Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification

Computers and Electronics in Agriculture

Volumn 89, Issue , 2012, Pages 175-181

  Huang, Min ^a   Zhu, Qibing ^a   Wang, Bojin ^a   Lu, Renfu ^b  

^a JIANGNAN UNIVERSITY   (China)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Apple; Hyperspectral scattering; Locally linear embedding; Mealiness; Partial least squares discriminant; Support vector machine

Indexed keywords

APPLE; HYPERSPECTRAL; LOCALLY LINEAR EMBEDDING; MEALINESS; PARTIAL LEAST SQUARE (PLS);

FRUITS; IMAGE RETRIEVAL; SCATTERING; SUPPORT VECTOR MACHINES;

ALGORITHMS;

ALGORITHM; DATA SET; DECIDUOUS TREE; DISCRIMINANT ANALYSIS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LINEARITY; NUMERICAL MODEL; PLANT EXTRACT; SPECTRAL ANALYSIS;

MALUS X DOMESTICA;

EID: 84867014749     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2012.09.003     Document Type: Article

References (24)

1. Andani Z., Jaeger S.R., Wakeling I., Macfie H.J.H. Mealiness in apples: towards a multilingual consumer vocabulary. Journal of Food Science 2001, 66(6):872-879.
2. Barreiro P., Ortiz C., Ruiz-Altisent M., Desmedt V., Schotte S., Andani Z., Wakeling I., Beyts P.K. Comparison between sensory and instrumental measurements for mealiness assessment in apples. A collaborative test. Food and Nutrition Press 1998, 29(5):509-525.
3. Barreiro P., Moya A., Correa E., Ruiz-Altisent M., Fernández-White M., Peris A., Wright K.M., Hills B.P. Prospects for the rapid detection of mealiness in apples by nondestructive NMR relaxometry. Applied Magnetic Resonance 2002, 22(3):387-400.
4. Bechar A., Mizrach A., Barreiro P., Landahl S. Determination of mealiness in apples using ultrasonic measurements. Biosystems Engineering 2005, 91(3):329-334.
5. Bignami C., Scossa A., Vagnoni G. Evaluation of old Italian apple cultivars by means of sensory analysis. Acta Horticulturae (ISHS) 2003, 598(1):85-90.
6. Chi M., Feng R., Bruzzone L. Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem. Advanced in Space Research 2008, 41(11):1793-1799.
7. De Smedt V., Barreiro P., Verlinden B.E., Veraverbeke E.A., De Baerdemaeker J., Nicolaï B.M. A mathematical model for the development of mealiness in apples. Postharvest Biology and Technology 2002, 25(3):273-291.
8. Harker F.R., Maindonald J., Murray S.H., Gunson F.A., Hallett I.C., Walker S.B. Sensory interpretation of instrumental measurements (I) texture of apple fruit. Postharvest Biology and Technology 2002, 24(3):225-239.
9. Huang M., Lu R. Apple mealiness detection using hyperspectral scattering technique. Postharvest Biology and Technology 2010, 58(3):168-175.
10. Infante R., Meneses C., Rubio P., Seibert E. Quantitative determination of flesh mealiness in peach through paper absorption of free juice. Postharvest Biology and Technology 2009, 51(1):118-121.
11. Khan N.M., Ksantini R., Ahmad I.S., Boufama B. A novel SVM+NDA model for classification with an application to face recognition. Pattern Recognition 2012, 45(1):66-79.
12. Lu R. Nondestructive measurement of firmness and soluble solids content for apple fruit using hyperspectral scattering images. Sensing and Instrumentation for Food Quality and Safety 2007, 1(1):19-27.
13. Moshou D., Wahlen S., Strasser R., Schenk A., Ramon H. Apple mealiness detection using fluorescence and self-organising maps. Computers and Electronics in Agriculture 2003, 40(1-3):103-114.
14. Ortiz C., Barreiro P., Correa E., Riquelme F., Ruiz-Altisent M. Non-destructive indentification of woolly peaches using impact response and near-infrared spectroscopy. Journal of Agricultural Engineering Research 2001, 78(3):281-289.
15. Peng Y., Lu R. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology 2008, 48(1):52-62.
16. Qin J., Lu R. Measurement of the absorption and scattering properties of turbid liquid foods using hyperspectral imaging. Applied Spectroscopy 2007, 61(4):388-396.
17. Qin J., Lu R. Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique. Postharvest Biology and Technology 2008, 49(3):355-365.
18. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
19. Sugumaran V., Ramachandran K.I. Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications 2011, 38(4):4088-4096.
20. Valero C., Barreiro P., Ruiz-Altisent M., Cubeddu R., Pifferi A., Taroni P., Torricelli A., Valentini G., Johnson D., Dover C. Mealiness detection in apples using time resolved reflectance spectroscopy. Journal of Texture Studies 2005, 36(4):439-458.
21. Vappnik V.N. The Nature of Statistical Learning Theory 2000, Springer, New York. second ed.
22. Zhang J., Zhuang Y.T. Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding. Journal of Zhejiang University Science A 2006, 8(4):550-558.
23. Zhao G., Zhu Q., Huang M. LLE-SVM classification of apple mealiness based on hyperspectral scattering image. Spectroscopy and Spectral Analysis 2010, 30(10):2739-2743.
24. Zhu R., Yao M. Image feature optimization based on nonlinear dimensionality reduction. Journal of Zhejiang University Science A 2009, 10(12):1720-1737.