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Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.
Phytochem Anal. 2009 Sep-Oct; 20(5):402-7.PA

Abstract

INTRODUCTION

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product.

OBJECTIVE

To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches.

METHODOLOGY

Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample.

RESULTS

Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds.

CONCLUSIONS

SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Authors+Show Affiliations

Università degli Studi del Piemonte Orientale, Dipartimento di Scienze Chimiche, Alimentari, Farmaceutiche e Farmacologiche, Novara, Italy.No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

19609881

Citation

Rinaldi, Maurizio, et al. "Pattern Recognition and Genetic Algorithms for Discrimination of Orange Juices and Reduction of Significant Components From Headspace Solid-phase Microextraction." Phytochemical Analysis : PCA, vol. 20, no. 5, 2009, pp. 402-7.
Rinaldi M, Gindro R, Barbeni M, et al. Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction. Phytochem Anal. 2009;20(5):402-7.
Rinaldi, M., Gindro, R., Barbeni, M., & Allegrone, G. (2009). Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction. Phytochemical Analysis : PCA, 20(5), 402-7. https://doi.org/10.1002/pca.1140
Rinaldi M, et al. Pattern Recognition and Genetic Algorithms for Discrimination of Orange Juices and Reduction of Significant Components From Headspace Solid-phase Microextraction. Phytochem Anal. 2009 Sep-Oct;20(5):402-7. PubMed PMID: 19609881.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction. AU - Rinaldi,Maurizio, AU - Gindro,Roberto, AU - Barbeni,Massimo, AU - Allegrone,Gianna, PY - 2009/7/18/entrez PY - 2009/7/18/pubmed PY - 2010/3/3/medline SP - 402 EP - 7 JF - Phytochemical analysis : PCA JO - Phytochem Anal VL - 20 IS - 5 N2 - INTRODUCTION: Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. OBJECTIVE: To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. METHODOLOGY: Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. RESULTS: Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. CONCLUSIONS: SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices. SN - 1099-1565 UR - https://www.unboundmedicine.com/medline/citation/19609881/Pattern_recognition_and_genetic_algorithms_for_discrimination_of_orange_juices_and_reduction_of_significant_components_from_headspace_solid_phase_microextraction_ L2 - https://doi.org/10.1002/pca.1140 DB - PRIME DP - Unbound Medicine ER -