Skip to main content
Log in

The Use of Qualitative Analysis in Food Research and Technology: Considerations and Reflections from an Applied Point of View

  • Published:
Food Analytical Methods Aims and scope Submit manuscript

Abstract

The application of qualitative analytical techniques is usually associated with the analysis of data sets targeting issues related with the presence or absence of a particular class or type of sample, pattern recognition and cluster analysis. In food sciences, these techniques are generally used to deal with the authenticity, classification, discrimination, fraud and origin of foods. In recent years, qualitative analysis became more relevant in both food research and industry applications, addressing fraud and traceability concerns in the food value chain. In this overview, some of the most common classification methods and techniques used in food sciences will be briefly described, with emphasis on the validation, interpretation and reporting of the results obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Adams, M.J. (1995) Chemometrics in analytical spectroscopy. In: NW Barnett (eds) RSC Spectroscopy Monographs. The Royal Society of Chemistry. UK, p 216

  • Badertscher M, Pretsch E (2006) Bad results from good data. Trends Anal Chem 25:1131–1138

    Article  CAS  Google Scholar 

  • Beebe KR, Peel RJ, Seasholtz MB (1998) Chemometrics a practical guide. John Wiley & Sons Ltd, New York, USA

    Google Scholar 

  • Berrueta LA, Alonso-Salces RM, Herberger K (2007) Supervised pattern recognition in food analysis. J Chromatogr A 1158:196–214

    Article  CAS  Google Scholar 

  • Bevilacqua M, Necatelli R, Bucci R, Magri AD, Magri SL, Marini F (2014) Chemometric classification techniques as tool for solving problems in analytical chemistry. J AOAC Int 97:19–27

    Article  CAS  Google Scholar 

  • Brereton RG (2000) Introduction to multivariate calibration in analytical chemistry. Analyst 125:2125–2154

    Article  CAS  Google Scholar 

  • Brereton RG (2006) Consequences of sample size, variable selection, and model validation and optimization, for predicting classification ability from analytical data. Trends Anal Chem 25:1103–1111

    Article  CAS  Google Scholar 

  • Brereton RG (2008) Applied chemometrics for scientist. John Wiley & Sons Ltd, Chichester, UK

    Google Scholar 

  • Brereton RG (2009) Chemometrics for pattern recognition. John Wiley & Sons Ltd, Chichester, UK

    Book  Google Scholar 

  • Brereton RG (2015) Pattern recognition in chemometrics. Chemom Intell Lab Syst 149(2015):90–96

    Article  CAS  Google Scholar 

  • Bro, R., & Smilde, A.K (2014). Principal component analysis: a tutorial review. Anal Methods, 6, 2812–2831.

  • Cozzolino D (2012) Recent trends on the use of infrared spectroscopy to trace and authenticate natural and agricultural food products. Appl Spectrosc Rev 47:518–530

    Article  CAS  Google Scholar 

  • Cozzolino D (2014) An overview of the use of infrared spectroscopy and chemometrics in authenticity and traceability of cereals. Food Res Int 60:262–265

    Article  CAS  Google Scholar 

  • Cozzolino D, Cynkar WU, Dambergs RG, Shah N, Smith P (2009) Multivariate methods in grape and wine analysis. International Journal of Wine Research 1:123–130

    Article  CAS  Google Scholar 

  • Ellison SLR, Fearn T (2005) Characterising the performance of qualitative analytical methods: statistics and terminology. Trends Anal Chem 24:468–476

    Article  CAS  Google Scholar 

  • Engel J, Gerretzen J, Szymanska E, Jansen JJ, Downey G, Blanchet L, Buydens LMC (2013) Breaking with trends in pre-processing. Trends Anal Chem 50:96–106

    Article  CAS  Google Scholar 

  • Esbensen KH (2002) Multivariate data analysis in practice. CAMO Process AS, Oslo, Norway

