English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Arabica coffee is considered to be of better quality than Robusta coffee. It has superior taste and aroma, better than Robusta coffee. To develop an authentication system for Arabica coffee, it is highly necessary to discriminate between pure Arabica coffee and Arabica adulterated with Robusta coffee. Ground roasted coffee samples are most difficult to discriminate from each other: visual inspection by the naked eye or even machine vision methods becomes very problematic. For this reason, we here propose a relatively new analytical method based on UV–visible spectroscopy for discrimination the pure and adulterated Arabica ground roasted coffee. In this study, 100 samples were used as samples with different degrees of adulteration (0%–60% of Robusta concentration in an Arabica–Robusta coffee blend). Spectral data of samples were acquired using a UV–visible spectrometer in the range of 190–1100 nm (Genesys 10s, Thermo Scientific, USA). Partial least square discriminant analysis (PLS-DA) was applied to discriminate between the pure and adulterated Arabica coffee based on UV–visible spectra data. Several pre-processing spectra were also tested to determine which one provides an appropriate discrimination model. The PLS-DA model has coefficient of correlation 0.89 (R2 = 0.79) with low Root mean square error of calibration (RMSEC) 0.226. The full-cross validation resulted in Q2 = 0.74 and low Root mean squared error of cross-validation (RMSECV) 0.254. Using this PLS-DA model, a total rate of correct classification of 97.5% was obtained in the prediction set. In conclusion, UV–visible spectroscopy in tandem with PLS-DA is a promising analytical method for differentiating between pure and adulterated Arabica ground roasted coffee.

Partial least squares with discriminant analysis and UV– visible spectroscopy for qualitative evaluation of Arabica and Robusta coffee in Lampung