Determination of Pear Internal Quality Attributes by Fourier Transform Near Infrared (FT-NIR) Spectroscopy and Multivariate Analysis

Food Analytical Methods

Volumn 6, Issue 2, 2013, Pages 569-577

  Jiang, Hui ^a   Zhu, Weixing ^a  

^a JIANGSU UNIVERSITY   (China)

Author keywords

Extreme learning machine; Fourier transform near infrared spectroscopy; Intact pear; Internal quality attributes; Latent vectors extraction

Indexed keywords

ERRORS; FORECASTING; FOURIER TRANSFORMS; FRUITS; INDEPENDENT COMPONENT ANALYSIS; INFRARED DEVICES; INTEGRATED CIRCUITS; KNOWLEDGE ACQUISITION; MACHINE LEARNING; MEAN SQUARE ERROR; MULTIVARIANT ANALYSIS; NEAR INFRARED SPECTROSCOPY; REGRESSION ANALYSIS; SPECTRUM ANALYSIS;

EXTREME LEARNING MACHINE; FOURIER TRANSFORM NEAR INFRARED SPECTROSCOPY; INTACT PEAR; INTERNAL QUALITY; LATENT VECTORS;

QUALITY CONTROL;

PYRUS;

EID: 84875050161     PISSN: 19369751     EISSN: 1936976X     Source Type: Journal    
DOI: 10.1007/s12161-012-9480-9     Document Type: Article

References (43)

