200 related articles for article (PubMed ID: 38201054)
21. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology.
Ren G; Wang Y; Ning J; Zhang Z
J Sci Food Agric; 2021 Mar; 101(5):2135-2142. PubMed ID: 32981110
[TBL] [Abstract][Full Text] [Related]
22. Non-destructive detection of protein content in mulberry leaves by using hyperspectral imaging.
Li X; Peng F; Wei Z; Han G; Liu J
Front Plant Sci; 2023; 14():1275004. PubMed ID: 37900759
[TBL] [Abstract][Full Text] [Related]
23. Nondestructive detection of anthocyanin content in fresh leaves of purple maize using hyperspectral data.
Yang X; Gao S; Gu X; Zhang C; Sun Q; Wei Z; Hu X; Qu X
Appl Opt; 2022 Jul; 61(21):6213-6222. PubMed ID: 36256234
[TBL] [Abstract][Full Text] [Related]
24. [Determination of total nitrogen content in fresh tea leaf using visible-near infrared spectroscopy].
Hu YG; Li PP; Mu JH; Mao HP; Wu CC; Chen B
Guang Pu Xue Yu Guang Pu Fen Xi; 2008 Dec; 28(12):2821-5. PubMed ID: 19248491
[TBL] [Abstract][Full Text] [Related]
25. Advanced Techniques for Hyperspectral Imaging in the Food Industry: Principles and Recent Applications.
Ma J; Sun DW; Pu H; Cheng JH; Wei Q
Annu Rev Food Sci Technol; 2019 Mar; 10():197-220. PubMed ID: 30633569
[TBL] [Abstract][Full Text] [Related]
26. Principles of Hyperspectral Microscope Imaging Techniques and Their Applications in Food Quality and Safety Detection: A Review.
Pu H; Lin L; Sun DW
Compr Rev Food Sci Food Saf; 2019 Jul; 18(4):853-866. PubMed ID: 33337001
[TBL] [Abstract][Full Text] [Related]
27. Research on moldy tea feature classification based on WKNN algorithm and NIR hyperspectral imaging.
Xin Z; Jun S; Xiaohong W; Bing L; Ning Y; Chunxia D
Spectrochim Acta A Mol Biomol Spectrosc; 2019 Jan; 206():378-383. PubMed ID: 30157445
[TBL] [Abstract][Full Text] [Related]
28. Development and Validation of Near-Infrared Methods for the Quantitation of Caffeine, Epigallocatechin-3-gallate, and Moisture in Green Tea Production.
Zhang S; Zuo Y; Wu Q; Wang J; Ban L; Yang H; Bai Z
J Anal Methods Chem; 2021; 2021():9563162. PubMed ID: 34820146
[TBL] [Abstract][Full Text] [Related]
29. Emerging Spectroscopic and Spectral Imaging Techniques for the Rapid Detection of Microorganisms: An Overview.
Wang K; Pu H; Sun DW
Compr Rev Food Sci Food Saf; 2018 Mar; 17(2):256-273. PubMed ID: 33350086
[TBL] [Abstract][Full Text] [Related]
30. Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy.
Li C; Guo H; Zong B; He P; Fan F; Gong S
Spectrochim Acta A Mol Biomol Spectrosc; 2019 Jan; 206():254-262. PubMed ID: 30121024
[TBL] [Abstract][Full Text] [Related]
31. Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer.
Zeng J; Ping W; Sanaeifar A; Xu X; Luo W; Sha J; Huang Z; Huang Y; Liu X; Zhan B; Zhang H; Li X
Plant Methods; 2021 Jan; 17(1):4. PubMed ID: 33407678
[TBL] [Abstract][Full Text] [Related]
32. [Application of near-infrared spectroscopy to quality detection of milk and its products].
Wang J; Wang JQ; Bu DP; Guo WJ; Shen JS; Wei HY; Zhou LY; Liu KL
Guang Pu Xue Yu Guang Pu Fen Xi; 2009 May; 29(5):1281-5. PubMed ID: 19650471
[TBL] [Abstract][Full Text] [Related]
33. Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging.
Dong C; Yang C; Liu Z; Zhang R; Yan P; An T; Zhao Y; Li Y
Sensors (Basel); 2021 Dec; 21(23):. PubMed ID: 34884054
[TBL] [Abstract][Full Text] [Related]
34. Quality evaluation of Keemun black tea by fusing data obtained from near-infrared reflectance spectroscopy and computer vision sensors.
Song Y; Wang X; Xie H; Li L; Ning J; Zhang Z
Spectrochim Acta A Mol Biomol Spectrosc; 2021 May; 252():119522. PubMed ID: 33582437
[TBL] [Abstract][Full Text] [Related]
35. Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials.
Manley M
Chem Soc Rev; 2014 Dec; 43(24):8200-14. PubMed ID: 25156745
[TBL] [Abstract][Full Text] [Related]
36. Color measurement of tea leaves at different drying periods using hyperspectral imaging technique.
Xie C; Li X; Shao Y; He Y
PLoS One; 2014; 9(12):e113422. PubMed ID: 25546335
[TBL] [Abstract][Full Text] [Related]
37. Identification of tea quality at different picking periods: A hyperspectral system coupled with a multibranch kernel attention network.
Wang Y; Ren Y; Kang S; Yin C; Shi Y; Men H
Food Chem; 2024 Feb; 433():137307. PubMed ID: 37683489
[TBL] [Abstract][Full Text] [Related]
38. Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging.
Shi Y; Wang Y; Hu X; Li Z; Huang X; Liang J; Zhang X; Zheng K; Zou X; Shi J
Food Chem; 2023 Jun; 411():135431. PubMed ID: 36681022
[TBL] [Abstract][Full Text] [Related]
39. LeafSpec-Dicot: An Accurate and Portable Hyperspectral Imaging Device for Dicot Leaves.
Li X; Chen Z; Wang J; Jin J
Sensors (Basel); 2023 Apr; 23(7):. PubMed ID: 37050749
[TBL] [Abstract][Full Text] [Related]
40. Nondestructive Testing for Wheat Quality with Sensor Technology Based on Big Data.
Tian YG; Zhang ZN; Tian SQ
J Anal Methods Chem; 2020; 2020():8851509. PubMed ID: 33274108
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]