Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy.
Foods. 2017 May 19; 6(5)F

Abstract

Moisture content (MC) is one of the most important quality parameters of green coffee beans. Therefore, its fast and reliable measurement is necessary. This study evaluated the feasibility of near infrared (NIR) spectroscopy and chemometrics for rapid and non-destructive prediction of MC in intact green coffee beans of both Coffeaarabica (Arabica) and Coffeacanephora (Robusta) species. Diffuse reflectance (log 1/R) spectra of intact beans were acquired using a bench top Fourier transform NIR instrument. MC was determined gravimetrically according to The International Organization for Standardization (ISO) 6673. Samples were split into subsets for calibration (n = 64) and independent validation (n = 44). A three-component partial least squares regression (PLSR) model using raw NIR spectra yielded a root mean square error of prediction (RMSEP) of 0.80% MC; a four component PLSR model using scatter corrected spectra yielded a RMSEP of 0.57% MC. A simplified PLS model using seven selected wavelengths (1155, 1212, 1340, 1409, 1724, 1908, and 2249 nm) yielded a similar accuracy (RMSEP: 0.77% MC) which opens the possibility of creating cheaper NIR instruments. In conclusion, NIR diffuse reflectance spectroscopy appears to be suitable for rapid and reliable MC prediction in intact green coffee; no separate model for Arabica and Robusta species is needed.

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Authors+Show Affiliations

Adnan A

Division Quality of Plant Products, Department of Crop Sciences, University of Goettingen, Carl-Sprengel-Weg 1, 37075 Goettingen, Germany. adnan.adnan@stud.uni-goettingen.de.

Hörsten DV

Institute for Application Techniques in Plant Protection, Julius Kühn Institute, Messeweg 11/12, 38140 Braunschweig, Germany. epawelz@gwdg.de.

Pawelzik E

Division Quality of Plant Products, Department of Crop Sciences, University of Goettingen, Carl-Sprengel-Weg 1, 37075 Goettingen, Germany. dieter.von-hoersten@jki.bund.de.

Mörlein AD

Department of Animal Sciences, University of Goettingen, Albrecht-Thaer-Weg 3, D-37075 Goettingen, Germany. daniel.moerlein@agr.uni-goettingen.de.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28534842

Citation

Adnan, Adnan, et al. "Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy." Foods (Basel, Switzerland), vol. 6, no. 5, 2017.

Adnan A, Hörsten DV, Pawelzik E, et al. Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy. Foods. 2017;6(5).

Adnan, A., Hörsten, D. V., Pawelzik, E., & Mörlein, A. D. (2017). Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy. Foods (Basel, Switzerland), 6(5). https://doi.org/10.3390/foods6050038

Adnan A, et al. Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy. Foods. 2017 May 19;6(5) PubMed PMID: 28534842.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy. AU - Adnan,Adnan, AU - Hörsten,Dieter von, AU - Pawelzik,Elke, AU - Mörlein,And Daniel, Y1 - 2017/05/19/ PY - 2017/03/10/received PY - 2017/05/04/revised PY - 2017/05/17/accepted PY - 2017/5/24/entrez PY - 2017/5/24/pubmed PY - 2017/5/24/medline KW - Coffea arabica (Arabica) KW - Coffea canephora (Robusta) KW - chemometrics KW - infrared spectroscopy KW - quality KW - rapid methods JF - Foods (Basel, Switzerland) JO - Foods VL - 6 IS - 5 N2 - Moisture content (MC) is one of the most important quality parameters of green coffee beans. Therefore, its fast and reliable measurement is necessary. This study evaluated the feasibility of near infrared (NIR) spectroscopy and chemometrics for rapid and non-destructive prediction of MC in intact green coffee beans of both Coffeaarabica (Arabica) and Coffeacanephora (Robusta) species. Diffuse reflectance (log 1/R) spectra of intact beans were acquired using a bench top Fourier transform NIR instrument. MC was determined gravimetrically according to The International Organization for Standardization (ISO) 6673. Samples were split into subsets for calibration (n = 64) and independent validation (n = 44). A three-component partial least squares regression (PLSR) model using raw NIR spectra yielded a root mean square error of prediction (RMSEP) of 0.80% MC; a four component PLSR model using scatter corrected spectra yielded a RMSEP of 0.57% MC. A simplified PLS model using seven selected wavelengths (1155, 1212, 1340, 1409, 1724, 1908, and 2249 nm) yielded a similar accuracy (RMSEP: 0.77% MC) which opens the possibility of creating cheaper NIR instruments. In conclusion, NIR diffuse reflectance spectroscopy appears to be suitable for rapid and reliable MC prediction in intact green coffee; no separate model for Arabica and Robusta species is needed. SN - 2304-8158 UR - https://www.unboundmedicine.com/medline/citation/28534842/Rapid_Prediction_of_Moisture_Content_in_Intact_Green_Coffee_Beans_Using_Near_Infrared_Spectroscopy_ L2 - https://www.mdpi.com/resolver?pii=foods6050038 DB - PRIME DP - Unbound Medicine ER -

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Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy.Foods. 2017 May 19; 6(5)F