectra information about each pixel. Due to the abundant information that the method provides, it has been applied to detect food safety. This study adopted line-scan Raman hyperspectral technology to quantify benzoyl peroxide (BPO) additive in flour. By analyzing the Raman spectra of BPO and flour, the 999 cm(-1) Raman peak was selected for the detection and identification of BPO in flour. Savitzky-Golay filter and adaptive iteratively reweighted penalized least squares (airPLS) methods were used to de-noise and fluorescence correction of the original Raman signals. Binary image was established by 999 cm(-1) single-band correction image and threshold segmentation, and this method was used to detect 11 mixture samples with different BPO additive concentrations. The results show that the BPO additives in the mixture samples can be detected, and the detected BPO pixels had a good linear relationship with the concentration of BPO in the mixture samples, correlation coefficient was 0.9902. The above results indicated that the method established in this paper can be applied to non-destructive quantitative detection of BPO additive in flour.
分类号: R15
- 相关文献
作者其他论文 更多>>
-
Chromosome-level genome assembly of the sweet potato rot nematode Ditylenchus destructor
作者:Yang, Yiwei;Hong, Bo;Liu, Chen;Li, Yingmei;Chang, Qing;Feng, Ruirui;Fang, Yuchuan;Wang, Kui;Peng, Deliang;Peng, Huan
关键词:
-
D-glucose-derived S-doped porous carbon: Sustainable and effective CO2 adsorption
作者:Xu, Qianyu;Wang, Junting;Feng, Jiamin;Liu, Chen;Hu, Xin;Xiao, Qiang;Demir, Muslum;Simsek, Utku Bulut;Demir, Muslum;Kilic, Murat;Wang, Linlin
关键词:S-doped porous carbons; Biomass; CO2 adsorption; Sodium thiosulfate, D-glucose
-
Recognition of maize seedling under weed disturbance using improved YOLOv5 algorithm
作者:Tang, Boyi;Zhao, Chunjiang;Tang, Boyi;Zhou, Jingping;Pan, Yuchun;Qu, Xuzhou;Cui, Yanglin;Liu, Chang;Li, Xuguang;Zhao, Chunjiang;Gu, Xiaohe;Li, Xuguang
关键词:Object detection; Maize seedlings; UAV RGB images; YOLOv5; Attention mechanism
-
Multi-Omics analysis reveals the sensory quality and fungal communities of Tibetan teas produced by wet- and dry-piling fermentation
作者:Chen, Shengxiang;Zhang, Mengxue;Luo, Shijie;Ning, Meiyi;Chen, Yuxi;Tan, Liqiang;Liu, Chen;Tang, Xiaobo;Liu, Xiao;Zhang, Ting;Zheng, Liang;Saarloos, Aafke
关键词:Ya'an Tibetan tea; Wet-pile fermentation; Dry-pile fermentation; Differential metabolites; Fungal communities
-
Boosting Cost-Efficiency in Robotics: A Distributed Computing Approach for Harvesting Robots
作者:Xie, Feng;Xie, Feng;Li, Tao;Feng, Qingchun;Li, Tao;Feng, Qingchun;Chen, Liping;Zhao, Chunjiang;Zhao, Hui
关键词:5G network; computation allocation; edge computing; harvesting robot; visual system
-
Genotyping Identification of Maize Based on Three-Dimensional Structural Phenotyping and Gaussian Fuzzy Clustering
作者:Xu, Bo;Zhao, Chunjiang;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao;Xu, Bo;Zhao, Chunjiang;Yang, Guijun;Zhang, Yuan;Liu, Changbin;Feng, Haikuan;Yang, Xiaodong;Yang, Hao
关键词:tassel; 3D phenotyping; TreeQSM; genotyping; clustering
-
High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges
作者:Cheng, Tao;Zhang, Dongyan;Cheng, Tao;Wang, Zhaoming;Zhang, Dongyan;Zhang, Gan;Yuan, Feng;Liu, Yaling;Wang, Tianyi;Ren, Weibo;Zhao, Chunjiang
关键词:Forage; High-throughput phenotyping; Precision identification; Sensors; Artificial intelligence; Efficient breeding