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Food Analytical Methods

, Volume 10, Issue 6, pp 1965–1971 | Cite as

SPA Combined with Swarm Intelligence Optimization Algorithms for Wavelength Variable Selection to Rapidly Discriminate the Adulteration of Apple Juice

  • Ying Li
  • Yajing Guo
  • Chang Liu
  • Wu Wang
  • Pingfan Rao
  • Caili Fu
  • Shaoyun Wang
Article

Abstract

The application of wavelength variable selection before partial least squares (PLS) regression to rapidly discriminate the adulteration of apple juice by Fourier transform near-infrared (FT-NIR) was investigated in this study. Successive projections algorithm (SPA) combined with four swarm intelligence optimization algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), group search optimizer (GSO), and firefly algorithm (FA), was applied to extract effective wavelength variables. The results demonstrated that the variable number of SPA-PSO-PLS models was validly improved with a wavelength variable of four. The accuracy of model was satisfactory with the coefficients of determination of prediction (R 2 p  = 0.9986) and good root mean square errors of prediction (RMSEP = 0.0628). The results suggested that SPA combined with swarm intelligence optimization algorithms for wavelength variable selection could rapidly and efficiently discriminate the adulteration of apple juice.

Keywords

FT-NIR spectroscopy Successive projections algorithm Swarm intelligence optimization algorithms Wavelength selection 

Notes

Acknowledgments

The authors gratefully acknowledged the support of the Science and Technology Program of Fujian, China (Grant No. 2016N0017), the Scientific Research Foundation for Returned Scholars, Ministry of Education of China (Grant No. LXKQ201301), Fujian Spark Program (Grant No. 2016S0042), and Fuzhou Science and Technology Project (Grant No. 2015-G-70).

Compliance with Ethical Standards

Conflict of Interest

Ying Li declares that she has no conflict of interest. Yajing Guo declares that she has no conflict of interest. Chang Liu declares that she has no conflict of interest. Wu Wang declares that he has no conflict of interest. Pingfan Rao declares that he has no conflict of interest. Caili Fu declares that he has no conflict of interest. Shaoyun Wang declares that she has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

