Detection of Adulteration in Coffee Products Using FTIR Spectroscopy and Multivariate Analysis - Sonny Widiarto; La Zakaria; Safitri Haurotul Jamalat; Dian Septiani Pratama; Irwan Saputra (Jurnal Kimia Sains dan Aplikasi, 2026)
Food fraud, particularly in coffee, is increasingly prevalent worldwide and poses significant risks to consumers. This study aimed to analyze the purity of coffee products in the Bandar Lampung market and evaluate the validity of Fourier Transform Infrared (FTIR) spectroscopy and chemometric methods. The research methodology includes preparing reference standards, coffee sampling from various sellers in Bandar Lampung, establishing calibration and validation sets, performing FTIR analysis, and data processing using Principal Component Analysis (PCA) and Partial Least Squares (PLS) with Minitab software. A total of five coffee samples collected from the Bandar Lampung market were analyzed, and this work should be considered a preliminary investigation due to the limited sample size. PCA was employed as an exploratory tool to classify coffee types and identify potential adulterants. The PCA results indicated that samples A, B, and C clustered closely with the robusta coffee standard, while samples D and E showed slight deviation, exhibiting spectral characteristics associated with corn powder. These findings are consistent with the regional context of Lampung, one of the major coffee-producing regions in Indonesia, where robusta is predominantly cultivated. In addition, PCA suggests that corn is the dominant adulterant compared to rice. Based on these observations, the quantitative analysis was performed using a PLS model developed from robusta coffee and corn adulterant mixtures. The model demonstrated high apparent internal performance under the present experimental conditions (R2 > 0.999), with relatively low calibration and prediction errors. However, these results should be interpreted with caution due to the limited number of samples and calibration design, which may increase the risk of overfitting. The estimates are therefore specific to the studied mixtures and experimental conditions. Predictions from the PLS model indicate that robusta coffee content in the sampled products ranges from approximately 71.7% to 98.2%, reflecting variability in the composition of the analyzed samples. The developed model is intended for quantifying corn adulteration in coffee samples and demonstrates the potential of FTIR-chemometric approaches for rapid coffee authentication.