Rapid classification of Chinese quince (Chaenomeles speciosa Nakai) fruit provenance by near-infrared spectroscopy and multivariate calibration

Analytical and Bioanalytical Chemistry

Volumn 409, Issue 1, 2017, Pages 115-120

  Shao, Wenhao ^a   Li, Yanjie ^a,b   Diao, Songfeng ^a   Jiang, Jingmin ^a   Dong, Ruxiang ^a  

^a RESEARCH INSTITUTE OF FORESTRY   (China)

^b UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

Chinese quince; LDA; NIR; QDA; SVM

Indexed keywords

DISCRIMINANT ANALYSIS; FRUITS; INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY;

CHINESE QUINCE; CLASSIFICATION ACCURACY; LINEAR DISCRIMINANT ANALYSIS; MEDICINAL MATERIALS; MULTIVARIATE CALIBRATION; NON-DESTRUCTIVE ANALYSIS; QUADRATIC DISCRIMINANT ANALYSIS; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

HERBACEOUS AGENT;

CALIBRATION; CHEMISTRY; CLASSIFICATION; DISCRIMINANT ANALYSIS; ECONOMICS; FRUIT; LEAST SQUARE ANALYSIS; NEAR INFRARED SPECTROSCOPY; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; ROSACEAE; SUPPORT VECTOR MACHINE; TIME FACTOR;

CALIBRATION; DISCRIMINANT ANALYSIS; DRUGS, CHINESE HERBAL; FRUIT; LEAST-SQUARES ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; ROSACEAE; SPECTROSCOPY, NEAR-INFRARED; SUPPORT VECTOR MACHINE; TIME FACTORS;

EID: 84992699531     PISSN: 16182642     EISSN: 16182650     Source Type: Journal    
DOI: 10.1007/s00216-016-9944-7     Document Type: Article

References (44)

