Simple analytical method using ultraviolet spectral dataset and chemometrics for the authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications

Diding Suhandy; Meinilwita Yulia; Agus Arip Munawar; Kusumiyati Kusumiyati

doi:10.1016/j.dib.2023.109820

Simple analytical method using ultraviolet spectral dataset and chemometrics for the authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications

Data Brief. 2023 Nov 19:51:109820. doi: 10.1016/j.dib.2023.109820. eCollection 2023 Dec.

Authors

Diding Suhandy¹, Meinilwita Yulia², Agus Arip Munawar³, Kusumiyati Kusumiyati⁴

Affiliations

¹ Spectroscopy Research Group (SRG), Laboratory of Bioprocess and Postharvest Engineering, Department of Agricultural Engineering, Faculty of Agriculture, The University of Lampung, Jl. Prof. Dr. Soemantri Brojonegoro No.1, Bandar Lampung 35145, Indonesia.
² Department of Agricultural Technology, Lampung State Polytechnic, Jl. Soekarno Hatta No. 10, Rajabasa Bandar Lampung 35141, Indonesia.
³ Department of Agricultural Engineering, Syiah Kuala University, Banda Aceh, Indonesia.
⁴ Department of Agronomy, Universitas Padjadjaran, Bandung, Indonesia.

Abstract

The possible application of a simple analytical method based on a UV (ultraviolet) spectral dataset coupled with SIMCA (soft independent modeling of class analogy) for authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications (GIs) was demonstrated. Three types of Indonesian specialty ground roasted coffee were used: GIs arabica coffee from Gayo Aceh (96 samples), GIs liberica coffee from Meranti-Riau (119 samples), and GIs robusta coffee from Lampung (150 samples) with 1 g weight of each sample. All samples were extracted using hot distilled water and 3 mL aqueous filtered samples were pipetted into a 10 mm quartz cell. Original UV spectral datasets were recorded in the range of 190-399 nm. The pre-processed spectral dataset was generated using three simultaneous different preprocessing techniques: moving average smoothing with 11 segments, standard normal variate (SNV), and Savitzky-Golay (SG) first derivative with window size and polynomial order value of 11 and 2. The supervised classification based on the SIMCA method was applied for preprocessed selected spectral data (250-399 nm). The PCA data showed that GIs coffee with different botanical and geographical indications can be well separated. The SIMCA classification was accepted with 100 % of correct classification (100 % CC). This dataset demonstrated the potential use of UV spectroscopy with chemometrics to perform simple and affordable authentication of Indonesian specialty ground roasted coffee with different botanical and geographical indications (GIs).

Keywords: Food authentication; Geographical indications; PCA; SIMCA; Specialty coffee; Spectral data; UV spectroscopy supervised classification.