Developments in near infrared spectroscopy. Prediction of chocolate quality from near-infrared spectroscopic measurements of raw cocoa beans

1992 ◽  
Vol 29 (5) ◽  
pp. 198 ◽  
Author(s):  
Anthony M. C. Davies
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cocoa Beans ◽  
Spectroscopic Measurements ◽  
Infrared Spectroscopic

Quality Control of Commercial Cocoa Beans (Theobroma cacao L.) by Near-infrared Spectroscopy

2018 ◽  
Vol 11 (5) ◽  
pp. 1510-1517 ◽  
Author(s):  
Juliana C. Hashimoto ◽  
Jéssica C. Lima ◽  
Renata M. S. Celeghini ◽  
Alessandra B. Nogueira ◽  
Priscilla Efraim ◽  
...  
Keyword(s):  
Quality Control ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Theobroma Cacao ◽  
Near Infrared ◽  
Cocoa Beans ◽  
Theobroma Cacao L

scholarly journals Agricultural products quality determination by means of near infrared spectroscopy

2022 ◽  
Vol 951 (1) ◽  
pp. 012112
Author(s):  
A A Munawar ◽  
Z Zulfahrizal ◽  
R Hayati ◽  
Syahrul
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Agricultural Products ◽  
Cocoa Bean ◽  
Cocoa Beans ◽  
Non Destructive

Abstract Cocoa is one of main agricultural products cultivated in many tropical countries and processed onto several derivative products. To determine cocoa beans qualities, laboratory procedures based on solvent extractions were mainly used, however most of them are destructive and may cause environmental pollutions. The main purpose of this present study is to employ near infrared spectroscopy (NIRS) for rapid and non-destructive assessment of cocoa beans in form of fat content. Near infrared spectral data of cocoa bean samples were measured as diffuse reflectance in wavelength range from 1000 to 2500 nm. Reference fat contents were measured using standard laboratory methods. Prediction models were developed using principal component regression with raw and baseline corrected spectra data. The results showed that fat contents of cocoa beans can be predicted and determined with maximum correlation coefficient (r) of 0.89 and ratio prediction to deviation (RPD) index of 2.87 for raw spectra and r of 0.91, RPD of 3.18 for baseline spectra correction. It may conclude that NIRS was feasible to be applied as a rapid and non-destructive method for cocoa bean quality assessment.

Prediction of chocolate quality from near-infrared spectroscopic measurements of the raw cocoa beans

1991 ◽  
Vol 2 (2-3) ◽  
pp. 161-172 ◽  
Author(s):  
A.M.C. Davies ◽  
J.G. Franklin ◽  
A. Grant ◽  
N.M. Griffiths ◽  
R. Shepherd ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cocoa Beans ◽  
Spectroscopic Measurements ◽  
Infrared Spectroscopic

Rapid measurement of total polyphenols content in cocoa beans by data fusion of NIR spectroscopy and electronic tongue

2014 ◽  
Vol 6 (14) ◽  
pp. 5008-5015 ◽  
Author(s):  
Xingyi Huang ◽  
Ernest Teye ◽  
Livingstone K. Sam-Amoah ◽  
Fangkai Han ◽  
Liya Yao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Data Fusion ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Electronic Tongue ◽  
Nir Spectroscopy ◽  
Cocoa Beans ◽  
Total Polyphenols ◽  
Rapid Measurement ◽  
Novel Approach

This work measures the total polyphenols content in cocoa beans by using a novel approach of integrating near infrared spectroscopy and electronic tongue, 110 samples of cocoa beans were analysed.

scholarly journals Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses

2018 ◽  
Vol 55 (7) ◽  
pp. 2457-2466 ◽  
Author(s):  
Douglas Fernandes Barbin ◽  
Leonardo Fonseca Maciel ◽  
Carlos Henrique Vidigal Bazoni ◽  
Margareth da Silva Ribeiro ◽  
Rosemary Duarte Sales Carvalho ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Statistical Analyses ◽  
Multivariate Statistical Analyses ◽  
Cocoa Beans ◽  
Multivariate Statistical ◽  
Compositional Characterization
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