HPLC-Based Chemometric Analysis for Coffee Adulteration

Abstract

<jats:p>Coffee is one of the top ten most adulterated foods. Coffee adulterations are mainly performed by mixing other low-value materials into coffee beans after roasting and grinding, such as spent coffee grounds, maize, soybeans and other grain products. The detection of adulterated coffee by high performance liquid chromatography (HPLC) is recognized as a targeted analytical method, which carbohydrates and other phenolic compounds are usually used as markers. However, the accurate qualitation and quantitation of HPLC analyses are time consuming. This study developed a chemometric analysis or called non-targeted analysis for coffee adulteration. The HPLC chromatograms were obtained by direct injection of liquid coffee into HPLC without sample preparation and the identification of target analytes. The distinction between coffee and adulterated coffee was achieved by statistical method. The HPLC-based chemometric provided more characteristic information (separated compounds) compared to photospectroscopy chemometric which only provide information of functional groups. In this study, green Arabica coffee beans, soybeans and green mung beans were roasted in industrial coffee bean roaster and then ground. Spent coffee ground was dried. Coffee and adulterants were mixed at different ratio before conducting HPLC analysis. Principal component analysis (PCA) toward HPLC data (retention time and peak intensity) was able to separate coffee from adulterated coffee. The detection limit of this method was 5%. Two models were built based on PCA data as well. The first model was used to differentiate coffee sample from adulterated coffee. The second model was designed to identify the specific adulterants mixed in the adulterated coffee. Various parameters such as sensitivity (SE), specificity (SP), reliability rate (RLR), positive likelihood (+LR) and negative likelihood (−LR) were applied to evaluate the performances of the designed models. The results showed that PCA-based models were able to discriminate pure coffee from adulterated sample (coffee beans adulterated with 5%–60% of soybeans, green mung beans or spent coffee grounds). The SE, SP, RLR, +LR and −LR for the first model were 0.875, 0.938, 0.813, 14.1 and 0.133, respectively. In the second model, it can correctly distinguish the adulterated coffee from the pure coffee. However, it had only about a 30% chance to correctly determine the specific adulterant out of three designed adulterants mixed into coffee. The SE, RLR and −LR were 0.333, 0.333 and 0.667, respectively, for the second model. Therefore, HPLC-based chemometric analysis was able to detect coffee adulteration. It was very reliable on the discrimination of coffee from adulterated coffee. However, it may need more work to tell discern which kind adulterant in the adulterated coffee.</jats:p>

Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes

“…Practically, authentication approaches are always based on either targeting specific markers of adulterants, or through the non-targeted fingerprinting approach [ 98 ]. Different metabolomics-based platforms were applied, including direct NMR spectroscopy, infrared/Raman spectroscopy, and UV-Vis versus hyphenated chromatographic technique coupled to mass spectrometry (i.e., GC/MS and LC/MS) [ 99 ], as highlighted in the next subsections.…”

“…LC-based quality control of coffee products has been comprehensively reviewed previously, and it is considered a targeted analytical platform using carbohydrates and other phenolic compounds as markers [ 99 ]. Moreover, flavoromics-based untargeted multidimensional preparative LC/MS analysis, with the aid of 1D- and 2D-NMR, could reveal different metabolites that positively and negatively affect coffee quality based on cup score.…”

Penggunaan UV-Vis Spektroskopi dan Kemometrika untuk Uji Keaslian Kopi Codot Lampung

“…Dari penelusuran pustaka, persentase campuran yang digunakan pada publikasi sebelumnya adalah bervariasi. Misalnya pada riset penggunaan metode high performance liquid chromatography (HPLC) dan kemometrika untuk kuantifikasi pencampuran kopi dengan ampas kopi digunakan persentase campuran sebesar 560% (Cheah & Fang 2020). FT-MIR spectroscopy dan kemometrika digunakan untuk identifikasi dan kuantifikasi pencampuran kopi.…”

“…Hasil klasifikasi kopi Codot murni dan campuran dinyatakan dalam bentuk matriks konfusi, seperti yang disajikan di Tabel 2 (Cheah & Fang 2020) Berdasarkan matriks konfusi tersebut dapat dihitung empat parameter berikut, yaitu sensitivitas (SEN), spesifisitas (SPEC), presisi (PREC), dan akurasi (ACC) dengan rentang nilai antara 0100% yang dihitung dengan persamaan 1 sampai 4 sebagai berikut (Luna et al 2016):…”

Application of genetic algorithm and multivariate methods for the detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy

“…This is a threat to public health. This adulteration is not only limited to milk but can also be observed in other foods like olive oil, honey, saffron, orange juice, coffee, apple juice, and nuts 2–11 . Milk has been reported to be adulterated by inexpensive, inferior quality, and harmful chemical substances to increase the volume, mask the quality parameters (e.g., protein and fat content), and prolong the product's shelf life 11–13 …”