Browsing by Author "Teye Ernest"

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    Application of portable near infrared spectroscopy for classifying and quantifying cocoa bean quality parameters
    (2021-03-11) Elliot K. Anyidoho; Teye Ernest; Agbemafle Robert; Amuah Charles L. Y.; Boadu Vida Gyimah
    Fermentation duration (FmD), fermentation index (FI), pH, and moisture content(Mc) are vital quality attributes of cocoa beans. In this study, portable near infrared spectroscopy (NIRS) and multivariate analyses were used for rapid determination of FmD, FI, pH, and Mc of cocoa beans. The samples were scanned in 900- to 1,700-nmwavelength, and the spectral data were pretreated independently with first deriva-tives (FD) and second derivatives (SD), multiplicative scatter correction (MSC), mean centering (MC), and standard normal variate (SNV), while linear discriminant analysis(LDA), support vector machine (SVM), and partial least squares regression (PLS-R)were used to build the prediction models for FmD, FI, pH, and Mc. MSC plus SVM gave an accurate classification of 100%. For predicting FI, pH, and Mc, the PLS-R model gave coefficient of correlation of 0.87, 0.82, and 0.89, respectively. The results showed that portable NIRS could be employed for cocoa bean examination. Novelty impact statement: Fermentation is the single most essential postharvest operation that influences cocoa beans quality parameters including moisture content ,fermentation index (FI) and pH. Unlike stationary laboratory based wet chemistry technique or table top NIR spectroscopy, this study revealed that the relatively inex-pensive portable NIR spectroscopy could provide very fast (within 30 s) results in the routine onsite evaluation of cocoa beans moisture content, fermentation index and pH on farmers field in Sub-Saharan Africa. In particular, the study outcome highlights the potential application of portable NIR spectroscopy based on machine learning for efficient classification of fermentation duration and quantification of moisture content & pH of cocoa beans in real-time usage.
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    Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method
    (2024-08-15) Boadu Vida Gyimah; Teye Ernest; Lamptey Francis Padi; Amuah Charles Lloyd Yeboah; Sam-Amoah L.K.
    African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.