166 related articles for article (PubMed ID: 27173544)
41. Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.
Guelpa A; Bevilacqua M; Marini F; O'Kennedy K; Geladi P; Manley M
Food Chem; 2015 Apr; 173():1220-7. PubMed ID: 25466147
[TBL] [Abstract][Full Text] [Related]
42. Hyperspectral sensing data analysis based on quasiconformal mapping-based multiple kernels learning machine.
Li JB; Xie X; Zhai J; Pan JS
Rev Sci Instrum; 2017 Jun; 88(6):065004. PubMed ID: 28668006
[TBL] [Abstract][Full Text] [Related]
43. [Comparison of methods for determining corn hardness (Zea mays L.)].
Salinas Y; Martínez F; Gomez J
Arch Latinoam Nutr; 1992 Mar; 42(1):59-63. PubMed ID: 1308647
[TBL] [Abstract][Full Text] [Related]
44. Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals.
Fox G; Manley M
J Sci Food Agric; 2014 Jan; 94(2):174-9. PubMed ID: 24038031
[TBL] [Abstract][Full Text] [Related]
45. Identification of Haploid Maize Kernel Using NIR Spectroscopy in Reflectance and Transmittance Modes: A Comparative Study.
Qin H; Ma JY; Chen SJ; Yan YL; Li W; Wang P; Liu J
Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Jan; 36(1):292-7. PubMed ID: 27228785
[TBL] [Abstract][Full Text] [Related]
46. Evaluation of near-infrared hyperspectral imaging for the assessment of potato processing aptitude.
López-Maestresalas A; Lopez-Molina C; Oliva-Lobo GA; Jarén C; Ruiz de Galarreta JI; Peraza-Alemán CM; Arazuri S
Front Nutr; 2022; 9():999877. PubMed ID: 36324619
[TBL] [Abstract][Full Text] [Related]
47. Effects of Varieties, Producing Areas, Ears, and Ear Positions of Single Maize Kernels on Near-Infrared Spectra for Identification and Traceability.
An D; Cui Y; Liu X; Jia S; Zheng S; Che X; Liu Z; Zhang X; Zhu D; Li S
PLoS One; 2016; 11(9):e0161489. PubMed ID: 27598344
[TBL] [Abstract][Full Text] [Related]
48. [Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region].
Tian X; Huang WQ; Li JB; Fan SX; Zhang BH
Guang Pu Xue Yu Guang Pu Fen Xi; 2016 Oct; 36(10):3237-42. PubMed ID: 30246759
[TBL] [Abstract][Full Text] [Related]
49. Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics.
Long Y; Huang W; Wang Q; Fan S; Tian X
Food Chem; 2022 Mar; 372():131246. PubMed ID: 34818727
[TBL] [Abstract][Full Text] [Related]
50. Near infrared hyperspectral imaging in quality and safety evaluation of cereals.
Sendin K; Williams PJ; Manley M
Crit Rev Food Sci Nutr; 2018 Mar; 58(4):575-590. PubMed ID: 27622307
[TBL] [Abstract][Full Text] [Related]
51. Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds.
Bai X; Zhang C; Xiao Q; He Y; Bao Y
RSC Adv; 2020 Mar; 10(20):11707-11715. PubMed ID: 35496579
[TBL] [Abstract][Full Text] [Related]
52. Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data.
Hu M; Zhai G; Zhao Y; Wang Z
Sci Rep; 2018 Apr; 8(1):6671. PubMed ID: 29703949
[TBL] [Abstract][Full Text] [Related]
53. [Predicting the chemical composition of intact kernels in maize hybrids by near infrared reflectance spectroscopy].
Wei LM; Jiang HY; Li JH; Yan YL; Dai JR
Guang Pu Xue Yu Guang Pu Fen Xi; 2005 Sep; 25(9):1404-7. PubMed ID: 16379276
[TBL] [Abstract][Full Text] [Related]
54. Fusarium species complex and mycotoxins in grain maize from maize hybrid trials and from grower's fields.
Dorn B; Forrer HR; Jenny E; Wettstein FE; Bucheli TD; Vogelgsang S
J Appl Microbiol; 2011 Sep; 111(3):693-706. PubMed ID: 21714835
[TBL] [Abstract][Full Text] [Related]
55. Near-Infrared Reflectance Spectroscopy (NIRS) assessment of δ(18)O and nitrogen and ash contents for improved yield potential and drought adaptation in maize.
Cabrera-Bosquet L; Sánchez C; Rosales A; Palacios-Rojas N; Araus JL
J Agric Food Chem; 2011 Jan; 59(2):467-74. PubMed ID: 21175211
[TBL] [Abstract][Full Text] [Related]
56. Classification of Fluorescently Labelled Maize Kernels Using Convolutional Neural Networks.
Wang Z; Guan B; Tang W; Wu S; Ma X; Niu H; Wan X; Zang Y
Sensors (Basel); 2023 Mar; 23(5):. PubMed ID: 36905044
[TBL] [Abstract][Full Text] [Related]
57. Hardness methods for testing maize kernels.
Fox G; Manley M
J Agric Food Chem; 2009 Jul; 57(13):5647-57. PubMed ID: 19496585
[TBL] [Abstract][Full Text] [Related]
58. [Feasibility study on an approach for identifying corn kernel varieties with seed coating agents via near infrared spectroscopy].
Jia SQ; Guo TT; Liu Z; Yan YL; An D; Gu JC; Li SM; Zhang SM; Zhu DH
Guang Pu Xue Yu Guang Pu Fen Xi; 2014 Nov; 34(11):2984-8. PubMed ID: 25752043
[TBL] [Abstract][Full Text] [Related]
59. Rapid and visual detection of the main chemical compositions in maize seeds based on Raman hyperspectral imaging.
Yang G; Wang Q; Liu C; Wang X; Fan S; Huang W
Spectrochim Acta A Mol Biomol Spectrosc; 2018 Jul; 200():186-194. PubMed ID: 29680497
[TBL] [Abstract][Full Text] [Related]
60. Classification of colonic tissues using near-infrared Raman spectroscopy and support vector machines.
Widjaja E; Zheng W; Huang Z
Int J Oncol; 2008 Mar; 32(3):653-62. PubMed ID: 18292943
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
