Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

Abstract

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.

Keywords: Chemometrics; Coffee bean; Geographical differentiation; NIR spectroscopy; Partial least squares-discriminant analysis; Robusta.

Fig. 1

Spectra of robusta coffee types: (i) bean, (ii) roasted, (iii) powder and mean spectra for coffee types: (iv) bean, (v) roasted, (vi) powder from five African countries.

Fig. 2

Raw mean spectra for coffee types: (a) raw, (b) roasted, (c) roasted powder.

Fig. 3

PCA score plot of the first three PCs of coffee beans from five African countries preprocessed- i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV.

Fig. 4

PCA score plot of the first three roasted coffee beans from five African countries preprocessed - i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV.

Fig. 5

PCA score plot of the first three roasted coffee powder from five African countries preprocessed: i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV.

Fig. 6

Score plot of PLS-DA model built from spectra of coffee beans from five African countries: i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV for robusta.

Fig. 7

Score plot of PLS-DA model built from spectra of roasted coffee from five African countries: i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV for robusta.

Fig. 8

Score plot of PLS-DA model built from spectra of roasted coffee powder from five African countries: i) raw ii) FD iii) SD iv) MC v) MSC vi) SNV for robusta.

Fig. 9

PC Loadings with three principal components for i) bean, ii) roasted, iii) roasted coffee powder.