    Google Scholar 

  • Esslinger S, Riedl J, Fauhl-Hassek C (2014) Potential and limitations of non-targeted fingerprinting for authentication of food in official control. Food Res Int 60:189–20

  • Gishen M, Dambergs RG, Cozzolino D (2005) Grape and wine analysis—enhancing the power of spectroscopy with chemometrics. A review of some applications in the Australian wine industry. Aust J Grape Wine Res 11:296–305

    Article  CAS  Google Scholar 

  • Gonzalez GA (2007) Use and misuse of supervised pattern recognition methods for interpreting compositional data. J Chromatogr A 1158:215–225

    Article  CAS  Google Scholar 

  • Granato D, Calado VMA, Jarvis B (2014) Observations on the use of statistical methods in food science and technology. Food Res Int 55:137–159

    Article  Google Scholar 

  • Hawkins DM (2004) The problem of overfitting. Journal of Chemical Informatics Computational. Science 44:1–12

    CAS  Google Scholar 

  • Khakimov B, Bak S, Engelsen SB (2014) High-throughput cereal metabolomics: current analytical technologies, challenges and perspective. J Cereal Sci 59:393–418

    Article  CAS  Google Scholar 

  • Khakimov B, Gürdeniz G, Engelsen SB (2015) Trends in the application of chemometrics to foodomics studies. Acta Aliment 44:4–31

    Article  CAS  Google Scholar 

  • Kumar N, Bansal A, Sarma GS, Rawal RK (2014) Chemometrics tools used in analytical chemistry: an overview. Talanta 123:186–199

    Article  CAS  Google Scholar 

  • Moller SF, von Frese J, Bro R (2005) Robust methods for multivariate data analysis. J Chemom 19:549–563

    Article  Google Scholar 

  • Naes T, Isaksson T, Fearn T, Davies T (2002) A user-friendly guide to multivariate calibration and classification. NIR Publications, Chichester, UK, 420 p

  • Otto, M. (1999) Chemometrics: statistics and computer application in analytical chemistry. Wiley-VCH, 314

  • Pulido A, Ruisanchez I, Boque R, Rius FX (2003) Uncertainty of results in routine quality analysis. Trends Anal Chem 22:647–654

    Article  CAS  Google Scholar 

  • Skov T, Honore AH, Jensen HM, Naes T, Engelsen SB (2014) Chemometriocs in foodomics: handling data structures from multiple analytical platforms. Trends Anal Chem 60:71–79

    Article  CAS  Google Scholar 

  • Smyth H, Cozzolino D (2013) Instrumental methods (spectroscopy, electronic nose and tongue) as tools to predict taste and aroma in beverages: advantages and limitations. Chem Rev 113:1429–1440

    Article  CAS  Google Scholar 

  • Szymanska E, Gerretzen J, Engel J, Geurts B, Blanchet L, Buydens LMC (2015) Chemometrics and qualitative analysis have a vibrant relationship. Trends Anal Chem 69:34–51

    Article  CAS  Google Scholar 

  • Westad F, Marini F (2015) Validation of chemometric models: a tutorial. Anal Chim Acta 893:14–23

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The support of Central Queensland University is acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to D. Cozzolino.

Ethics declarations

Conflict of Interest

Mr. Neville Doyle declares that he has no conflict of interest. Dr. Dave Swain declares that he has no conflict of interest. Dr. J.J. Roberts declares that he has no conflict of interest. Daniel Cozzolino declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human or animal subjects.

Informed Consent

(In case humans are involved) Informed consent was obtained from all individual participants included in the study.

(If not applicable on the study) Not applicable.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Doyle, N., Swain, D., Roberts, J.J. et al. The Use of Qualitative Analysis in Food Research and Technology: Considerations and Reflections from an Applied Point of View. Food Anal. Methods 10, 964–969 (2017). https://doi.org/10.1007/s12161-016-0654-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12161-016-0654-8

Keywords

Navigation