Practical comparison of sparse methods for classification of Arabica and Robusta coffee species using near infrared hyperspectral imaging

Chemometrics and Intelligent Laboratory Systems

Volumn 146, Issue , 2015, Pages 503-511

  Calvini, Rosalba ^a   Ulrici, Alessandro ^a   Amigo, Jose Manuel ^b  

^a UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Green coffee beans; HIS NIR spectroscopy; Hyperspectral imaging; Sparse methods; Variable selection

Indexed keywords

ARTICLE; COFFEA ARABICA; COFFEA CANEPHORA; COFFEE; DISCRIMINANT ANALYSIS; FOOD ANALYSIS; IMAGE ANALYSIS; INTERMETHOD COMPARISON; K NEAREST NEIGHBOR; MATHEMATICAL ANALYSIS; NEAR INFRARED SPECTROSCOPY; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; QUALITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY; SPECIES DIFFERENCE;

EID: 84937918622     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2015.07.010     Document Type: Article

References (51)

1. Gowen A.A., O'Donnell C., Cullen P.J., Downey G., Frias J.M. Hyperspectral imaging-an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 2007, 18(12):590-598.
2. Amigo J.M., Cruz J., Bautista M., Maspoch S., Coello J., Blanco M. Study of pharmaceutical samples by NIR chemical-image and multivariate analysis. TrAC Trends Anal. Chem. 2008, 27(8):696-713.
3. Amigo J.M. Practical issues of hyperspectral imaging analysis of solid dosage forms. Anal. Bioanal. Chem. 2010, 398(1):93-109.
4. Burger J., Gowen A. Data handling in hyperspectral image analysis. Chemom. Intell. Lab. Syst. 2011, 108(1):13-22.
5. Vidal M., Amigo J.M. Pre-processing of hyperspectral images. Essential steps before image analysis. Chemom. Intell. Lab. Syst. 2012, 117:138-148.
6. Ferrari C., Foca G., Ulrici A. Handling large datasets of hyperspectral images: reducing data size without loss of useful information. Anal. Chim. Acta 2013, 802:29-39.
7. Xiaobo Z., Jiewen Z., Povey M.J.W., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 2010, 667(1):14-32.
8. Norgaard L., Saudland A., Wagner J., Nielsen J.P., Munck L., Engelsen S.B. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc. 2000, 54(3):413-419.
9. Leardi R., Norgaard L. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemom. 2004, 18(11):486-497.
10. Foca G., Salvo D., Cino A., Ferrari C., Lo Fiego D.P., Minelli G., Ulrici A. Classification of pig fat samples from different subcutaneous layers by means of fast and nondestructive analytical techniques. Food Res. Int. 2013, 52(1):185-197.
11. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J. Chemom. 2000, 14(5-6):643-655.
12. Jouan-Rimbaud D., Walczak B., Poppi R.J., de Noord O.E., Massart D.L. Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration. Anal. Chem. 1997, 69(21):4317-4323.
13. Cocchi M., Seeber R., Ulrici A. WPTER: wavelet packet transform for efficient pattern recognition of signals. Chemom. Intell. Lab. Syst. 2001, 57(2):97-119.
14. Friedman J.H. Fast sparse regression and classification. Int. J. Forecast. 2012, 28(3):722-738.
15. Lê Cao K.A., Martin P.G., Robert-Granié C., Besse P. Sparse canonical methods for biological data integration: application to a cross-platform study. BMC bioinformatics 2009, 10(1):34.
16. Allen G.I., Maletić-Savatić M. Sparse non-negative generalized PCA with applications to metabolomics. Bioinformatics 2011, 27(21):3029-3035.
17. Colombani C., Croiseau P., Fritz S., Guillaume F., Legarra A., Ducrocq V., Robert-Granié C. A comparison of partial least squares (PLS) and sparse PLS regressions in genomic selection in French dairy cattle. J. Dairy Sci. 2012, 95(4):2120-2131.
18. Karaman I., Qannari E.M., Martens H., Hedemann M.S., Knudsen K.E.B., Kohler A. Comparison of Sparse and Jack-knife partial least squares regression methods for variable selection. Chemom. Intell. Lab. Syst. 2013, 122:65-77.
19. Wang R., Zeng W., Ming J. Discrimination of the white wine based on sparse principal component analysis and support vector machine. Computer Engineering and Networking 2014, 695-702.
20. Kunz M.R., Ottaway J., Kalivas J.H., Georgiou C.A., Mousdis G.A. Updating a synchronous fluorescence spectroscopic virgin olive oil adulteration calibration to a new geographical region. J. Agric. Food Chem. 2011, 59(4):1051-1057.
21. Filzmoser P., Gschwandtner M., Todorov V. Review of sparse methods in regression and classification with application to chemometrics. J. Chemom. 2012, 26:42-51.
22. Witten D.M., Tibshirani R., Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Bio-statistics 2009, 10(3):515-534.
