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Pappoe JA, Mongson O, Amuah CLY, Opoku-Ansah J, Adueming POW, Boateng R, Eghan MJ, Sackey SS, Anyidoho EK, Huzortey AA, Anderson B, Vowotor MK, Teye E. Classification of Organic and Conventional Cocoa Beans Using Laser-Induced Fluorescence Spectroscopy Combined with Chemometric Techniques. J Fluoresc 2023:10.1007/s10895-023-03499-3. [PMID: 37971609 DOI: 10.1007/s10895-023-03499-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
The craving for organic cocoa beans has resulted in fraudulent practices such as mislabeling, adulteration, all known as food fraud, prompting the international cocoa market to call for the authenticity of organic cocoa beans before export. In this study, we proposed robust models using laser-induced fluorescence (LIF) and chemometric techniques for rapid classification of cocoa beans as either organic or conventional. The LIF measurements were conducted on cocoa beans harvested from organic and conventional farms. From the results, conventional cocoa beans exhibited a higher fluorescence intensity compared to organic ones. In addition, a general peak wavelength shift was observed when the cocoa beans were excited using a 445 nm laser source. These results highlight distinct characteristics that can be used to differentiate between organic and conventional cocoa beans. Identical compounds were found in the fluorescence spectra of both the organic and conventional ones. With preprocessed fluorescence spectra data and utilizing principal component analysis, classification models such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Network (NN) and Random Forest (RF) models were employed. LDA and NN models yielded 100.0% classification accuracy for both training and validation sets, while 99.0% classification accuracy was achieved in the training and validation sets using SVM and RF models. The results demonstrate that employing a combination of LIF and either LDA or NN can be a reliable and efficient technique to classify authentic cocoa beans as either organic or conventional. This technique can play a vital role in maintaining integrity and preventing fraudulent practices in the cocoa bean supply chain.
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Affiliation(s)
- Justice Allotey Pappoe
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Space Environment, Institute of Basic and Applied Sciences, Egypt-Japan University of Science and Technology, Alexandria, Egypt
| | - Olivia Mongson
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Charles Lloyd Yeboah Amuah
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana.
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana.
| | - Jerry Opoku-Ansah
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Peter Osei-Wusu Adueming
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Rabbi Boateng
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Moses Jojo Eghan
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Samuel Sonko Sackey
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | | | - Andrew Atiogbe Huzortey
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Benjamin Anderson
- Laser and Fibre Optics Centre, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Michael Kwame Vowotor
- Department of Physics, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
| | - Ernest Teye
- Department of Agricultural Engineering, School of Agriculture, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana
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Sentellas S, Saurina J. Authentication of Cocoa Products Based on Profiling and Fingerprinting Approaches: Assessment of Geographical, Varietal, Agricultural and Processing Features. Foods 2023; 12:3120. [PMID: 37628119 PMCID: PMC10453789 DOI: 10.3390/foods12163120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
Cocoa and its derivative products, especially chocolate, are highly appreciated by consumers for their exceptional organoleptic qualities, thus being often considered delicacies. They are also regarded as superfoods due to their nutritional and health properties. Cocoa is susceptible to adulteration to obtain illicit economic benefits, so strategies capable of authenticating its attributes are needed. Features such as cocoa variety, origin, fair trade, and organic production are increasingly important in our society, so they need to be guaranteed. Most of the methods dealing with food authentication rely on profiling and fingerprinting approaches. The compositional profiles of natural components -such as polyphenols, biogenic amines, amino acids, volatile organic compounds, and fatty acids- are the source of information to address these issues. As for fingerprinting, analytical techniques, such as chromatography, infrared, Raman, and mass spectrometry, generate rich fingerprints containing dozens of features to be used for discrimination purposes. In the two cases, the data generated are complex, so chemometric methods are usually applied to extract the underlying information. In this review, we present the state of the art of cocoa and chocolate authentication, highlighting the pros and cons of the different approaches. Besides, the relevance of the proposed methods in quality control and the novel trends for sample analysis are also discussed.
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Affiliation(s)
- Sonia Sentellas
- Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain;
- Research Institute in Food Nutrition and Food Safety, Universitat de Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), 08921 Santa Coloma de Gramenet, Spain
- Serra Húnter Fellow Programme, Generalitat de Catalunya, Via Laietana 2, 08003 Barcelona, Spain
| | - Javier Saurina
- Department of Chemical Engineering and Analytical Chemistry, Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain;
- Research Institute in Food Nutrition and Food Safety, Universitat de Barcelona, Av. Prat de la Riba 171, Edifici Recerca (Gaudí), 08921 Santa Coloma de Gramenet, Spain
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Quality Evaluation of Fair-Trade Cocoa Beans from Different Origins Using Portable Near-Infrared Spectroscopy (NIRS). Foods 2022; 12:foods12010004. [PMID: 36613219 PMCID: PMC9818779 DOI: 10.3390/foods12010004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
Determining cocoa bean quality is crucial for many players in the international supply chain. However, actual methods rely on a cut test protocol, which is limited by its subjective nature, or on time-consuming, expensive and destructive wet-chemistry laboratory procedures. In this context, the application of near infrared (NIR) spectroscopy, particularly with the recent developments of portable NIR spectrometers, may represent a valuable solution for providing a cocoa beans' quality profile, in a rapid, non-destructive, and reliable way. Monitored parameters in this work were dry matter (DM), ash, shell, fat, protein, total polyphenols, fermentation index (FI), titratable acidity (TA) and pH. Different chemometric analyses were performed on the spectral data and calibration models were developed using modified partial least squares regression. Prediction equations were validated using a fivefold cross-validation and a comparison between the different prediction performances for the portable and benchtop NIR spectrometers was provided. The NIRS benchtop instrument provided better performance of quantification considering the whole than the portable device, showing excellent prediction capability in protein and DM quantification. On the other hand, the NIRS portable device, although showing lower but valuable performance of prediction, can represent an appealing alternative to benchtop instruments for food business operators, being applicable in the field.
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