Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

Document Type : Research article

Authors

1 Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Food Safety Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

3 Department of Toxicology and Pharmacology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

4 Wageningen Food Safety Research, Wageningen University and Research, Akkermaalsbos 2, 6708 WB, Wageningen, The Netherlands.

5 Food Quality and Design Group, Wageningen University and Research, Bornse Weilanden 9, 6708 WG, Wageningen, The Netherlands.

6 Department of Medicinal Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

7 Department of Pharmaceutics, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

8 Faroogh Life Sciences Research Laboratory, Tehran, Iran.

9 Department of Clinical Pharmacy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Abstract

The aim of this study is to investigate the novel application of a ‎handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900–1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew’s correlation ‎coefficient ‎value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew’s correlation ‎coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.

Graphical Abstract

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration

Keywords


  • Guyon F, Auberger P, Gaillard L, Loublanches C, Viateau M, Sabathié N, Salagoïty MH and Médina B. 13C/12C isotope ratios of organic acids, glucose and fructose determined by HPLC-co-IRMS for lemon juices authenticity. Food Chem. (2014) 146: 36-40.
  • Huang Y, Rasco B, Cavinato A. Application of infrared technology to juice analysis. Infrared Spectroscopy for Food Quality Analysis and Control: Academic Press Oxford, United Kingdom (2009) 150-53.
  • Cautela D, Laratta B, Santelli F, Trifirò A, Servillo L, Castaldo D. Estimating bergamot juice adulteration of lemon juice by high-performance liquid chromatography (HPLC) analysis of flavanone glycosides. J Agric Food Chem. (2008) 56: 5407-14.
  • Saeidi I, Hadjmohammadi MR, Peyrovi M, Iranshahi M, Barfi B, Babaei AB and Mohammad Dust M. HPLC determination of hesperidin, diosmin and eriocitrin in Iranian lime juice using polyamide as an adsorbent for solid phase extraction. J Pharm Biomed Anal. (2011) 56: 419-22.
  • Kvasnička F, Voldřich M, Pyš P, Vinš I. Determination of Isocitric acid in citrus juice—a comparison of HPLC, enzyme set and capillary isotachophoresis methods. J Food Compost Anal. (2002) 15: 685-91.
  • Abad-García B, Garmón-Lobato S, Sánchez-Ilárduya MB, Berrueta LA, Gallo B, Vicente F and Alonso-Salces RM. Polyphenolic contents in Citrus fruit juices: Eur Food Res Technol. (2014) 238:803-18.
  • Wang Z, Jablonski JE. Targeted and non-targeted detection of lemon juice adulteration by LC-MS and chemometrics. Food Addit Contam Part A. (2016) 33: 560-73.
  • 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: 1331-40.
  • Li J, Huang W, Zhao C, Zhang B. A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J Food Eng. (2013) 116: 324-32.
  • Shafiee S, Minaei S. Combined data mining/NIR spectroscopy for purity assessment of lime juice. Infrared Phys Technol. (2018) 91: 193-9.
  • Mossoba MM, Azizian H, Fardin-Kia AR, Karunathilaka SR, Kramer JK. First application of newly developed FT-NIR spectroscopic methodology to predict authenticity of extra virgin olive oil retail products in the USA. Lipids. (2017) 52: 443-55.
  • Chen H, Tan C, Lin Z, Wu T. Detection of melamine adulteration in milk by near-infrared spectroscopy and one-class partial least squares. Spectrochim Acta A Mol Biomol Spectrosc. (2017) 173: 832-6.
  • Liu N, Parra HA, Pustjens A, Hettinga K, Mongondry P, van Ruth SM. Evaluation of portable near-infrared spectroscopy for organic milk authentication. Talanta. (2018) 184: 128-35.
  • Teye E, Amuah CL, McGrath T, Elliott C. Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics. Spectrochim Acta A Mol Biomol Spectrosc. (2019) 217: 147-154.
  • Kartakoullis A, Comaposada J, Cruz-Carrión A, Serra X, Gou P. Feasibility study of smartphone-based Near Infrared Spectroscopy (NIRS) for salted minced meat composition diagnostics at different temperatures. Food Chem. (2019) 278: 314-21.
  • Downey G. Authentication of food and food ingredients by near infrared spectroscopy. J Near Infrared Spectrosc. (1996) 4: 47-61.
  • Sørensen KM, Khakimov B, Engelsen SB. The use of rapid spectroscopic screening methods to detect adulteration of food raw materials and ingredients. Curr Opin Food Sci. (2016) 10: 45-51.
  • Fluvià Sabio S. NIR techniques and chemometrics data analysis applied to food adulteration detection. Universitat Politècnica de Catalunya (2015) 78-80.
  • Marini F, ‎ Lanteri S and Armanino C. Chemometrics in food chemistry. Springer, Berlin, Heidelberg (2013) 91-143.
  • Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML and Goodacre R. A tutorial review: Metabolomics and partial least squares-discriminant analysis–a marriage of convenience or a shotgun wedding. Anal Chim Acta. (2015) 879: 10-23.
  • Deng Z, Zhu X, Cheng D, Zong M, Zhang S. Efficient kNN classification algorithm for big data. Neurocomputing. (2016) 195: 143-8.
  • AIJN (Association of the Industry of Juices and Nectars of the European Union).Code of practice for evaluation of fruit and vegetable juices 6.26. Reference. guideline for lime juice. (2016): 1-4.
  • Tellspec–Beam Your Health Up–TellSpec. Available from: URL: http://tellspec.com/en/.
  • Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. (2010) 2: 433-59.
  • Barker M, Rayens W. Partial least squares for discrimination. J Chemom. (2003) 17: 166-73.
  • Peterson LE. K-nearest neighbor. Scholarpedia J. (2009) 4: 1883.
  • Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst. (2001) 58: 109-30.
  • Tharwat A. Classification assessment methods. Prog Adv Comput Intell Eng. (2018) 16: 56-65.
  • Flight L, Julious SA. The disagreeable behaviour of the kappa statistic. Pharm Stat. (2015) 14: 74-8.
  • Wong T-T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. (2015) 48: 2839-46.
  • Kumar N, Bansal A, Sarma G, Rawal RK. Chemometrics tools used in analytical chemistry: An overview. Talanta. (2014) 123: 186-99.
  • Curcio JA, Petty CC. The near infrared absorption spectrum of liquid water. JOSA. (1951) 41: 302-4.
  • Barbin DF, Felicio ALdSM, Sun D-W, Nixdorf SL, Hirooka EY. Application of infrared spectral techniques on quality and compositional attributes of coffee: An overview. Food Res Int. (2014) 61: 23-32.
  • Rinnan Å, Van Den Berg F, Engelsen SB. Review of the most common pre-processing techniques for near-infrared spectra. Trends Analyt Chem. (2009) 28: 1201-22.
  • Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. (2004) 44: 1-12.
  • Sokolova M, Japkowicz N, Szpakowicz S, editors. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Australasian joint conference on artificial intelligence; 2006; Springer, Berlin, Germany.
  • Akosa J. Predictive accuracy: A misleading performance measure for highly imbalanced data. Proceedings of the SAS Global Forum. (2017): 2-5.
  • Alewijn M, van der Voet H, van Ruth S. Validation of multivariate classification methods using analytical fingerprints–concept and case study on organic feed for laying hens. J Food Compost Anal. (2016) 51: 15-23.