References

  1. Adyanthaya I, Kwon YI, Apostolidis E, Shetty K (2010) Health benefits of apple phenolics from postharvest stages for potential type 2 diabetes management using in vitro models. J Food Biochem 34:31–49CrossRefGoogle Scholar
  2. Barnes R, Dhanoa M, Lister S (2004) Letter: correction to the description of standard normal variate (SNV) and de-trend (DT) ransformations in practical spectroscopy with applications in food and everage analysis—2nd edition. J Near Infrared Spectrosc 1:185–186CrossRefGoogle Scholar
  3. Boyer J, Liu RH (2004) Apple phytochemicals and their health benefits. Nutr J 3:1CrossRefGoogle Scholar
  4. Chen J, Arnold MA, Small GW (2004) Comparison of combination and first overtone spectral regions for near-infrared calibration models for glucose and other biomolecules in aqueous solutions. Anal Chem 76:5405–5413CrossRefGoogle Scholar
  5. Cheng JH, Sun DW, Pu HB (2016) Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle. Food Chem 197:855–863CrossRefGoogle Scholar
  6. Clark CJ (2016) Fast determination by Fourier-transform infrared spectroscopy of sugaracid composition of citrus juices for determination of industry maturity standards. New Zeal J Crop Hort 44:69–82CrossRefGoogle Scholar
  7. Cozzolino D, Kwiatkowski M, Parker M, Cynkar W, Dambergs R, Gishen M, Herderich M (2004) Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Anal Chim Acta 513:73–80CrossRefGoogle Scholar
  8. Cozzolino D, Smyth HE, Lattey KA, Cynkar W, Janik L, Dambergs RG, Francis IL, Gishen M (2005) Relationship between sensory analysis and near infrared spectroscopy in Australian Riesling and Chardonnay wines. Anal Chim Acta 539:341–348CrossRefGoogle Scholar
  9. Dambergs RG, Kambouris A, Francis IL, Gishen M (2002) Rapid analysis of methanol in grape-derived distillation products using near-infrared transmission spectroscopy. J Agr Food Chem 50:3079–3084CrossRefGoogle Scholar
  10. Deák K, Szigedi T, Pék Z, Baranowski P, Helyes L (2015) Carotenoid determination in tomato juice using near infrared spectroscopy. Int Agrophys 29:275–282CrossRefGoogle Scholar
  11. Dhanoa M, Lister S, Sanderson R, Barnes R (1994) The link between multiplicative scatter correction (MSC) and standard normal variate (SNV) transformations of NIR spectra. J Near Infrared Spectrosc 2:43–47CrossRefGoogle Scholar
  12. Fan SX, Zhang BH, Li JB, Huang WQ, Wang CP (2016) Effect of spectrum measurement position variation on the robustness of NIR spectroscopy models for soluble solids content of apple. Biosyst Eng 143:9–19CrossRefGoogle Scholar
  13. Gestal M, Gómez-Carracedo M, Andrade J, Dorado J, Fernández E, Prada D, Pazos A (2004) Classification of apple beverages using artificial neural networks with previous variable selection. Anal Chim Acta 524:225–234CrossRefGoogle Scholar
  14. Goodarzi M, Coelho LDS (2014) Firefly as a novel swarm intelligence variable selection method in spectroscopy. Anal Chim Acta 852:20–27CrossRefGoogle Scholar
  15. Granato D, Koot A, Schnitzler E, Ruth SM (2015) Authentication of geographical origin and crop system of grape juices by phenolic compounds and antioxidant activity using chemometrics. J Food Sci 80:584–593CrossRefGoogle Scholar
  16. Guo HS, Chen JM, Pan T, Wang JH, Cao G (2014) Vis-NIR wavelength selection for non-destructive discriminant analysis of breed screening of transgenic sugarcane. Anal Methods 6:8810–8816CrossRefGoogle Scholar
  17. Han SH, Zhang WW, Li X, Li PY, Liu JX (2016) Determination of three alcohols in Chinese Dukang Base liquor by FT-NIR spectroscopy. Food Anal Method 9:2194–2199CrossRefGoogle Scholar
  18. Isaksson T, Næs T (1988) The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR spectroscopy. Appl Spectrosc 42:1273–1284CrossRefGoogle Scholar
  19. Jiang H, Zhang H, Chen QS, Mei CL, Liu GH (2015) Identification of solid state fermentation degree with FT-NIR spectroscopy: comparison of wavelength variable selection methods of CARS and SCARS. Spectrochim Acta A 149:1–7CrossRefGoogle Scholar
  20. Kucheryavskiy S, Lomborg CJ (2015) Monitoring of whey quality with NIR spectroscopy—a feasibility study. Food Chem 176:271–277CrossRefGoogle Scholar
  21. Li HD, Liang YZ, Xu QS, Cao DS (2009) Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal Chim Acta 648:77–84CrossRefGoogle Scholar
  22. Miyawaki O, Gunathilake M, Omote C, Koyanagi T, Sasaki T, Take H, Matsuda A, Ishisaki K, Miwa S, Kitano S (2016) Progressive freeze-concentration of apple juice and its application to produce a new type apple wine. J Food Eng 171:153–158CrossRefGoogle Scholar
  23. Pedro AM, Ferreira MM (2005) Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Anal Chem 77:2505–2511CrossRefGoogle Scholar
  24. Pei M, Huang XJ (2016) Determination of trace phenolic acids in fruit juice samples using multiple monolithic fiber solid-phase microextraction coupled with high-performance liquid chromatography. Anal Methods 8:3831–3838CrossRefGoogle Scholar
  25. Pereira AFC, Pontes MJC, Neto FFG, Santos SRB, Galvao RKH, Araujo MCU (2008) NIR spectrometric determination of quality parameters in vegetable oils using iPLS and variable selection. Food Res Int 41:341–348CrossRefGoogle Scholar
  26. Rady AM, Guyer DE (2015) Evaluation of sugar content in potatoes using NIR reflectance and wavelength selection techniques. Postharvest Biol Tec 103:17–26CrossRefGoogle Scholar
  27. Soares SFC, Gomes AA, Araujo MCU, Filho ARG, Galvão RKH (2013) The successive projections algorithm. Trac-Trend Anal Chem 42:84–98CrossRefGoogle Scholar
  28. Spinelli F, Dutra S, Carnieli G, Leonardelli S, Drehmer A, Vanderlinde R (2016) Detection of addition of apple juice in purple grape juice. Food Control 69:1–4CrossRefGoogle Scholar
  29. Staggs J (2005) Savitzky–Golay smoothing and numerical differentiation of cone calorimeter mass data. Fire Safety J 40:493–505CrossRefGoogle Scholar
  30. Tang G, Huang Y, Tian KD, Song XZ, Yan H, Hu J, Xiong YM, Min S (2014) A new spectral variable selection pattern using competitive adaptive reweighted sampling combined with successive projections algorithm. Analyst 139:4894–4902CrossRefGoogle Scholar
  31. Tong PJ, Du YP, Zheng KY, Wu T, Wang JJ (2015) Improvement of NIR model by fractional order Savitzky-Golay derivation (FOSGD) coupled with wavelength selection. Chemometr Intell Lab 143:40–48CrossRefGoogle Scholar
  32. Williams AB, Ayejuyo OO, Ogunyale AF (2009) Trace metal levels in fruit juices and carbonated beverages in Nigeria. Environ Monit Assess 156:303–306CrossRefGoogle Scholar
  33. Wu ZZ, Xu EB, Wang F, Long J, Jiao XM, Xu AQ, Jin ZY (2015) Rapid determination of process variables of Chinese rice wine using FT-NIR spectroscopy and efficient wavelengths selection methods. Food Anal Method 8:1456–1467CrossRefGoogle Scholar
  34. Xie LJ, Ye XQ, Liu DH, Ying YB (2009) Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chem 114:1135–1140CrossRefGoogle Scholar
  35. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Com 2:78–84CrossRefGoogle Scholar
  36. Ye MQ, Gao ZP, Li Z, Yuan YH, Yue TL (2016) Rapid detection of volatile compounds in apple wines using FT-NIR spectroscopy. Food Chem 190:701–708CrossRefGoogle Scholar
  37. Zheng KY, Li QQ, Wang JJ, Geng JP, Cao P, Sui T, Wang X, Du YP (2012) Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra. Chemometr Intell Lab 112:48–54CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.College of Biological Sciences and EngineeringFuzhou UniversityFuzhouChina
  2. 2.College of Electrical Engineering and AutomationFuzhou UniversityFuzhouChina
  3. 3.School of Food Science and BiotechnologyZhejiang Gongshang UniversityHangzhouChina

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