23. Lee M., Shen H., Huang J.Z., Marron J.S. Biclustering via sparse singular value de-composition. Biometrics 2010, 66:1087-1095.
24. Bro R., Papalexakis E.E., Acar E., Sidiropoulos N.D. Coclustering - a useful tool for chemometrics. J. Chemom. 2012, 26(6):256-263.
25. Zou H., Hastie T., Tibshirani R. Sparse principal component analysis. J. Comput. Graph. Stat. 2006, 15(2):265-286.
26. Jolliffe I.T., Trendafilov N.T., Uddin M. A modified principal component technique based on the LASSO. J. Comput. Graph. Stat. 2003, 12(3):531-547.
27. Shen H., Huang J.Z. Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 2008, 99(6):1015-1034.
28. Chun H., Keleş S. Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J. R. Stat. Soc. Ser. B (Stat Methodol.) 2010, 72(1):3-25.
29. Lê Cao K.A., Rossouw D., Robert-Granié C., Besse P. A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. Biol. 2008, 7(1):37.
30. Lê Cao K.A., Boitard S., Besse P. Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC bioinformatics 2011, 12(1):253-269.
31. Clemmensen L., Hastie T., Witten D., Ersbøll B. B, Sparse discriminant analysis. Technometrics 2011, 53(4):406-413.
32. Peerbhay K., Mutanga O., Ismail R. Does simultaneous variable selection and dimension reduction improve the classification of pinus forest species?. J. Appl. Remote Sens. 2014, 8(1):085194. 10.1117/1.JRS.8.085194.
33. Rasmussen M.A., Bro R. A tutorial on the Lasso approach to sparse modeling. Chemom. Intell. Lab. Syst. 2012, 119:21-31.
34. Martín M., Pablos F., González A. Discrimination between arabica and robusta green coffee varieties according to their chemical composition. Talanta 1998, 46:1259-1264.
35. Santos J., Sarraguça M., Rangel A., Lopes J. Evaluation of green coffee beans quality using near infrared spectroscopy: a quantitative approach. Food Chem. 2012, 135:1828-1835.
36. Myles A.J., Zimmerman T.A., Brown S.D. Transfer of multivariate classification models between laboratory and process near-infrared spectrometers for the discrimination of green arabica and robusta coffee beans. Appl. Spectrosc. 2006, 60:1198-1203.
37. Esteban-Di{dotless}ez I., González-Sáiz J.M., Pizarro C. An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS. Anal. Chim. Acta 2004, 514:57-67.
38. Esteban-Diez I., Gonzalez-iaiz J.M., Saenz-Gonzalez C., Pizarro C. Coffee varietal differentiation based on near infrared spectroscopy. Talanta 2007, 71:221-229.
39. Fiore A.G., Romaniello R., Peri G., Severini C. Quality assessment of roasted coffee blends by hyperspectral image analysis. Proceedings of 22nd International Conference on Coffee Science, Campinas, Brazil 2008.
40. Backhaus A., Bollenbeck F., Seiffert U. High-throughput quality control of coffee varieties and blends by artificial neural networks and hyperspectral imaging. Proceedings of the 1st International Congress on Cocoa, Coffee and Tea, CoCoTea 2011.
41. Wold S., Esbensen K., Geladi P. Principal component analysis. Chemom. Intell. Lab. Syst. 1987, 2:37-52.
42. Cover T., Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13:21-27.
43. Chevallier S., Bertrand D., Kohler A., Courcoux P. Application of PLS-DA in multivariate image analysis. J. Chemometrics 2006, 20:221-229.
44. Tibshirami R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. 1996, 58:267-288.
45. Lorber A., Wangen L., Kowalski B. A theoretical foundation for the PLS algorithm. J. Chemom. 1987, 1:19-31.
46. Burger J., Geladi P. Hyperspectral NIR image regression part II: dataset preprocessing diagnostics. J. Chemom. 2006, 20:106-119.
47. Burger J., Geladi P. Hyperspectral NIR image regression part I: calibration and correction. J. Chemom. 2005, 19:355-363.
48. Ulrici A., Serranti S., Ferrari C., Cesare D., Foca G., Bonifazi G. Efficient chemometric strategies for PET-PLA discrimination in recycling plants using hyperspectral imaging. Chemom. Intell. Lab. Syst. 2013, 122:31-39.
49. Forina M., Oliveri P., Jäger H., Römisch U., Smeyers-Verbeke J. Class modeling techniques in the control of the geographical origin of wines. Chemom. Intell. Lab. Syst. 2009, 99:127-137.
50. Szymanska E., Brodrick E., Williams M., Davies A.N., Van Manen H.J., Buydens L.C.M. Data size reduction strategy for the classification of breath and air samples using multicapillary column-ion mobility spectrometry. Anal. Chem. 2015, 87(2):869-875.
51. Handbook of Near Infrared Analysis 2007, Third Edition. D.A. Burns, E.W. Ciurzak (Eds.).