1. Bartlett MS, Movellan JR, Sejnowshi TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Network 13: 1450-1464.
2. Berger AJ, Feld MS (1997) Analytical method of estimating chemometric prediction error. Appl Spectrosc 51: 725-732.
3. Blanco D, Mulgrew B, McLaughlin S, Ruiz DP, Carrion MC (2006) The use of ICA in multiplicative noise. Neurocomputing 69: 1435-1441.
4. Cao LJ, Chua KS, Chong WK, Lee HP, Gu QM (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55: 321-336.
5. Carlomagno G, Capozzo L, Attolico G, Distante A (2004) Non-destructive grading of peaches by near-infrared spectrometry. Infrared Phys Technol 46: 23-29.
6. Cavaco AM, Guerra R, Silva JM, Antunes D (2008) Comparison of two different integration sphere configurations for 'Rocha' pear determination of firmness and soluble solids by Vis/NIR spectroscopy. In: 4th IASME/WSEAS International Conference on Energy, Environment, Ecosystems and Sustainable Development (EEESD'08), Algarve, Portugal, June 11-13, 2008, pp 526-531.
7. Centner V, de Noord OE, Massart DL (1998) Detection of nonlinearity in multivariate calibration. Anal Chim Acta 376: 153-168.
8. Chauchard F, Cogdill R, Roussel S, Roger JM, Bellon-Maurel V (2004) Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometr Intell Lab 71: 141-150.
9. Chen Q, Zhao J, Fang CH, Wang D (2007) Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochim Acta A 66: 568-574.
10. Chen Q, Cai J, Wan X, Zhao J (2011) Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy. LWT-Food Sci Technol 44: 2053-2058.
11. DiFoggio R (1995) Examination of some misconceptions about near-infrared analysis. Appl Spectrosc 49: 67-75.
12. Faber K, Kowalski BR (1997) Improved prediction error estimates for multivariate calibration by correcting for the measurement error in the reference values. Appl Spectrosc 51: 660-665.
13. Ferrer-Gallego R, Hernández-Hierro JM, Rivas-Gonzalo JC, Escribano-Bailón MT (2011) Determination of phenolic compounds of grape skins during ripening by NIR spectroscopy. LWT-Food Sci Technol 44: 847-853.
14. Fu X, Zhou Y, Ying Y, Lu H, Xu H (2006) NIR assessment of soluble solids and firmness for pears of different cultivars. Proc SPIE 6381: 63810N.
15. Gomez C, Viscarra Rossel RA, McBratney AB (2008) Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: an Australian case study. Geoderma 146: 403-411.
16. He Y, Li X, Deng X (2007) Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. J Food Eng 79: 1238-1242.
17. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IJCNN 2004), Budapest, Hungary, July 25-29, 2004, vol 2, pp 985-990.
18. Huang G-B, Liang N-Y, Rong H-J, Saratchandran P, Sundararajan N (2005) On-line sequential extreme learning machine. In: The IASTED International Conference on Computational Intelligence (CI 2005), Calgary, Canada, July 4-6, 2005.
19. Huang G-B, Chen L, Siew C-K (2006a) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Network 17: 879-892.
20. Huang G-B, Zhu Q-Y, Siew C-K (2006b) Extreme learning machine: theory and applications. Neurocomputing 70: 489-501.
21. Huang C, Han L, Yang Z, Liu X (2009) Exploring the use of near infrared reflectance spectroscopy to predict minerals in straw. Fuel 88: 163-168.
22. Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74: 155-163.
23. Katsumata N, Matsuyama Y (2005) Database retrieval for similar images using ICA and PCA bases. Eng Appl Artif Intel 18: 705-717.
24. Kim Y, Himmelsbach DS, Kays SE (2007) ATR-Fourier transform mid-infrared spectroscopy for determination of trans fatty acids in ground cereal products without oil extraction. J Agr Food Chem 55: 4327-4333.
25. Kou X, Liu X, Li J, Xiao H, Wang J (2012) Effects of ripening, 1-methylcyclopropene and ultra-high-pressure pasteurisation on the change of volatiles in Chinese pear cultivars. J Sci Food Agr 92: 177-183.
26. Koutras MV, Alexandrou VA (1997) Non-parametric randomness tests based on success runs of fixed length. Stat Probab Lett 32: 393-404.
27. Kramer K, Ebel S (2000) Application of NIR reflectance spectroscopy for the identification of pharmaceutical excipients. Anal Chim Acta 420: 155-161.
28. Kwak N, Kim C, Kim H (2008) Dimensionality reduction based on ICA for regression problems. Neurocomputing 71: 2596-2603.
29. Li J, Zhang Q, Cui Y, Yan J, Cao J, Zhao Y, Jiang W (2010) Use of UV-C treatment to inhibit the microbial growth and maintain the quality of Yali pear. J Food Sci 75: M503-M507.
30. Liu Y, Chen X, Ouyang A (2008) Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. LWT-Food Sci Technol 41: 1720-1725.
31. Liu Y, Sun X, Ouyang A (2010) Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN. LWT-Food Sci Technol 43: 602-607.
32. Liu Y, Liu W, Sun X, Gao R, Pan Y, Ouyang A (2011) Potable NIR spectroscopy predicting soluble solids content of pears based on LEDs. J Phys: Conf Ser 277: 012026.
33. Lorber A, Kowalski BR (1988) Estimation of prediction error for multivariate calibration. J Chemometr 2: 93-109.
34. McGlone VA, Jordan RB, Martinsen PJ (2002) Vis/NIR estimation at harvest of pre- and post-storage quality indices for 'Royal Gala' apple. Postharvest Biol Technol 25: 135-144.
35. Minhas R, Baradarani A, Seifzadeh S, Jonathan Wu QM (2010) Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73: 1906-1917.
36. Nguyen TV, Patra JC (2008) A simple ICA-based digital image watermarking scheme. Digit Signal Process 18: 762-776.
37. Nicolaï BM, Verlinden BE, Desmet M, Saevels S, Saeys W, Theron K, Cubeddu R, Pifferi A, Torricelli A (2008) Time-resolved and continuous wave NIR reflectance spectroscopy to predict soluble solids content and firmness of pear. Postharvest Biol Technol 47: 68-74.
38. Paz P, Sánchez M-T, Pérez-Marín D, Guerrero JE, Garrido-Varo A (2009) Instantaneous quantitative and qualitative assessment of pear quality using near infrared spectroscopy. Comput Electron Agr 69: 24-32.
39. Sun T, Lin H, Xu H, Ying Y (2009) Effect of fruit moving speed on predicting soluble solids content of 'Cuiguan' pears (Pomaceae pyrifolia Nakai cv. Cuiguan) using PLS and LS-SVM regression. Postharvest Biol Technol 51: 86-90.
40. Tanaka M, Kojima T (1996) Near-infrared monitoring of the growth period of Japanese pear fruit based on constituent sugar concentrations. J Agr Food Chem 44: 2272-2277.
41. Xiaobo Z, Jiewen Z, Xingyi H, Yanxiao L (2007) Use of FT-NIR spectrometry in non-invasive measurements of soluble solid contents (SSC) of 'Fuji' apple based on different PLS models. Chemometr Intell Lab 87: 43-51.
42. Yang L, Gong H, Chen K (2010) Simultaneous measurement of soluble solid content, pH, firmness and density of 'Dangshan' pear using FT-NIR spectrometry. In: 2010 3rd International Congress on Image and Signal Processing (CISP2010), Oct. 16-18 2010, pp 3380-3385.
43. Ye Y, Zhang Z-L, Zeng J, Peng L (2008) A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction. Appl Math Comput 205: 799-806.