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Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy.
J Sci Food Agric. 2017 Mar; 97(4):1260-1266.JS

Abstract

BACKGROUND

Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner.

RESULTS

PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg-1 and 0.358 µg kg-1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%.

CONCLUSION

The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Taradolsirithitikul P
Department of Microbiology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.
Sirisomboon P
Curriculum of Agricultural Engineering, Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520, Thailand.
Dachoupakan Sirisomboon C
Department of Microbiology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.

MeSH

CoffeaCoffeeFood ContaminationHumansModels, StatisticalOchratoxinsReproducibility of ResultsSeedsSpectroscopy, Fourier Transform InfraredSpectroscopy, Near-Infrared

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27324609

Citation

Taradolsirithitikul, Panchita, et al. "Qualitative and Quantitative Analysis of Ochratoxin a Contamination in Green Coffee Beans Using Fourier Transform Near Infrared Spectroscopy." Journal of the Science of Food and Agriculture, vol. 97, no. 4, 2017, pp. 1260-1266.
Taradolsirithitikul P, Sirisomboon P, Dachoupakan Sirisomboon C. Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy. J Sci Food Agric. 2017;97(4):1260-1266.
Taradolsirithitikul, P., Sirisomboon, P., & Dachoupakan Sirisomboon, C. (2017). Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy. Journal of the Science of Food and Agriculture, 97(4), 1260-1266. https://doi.org/10.1002/jsfa.7859
Taradolsirithitikul P, Sirisomboon P, Dachoupakan Sirisomboon C. Qualitative and Quantitative Analysis of Ochratoxin a Contamination in Green Coffee Beans Using Fourier Transform Near Infrared Spectroscopy. J Sci Food Agric. 2017;97(4):1260-1266. PubMed PMID: 27324609.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Qualitative and quantitative analysis of ochratoxin A contamination in green coffee beans using Fourier transform near infrared spectroscopy. AU - Taradolsirithitikul,Panchita, AU - Sirisomboon,Panmanas, AU - Dachoupakan Sirisomboon,Cheewanun, Y1 - 2016/07/15/ PY - 2015/11/03/received PY - 2016/04/27/revised PY - 2016/06/13/accepted PY - 2016/6/22/pubmed PY - 2017/7/1/medline PY - 2016/6/22/entrez KW - Fourier transform near infrared spectroscopy KW - green coffee beans KW - ochratoxin A KW - partial least square discriminant analysis (PLS-DA) KW - partial least square regression (PLSR) SP - 1260 EP - 1266 JF - Journal of the science of food and agriculture JO - J Sci Food Agric VL - 97 IS - 4 N2 - BACKGROUND: Ochratoxin A (OTA) contamination is highly prevalent in a variety of agricultural products including the commercially important coffee bean. As such, rapid and accurate detection methods are considered necessary for the identification of OTA in green coffee beans. The goal of this research was to apply Fourier transform near infrared spectroscopy to detect and classify OTA contamination in green coffee beans in both a quantitative and qualitative manner. RESULTS: PLSR models were generated using pretreated spectroscopic data to predict the OTA concentration. The best model displayed a correlation coefficient (r) of 0.814, a standard error of prediction (SEP and bias of 1.965 µg kg-1 and 0.358 µg kg-1 , respectively. Additionally, a PLS-DA model was also generated, displaying a classification accuracy of 96.83% for a non-OTA contaminated model and 80.95% for an OTA contaminated model, with an overall classification accuracy of 88.89%. CONCLUSION: The results demonstrate that the developed model could be used for detecting OTA contamination in green coffee beans in either a quantitative or qualitative manner. © 2016 Society of Chemical Industry. SN - 1097-0010 UR - https://www.unboundmedicine.com/medline/citation/27324609/Qualitative_and_quantitative_analysis_of_ochratoxin_A_contamination_in_green_coffee_beans_using_Fourier_transform_near_infrared_spectroscopy_ DB - PRIME DP - Unbound Medicine ER -