1. Sawai R, Kuroda K, Shibata T, Gomyou R, Osawa K, Shimizu K. Anti-influenza virus activity of Chaenomeles sinensis. J Ethnopharmacol. 2008;118(1):108–12.
2. YingBo Y, Xia L, Qi Y, ZhiJun W, LianNa S. Studies on chemical constituents of Chaenomeles speciosa (Sweet) Nakai (II). Dier junyi daxue xuebao. 2009;30(10):1195–8.
3. Committee CP. Chinese pharmacopoeia, vol. 276. Beijing: China Medical Science; 2010.
4. Sheikha E, Farag A, Condur A, Métayer I, Le Nguyen DD, Loiseau G, et al. Determination of fruit origin by using 26S rDNA fingerprinting of yeast communities by PCR–DGGE: preliminary application to Physalis fruits from Egypt. Yeast. 2009;26(10):567–73.
5. El Sheikha AF, Montet D. Determination of fruit origin by using 28S rDNA fingerprinting of fungi communities by PCR-DGGE: an application to Physalis fruits from Egypt, Uganda and Colombia. Fruits. 2011;66(02):79–89.
6. Licciardello F, Muratore G, Avola C, Tomaselli F, Maccarone E, editors. Geographical origin assessment of orange juices by comparison of free aminoacids distribution. II International Symposium on Citrus Biotechnology 892; 2009.
7. Nojima S, Linn Jr C, Roelofs W. Identification of host fruit volatiles from flowering dogwood (Cornus florida) attractive to dogwood-origin Rhagoletis pomonella flies. J Chem Ecol. 2003;29(10):2347–57.
8. Huang Y, Cavinato AG, Tang J, Swanson BG, Lin M, Rasco BA. Characterization of sol–gel transitions of food hydrocolloids with near infra-red spectroscopy. LWT-Food Sci Technol. 2007;40(6):1018–26.
9. Gómez AH, He Y, Pereira AG. Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. J Food Eng. 2006;77(2):313–9.
10. Fu X, Ying Y, Liu Y, Lu H. Detection of pear firmness using near infrared diffuse reflectance spectroscopy. Guang Pu Xue Yu Guang Pu Fen Xi. 2006;26(6):1038–41.
11. Liu Y, Chen X, Ouyang A. Nondestructive determination of pear internal quality indices by visible and near-infrared spectrometry. LWT-Food Sci Technol. 2008;41(9):1720–5.
12. Cavaco AM, Pinto P, Antunes MD, da Silva JM, Guerra R. ‘Rocha’ pear firmness predicted by a Vis/NIR segmented model. Postharvest Biol Technol. 2009;51(3):311–9.
13. Bobelyn E, Serban A-S, Nicu M, Lammertyn J, Nicolai BM, Saeys W. Postharvest quality of apple predicted by NIR-spectroscopy: study of the effect of biological variability on spectra and model performance. Postharvest Biol Technol. 2010;55(3):133–43.
14. Nicolaï BM, Lötze E, Peirs A, Scheerlinck N, Theron KI. Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biol Technol. 2006;40(1):1–6.
15. Carlomagno G, Capozzo L, Attolico G, Distante A. Non-destructive grading of peaches by near-infrared spectrometry. Infrared Phys Technol. 2004;46(1):23–9.
16. X-p F, Y-b Y, Zhou Y, Xie L-j, Xu H-r. Application of NIR spectroscopy for firmness evaluation of peaches. J Zhejiang Univ Sci B. 2008;9(7):552–7.
17. Golding J, Satyan S, Liebenberg C, Walsh K, McGlasson W, editors. Application of portable NIR for measuring soluble solids concentrations in peaches. VI International Peach Symposium 713; 2005.
18. Moghimi A, Aghkhani MH, Sazgarnia A, Sarmad M. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Biosyst Eng. 2010;106(3):295–302.
19. Carlini P, Massantini R, Mencarelli F. Vis-NIR measurement of soluble solids in cherry and apricot by PLS regression and wavelength selection. J Agric Food Chem. 2000;48(11):5236–42.
20. Huang M, Tang J, Yang B, Zhu Q. Classification of maize seeds of different years based on hyperspectral imaging and model updating. Comput Electron Agric. 2016;122:139–45.
21. He Y, Li X, Deng X. Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model. J Food Eng. 2007;79(4):1238–42.
22. Khanmohammadi M, Karami F, Mir-Marqués A, Garmarudi AB, Garrigues S, de la Guardia M. Classification of persimmon fruit origin by near infrared spectrometry and least squares-support vector machines. J Food Eng. 2014;142:17–22.
23. Camps C, Christen D. Non-destructive assessment of apricot fruit quality by portable visible-near infrared spectroscopy. LWT-Food Sci Technol. 2009;42(6):1125–31.
24. Team RC. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013. ISBN 3-900051-07-0; 2014.
25. Mevik BH, Segtnan VH, Næs T. Ensemble methods and partial least squares regression. J Chemom. 2004;18(11):498–507.
26. Reid LM, Woodcock T, O’Donnell CP, Kelly JD, Downey G. Differentiation of apple juice samples on the basis of heat treatment and variety using chemometric analysis of MIR and NIR data. Food Res Int. 2005;38(10):1109–15.
27. Luan Q-F, Li Y-J, Jiang J-M, Yue H-F, Diao S-F. Discrimination of Pinus taeda × P. caribaea var. hondurensis between its allele-species and hybrids using near infrared spectroscopy. J Near Infrared Spectrosc. 2013;21(3):203–11.
28. Wu W, Mallet Y, Walczak B, Penninckx W, Massart D, Heuerding S, et al. Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Anal Chim Acta. 1996;329(3):257–65.
29. Xing J, Bravo C, Moshou D, Ramon H, De Baerdemaeker J. Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy. Comput Electron Agric. 2006;52(1):11–20.
30. Indahl UG, Sahni NS, Kirkhus B, Næs T. Multivariate strategies for classification based on NIR-spectra—with application to mayonnaise. Chemometr Intell Lab Syst. 1999;49(1):19–31.
31. Belousov A, Verzakov S, Von Frese J. Applicational aspects of support vector machines. J Chemom. 2002;16(8–10):482–9.
32. Sain SR. The nature of statistical learning theory. Technometrics. 1996;38(4):409.
33. Thissen U, Pepers M, Üstün B, Melssen W, Buydens L. Comparing support vector machines to PLS for spectral regression applications. Chemometr Intell Lab Syst. 2004;73(2):169–79.
34. Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293–300.
35. Chauchard F, Cogdill R, Roussel S, Roger J, Bellon-Maurel V. 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 Syst. 2004;71(2):141–50.
36. Sun T, Lin H, Xu H, Ying Y. 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. 2009;51(1):86–90.
37. Xudong S, Hailiang Z, Yande L. Nondestructive assessment of quality of Nanfeng mandarin fruit by a portable near infrared spectroscopy. IJABE. 2009;2(1):65–71.
38. Liu F, He Y, Wang L, Sun G. Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration. Food Bioproc Tech. 2011;4(8):1331–40.
39. Wu W, Massart DL. Artificial neural networks in classification of NIR spectral data: selection of the input. Chemometr Intell Lab Syst. 1996;35(1):127–35.
40. Jaillais B, Pinto R, Barros AS, Rutledge DN. Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water. Vib Spectrosc. 2005;39(1):50–8.
41. Sinelli N, Cerretani L, Egidio VD, Bendini A, Casiraghi E. Application of near (NIR) infrared and mid (MIR) infrared spectroscopy as a rapid tool to classify extra virgin olive oil on the basis of fruity attribute intensity. Food Res Int. 2010;43(1):369–75.
42. Christy AA, Kasemsumran S, Du Y, OZAKI Y. The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics. Anal Sci. 2004;20(6):935–40.
43. Cozzolino D, Cowey G, Lattey K, Godden P, Cynkar W, Dambergs R, et al. Relationship between wine scores and visible–near-infrared spectra of Australian red wines. Anal Bioanal Chem. 2008;391(3):975–81.
44. Teena M, Manickavasagan A, Ravikanth L, Jayas D. Near infrared (NIR) hyperspectral imaging to classify fungal infected date fruits. J Stored Prod Res. 2014;59:306–13.