These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
351 related articles for article (PubMed ID: 27342797)
1. Data dimensionality reduction and data fusion for fast characterization of green coffee samples using hyperspectral sensors. Calvini R; Foca G; Ulrici A Anal Bioanal Chem; 2016 Oct; 408(26):7351-66. PubMed ID: 27342797 [TBL] [Abstract][Full Text] [Related]
2. Handling large datasets of hyperspectral images: reducing data size without loss of useful information. Ferrari C; Foca G; Ulrici A Anal Chim Acta; 2013 Nov; 802():29-39. PubMed ID: 24176502 [TBL] [Abstract][Full Text] [Related]
3. Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee. Calvini R; Amigo JM; Ulrici A Anal Chim Acta; 2017 May; 967():33-41. PubMed ID: 28390483 [TBL] [Abstract][Full Text] [Related]
4. Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis. Zhang C; Liu F; He Y Sci Rep; 2018 Feb; 8(1):2166. PubMed ID: 29391427 [TBL] [Abstract][Full Text] [Related]
5. Hyperspectral face recognition with spatiospectral information fusion and PLS regression. Uzair M; Mahmood A; Mian A IEEE Trans Image Process; 2015 Mar; 24(3):1127-37. PubMed ID: 25608305 [TBL] [Abstract][Full Text] [Related]
6. A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI). Ru C; Li Z; Tang R Sensors (Basel); 2019 May; 19(9):. PubMed ID: 31052476 [TBL] [Abstract][Full Text] [Related]
7. An innovative multivariate strategy for HSI-NIR images to automatically detect defects in green coffee. Oliveri P; Malegori C; Casale M; Tartacca E; Salvatori G Talanta; 2019 Jul; 199():270-276. PubMed ID: 30952257 [TBL] [Abstract][Full Text] [Related]
8. Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis. Buratti S; Sinelli N; Bertone E; Venturello A; Casiraghi E; Geobaldo F J Sci Food Agric; 2015 Aug; 95(11):2192-200. PubMed ID: 25258213 [TBL] [Abstract][Full Text] [Related]
9. Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber. Juliano da Silva C; Pasquini C Analyst; 2015 Jan; 140(2):512-22. PubMed ID: 25408949 [TBL] [Abstract][Full Text] [Related]
10. Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. Ravn C; Skibsted E; Bro R J Pharm Biomed Anal; 2008 Nov; 48(3):554-61. PubMed ID: 18774667 [TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method. Sun M; Zhang D; Liu L; Wang Z Food Chem; 2017 Mar; 218():413-421. PubMed ID: 27719929 [TBL] [Abstract][Full Text] [Related]
14. Integration of spectral and textural features of visible and near-infrared hyperspectral imaging for differentiating between normal and white striping broiler breast meat. Jiang H; Yoon SC; Zhuang H; Wang W; Li Y; Yang Y Spectrochim Acta A Mol Biomol Spectrosc; 2019 Apr; 213():118-126. PubMed ID: 30684880 [TBL] [Abstract][Full Text] [Related]
15. Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. Burger J; Geladi P Analyst; 2006 Oct; 131(10):1152-60. PubMed ID: 17003864 [TBL] [Abstract][Full Text] [Related]
16. [The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion]. Dong G; Guo J; Wang C; Chen ZL; Zheng L; Zhu DZ Guang Pu Xue Yu Guang Pu Fen Xi; 2015 Dec; 35(12):3369-74. PubMed ID: 26964212 [TBL] [Abstract][Full Text] [Related]
17. Classification of oat and groat kernels using NIR hyperspectral imaging. Serranti S; Cesare D; Marini F; Bonifazi G Talanta; 2013 Jan; 103():276-84. PubMed ID: 23200388 [TBL] [Abstract][Full Text] [Related]
18. Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique. Xie A; Sun DW; Xu Z; Zhu Z Talanta; 2015 Jul; 139():208-15. PubMed ID: 25882428 [TBL] [Abstract][Full Text] [Related]
19. Evaluation of green coffee beans quality using near infrared spectroscopy: a quantitative approach. Santos JR; Sarraguça MC; Rangel AO; Lopes JA Food Chem; 2012 Dec; 135(3):1828-35. PubMed ID: 22953929 [TBL] [Abstract][Full Text] [Related]
20. A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging. Amigo JM; Ravn C; Gallagher NB; Bro R Int J Pharm; 2009 May; 373(1-2):179-82. PubMed ID: 19429304 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]