144 related articles for article (PubMed ID: 38257231)
1. The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee.
Green S; Fanning E; Sim J; Eyres GT; Frew R; Kebede B
Molecules; 2024 Jan; 29(2):. PubMed ID: 38257231
[TBL] [Abstract][Full Text] [Related]
2. Single Origin Coffee Aroma: From Optimized Flavor Protocols and Coffee Customization to Instrumental Volatile Characterization and Chemometrics.
Zakidou P; Plati F; Matsakidou A; Varka EM; Blekas G; Paraskevopoulou A
Molecules; 2021 Jul; 26(15):. PubMed ID: 34361765
[TBL] [Abstract][Full Text] [Related]
3. Classification of Black Mahlab seeds (Monechma ciliatum) using GC-MS and FT-NIR and simultaneous prediction of their major volatile compounds using chemometrics.
Elrasheid Tahir H; Adam Mariod A; Hashim SBH; Arslan M; Komla Mahunu G; Xiaowei H; Zhihua L; Abdalla IIH; Xiaobo Z
Food Chem; 2023 May; 408():134948. PubMed ID: 36528991
[TBL] [Abstract][Full Text] [Related]
4. Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).
Zhang X; Li W; Yin B; Chen W; Kelly DP; Wang X; Zheng K; Du Y
Spectrochim Acta A Mol Biomol Spectrosc; 2013 Oct; 114():350-6. PubMed ID: 23786975
[TBL] [Abstract][Full Text] [Related]
5. Rapid Detection of Volatile Oil in
Yan H; Guo C; Shao Y; Ouyang Z
Pharmacogn Mag; 2017; 13(51):439-445. PubMed ID: 28839369
[TBL] [Abstract][Full Text] [Related]
6. Near infrared spectroscopy: an analytical tool to predict coffee roasting degree.
Alessandrini L; Romani S; Pinnavaia G; Dalla Rosa M
Anal Chim Acta; 2008 Sep; 625(1):95-102. PubMed ID: 18721545
[TBL] [Abstract][Full Text] [Related]
7. Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy.
Adnan A; Hörsten DV; Pawelzik E; Mörlein AD
Foods; 2017 May; 6(5):. PubMed ID: 28534842
[TBL] [Abstract][Full Text] [Related]
8. Brazilian Coffee Blends: A Simple and Fast Method by Near-Infrared Spectroscopy for the Determination of the Sensory Attributes Elicited in Professional Coffee Cupping.
Baqueta MR; Coqueiro A; Valderrama P
J Food Sci; 2019 Jun; 84(6):1247-1255. PubMed ID: 31116425
[TBL] [Abstract][Full Text] [Related]
9. Application of UV-Vis-NIR and FTIR spectroscopy coupled with chemometrics for quality prediction of katsuobushi based on the number of smoking treatments.
Park M; Yu JY; Ko JA; Park HJ
Food Chem; 2024 Jun; 442():138604. PubMed ID: 38306767
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of spontaneous fermentation impact on the physicochemical properties and sensory profile of green and roasted arabica coffee by digital technologies.
Wu H; Gonzalez Viejo C; Fuentes S; Dunshea FR; Suleria HAR
Food Res Int; 2024 Jan; 176():113800. PubMed ID: 38163710
[TBL] [Abstract][Full Text] [Related]
11. Fourier transform infrared spectroscopy and near infrared spectroscopy for the quantification of defects in roasted coffees.
Craig AP; Franca AS; Oliveira LS; Irudayaraj J; Ileleji K
Talanta; 2015 Mar; 134():379-386. PubMed ID: 25618683
[TBL] [Abstract][Full Text] [Related]
12. Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees.
Pires FC; Pereira RGFA; Baqueta MR; Valderrama P; Alves da Rocha R
Food Chem; 2021 Dec; 365():130471. PubMed ID: 34252622
[TBL] [Abstract][Full Text] [Related]
13. Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) Fingerprinting and Chemometrics for Coffee Classification and Authentication.
Núñez N; Saurina J; Núñez O
Molecules; 2023 Dec; 29(1):. PubMed ID: 38202813
[TBL] [Abstract][Full Text] [Related]
14. Impact of roasting on the phenolic and volatile compounds in coffee beans.
Wu H; Lu P; Liu Z; Sharifi-Rad J; Suleria HAR
Food Sci Nutr; 2022 Jul; 10(7):2408-2425. PubMed ID: 35844912
[TBL] [Abstract][Full Text] [Related]
15. How to Identify Roast Defects in Coffee Beans Based on the Volatile Compound Profile.
Rusinek R; Dobrzański B; Oniszczuk A; Gawrysiak-Witulska M; Siger A; Karami H; Ptaszyńska AA; Żytek A; Kapela K; Gancarz M
Molecules; 2022 Dec; 27(23):. PubMed ID: 36500625
[TBL] [Abstract][Full Text] [Related]
16. Smart Online Coffee Roasting Process Control: Modelling Coffee Roast Degree and Brew Antioxidant Capacity for Real-Time Prediction by Resonance-Enhanced Multi-Photon Ionization Mass Spectrometric (REMPI-TOFMS) Monitoring of Roast Gases.
Czech H; Heide J; Ehlert S; Koziorowski T; Zimmermann R
Foods; 2020 May; 9(5):. PubMed ID: 32422859
[TBL] [Abstract][Full Text] [Related]
17. Prediction of specialty coffee cup quality based on near infrared spectra of green coffee beans.
Tolessa K; Rademaker M; De Baets B; Boeckx P
Talanta; 2016 Apr; 150():367-74. PubMed ID: 26838420
[TBL] [Abstract][Full Text] [Related]
18. Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS.
Caporaso N; Whitworth MB; Cui C; Fisk ID
Food Res Int; 2018 Jun; 108():628-640. PubMed ID: 29735099
[TBL] [Abstract][Full Text] [Related]
19. Geographical discrimination of Paw San rice cultivated in different regions of Myanmar using near-infrared spectroscopy, headspace-gas chromatography-ion mobility spectrometry and chemometrics.
Thantar S; Mihailova A; Islam MD; Maxwell F; Hamed I; Vlachou C; Kelly SD
Talanta; 2024 Jun; 273():125910. PubMed ID: 38492284
[TBL] [Abstract][Full Text] [Related]
20. Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: possibilities and limits.
Smyth HE; Cozzolino D; Cynkar WU; Dambergs RG; Sefton M; Gishen M
Anal Bioanal Chem; 2008 Apr; 390(7):1911-6. PubMed ID: 18283438
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
