Analytical method to detect adulteration of ground roasted coffee

Analysis of volatile and nonvolatile compounds in decaffeinated and regular coffee prepared under various roasting conditions

This study investigated the effect of various roasting conditions on regular and decaffeinated green beans. Regular and decaffeinated green beans from Guatemala, Brazil, Ethiopia, and Colombia were prepared under light, medium, and dark roasting conditions. Analysis of the decaffeination-induced changes in nonvolatile compounds revealed that decaffeinated green coffee beans had significantly lower concentrations of trigonelline (25%) and total carbohydrates (16%) but a higher chlorogenic acid content (10-14%) than regular green coffee beans (bothp < 0.05). Flavor differences between regular and decaffeinated coffee were investigated by analysis of the volatile and nonvolatile compounds in roasted coffee beans. From the odor impact ratio values, 3-ethyl-2,5-dimethyl pyrazine, 5-methyl furfural, and guaiacol were primarily responsible for coffee flavor. 3-Ethyl-2,5-dimethyl pyrazine had 58% lower concentration in decaffeinated coffee than in regular coffee. This study is valuable in providing the chemical composition of decaffeinated coffee and way to improve the quality of decaffeinated coffee.

Improvement of Robusta coffee aroma with l-leucine powder

BACKGROUND

l-Leucine powder (LP) was added to green Robusta coffee beans in order to reduce the difference in flavour between Robusta and Arabica coffee. l-Leucine was selected as an additive based on the Maillard reaction. The pre-treatment method conducted in this study was a short soaking (M1) or spraying procedure (M2), then LP was added at varying levels up to 3% (w/w, 30 g kg^-1 ). All samples were roasted (240 °C for 15 min) and extracted using an espresso machine. Volatile compounds were analysed by solid-phase microextraction-gas chromatography-mass selective detection.

RESULTS

Thirty volatile compounds (six pyrroles, eight pyrazines, three phenols, nine furans, two ketones, two aldehydes) were analysed. In 15 coffee samples, the levels of total volatile compounds (based on peak area ratios) ranged from 8.9 (M1-1) to 15. Non-treated Robusta had higher levels of bitter aroma compounds than Arabica coffee, and Robusta coffee had lower levels of bitter aroma compounds when pre-treated with LP. The sum of bitter volatiles (phenols, pyrroles, pyrazines) was lowest in M1-5 (3% LP), M2-1 (1% LP; both dried at 50 °C for 15 min) and M2-7 (3% LP, dried at 70 °C for 15 min) compared with non-treated Robusta (P < 0.05).

CONCLUSION

From the results of this study it can be shown that pre-treatment with LP can improve the flavour of Robusta. © 2023 Society of Chemical Industry.

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.

Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee

Roasted coffee has been the target of increasingly complex adulterations. Sensitive, non-destructive, rapid and multicomponent techniques for their detection are sought after. This work proposes the detection of several common adulterants (corn, barley, soybean, rice, coffee husks and robusta coffee) in roasted ground arabica coffee (from different geographic regions), combining near-infrared (NIR) spectroscopy and chemometrics (Principal Component Analysis—PCA). Adulterated samples were composed of one to six adulterants, ranging from 0.25 to 80% (w/w). The results showed that NIR spectroscopy was able to discriminate pure arabica coffee samples from adulterated ones (for all the concentrations tested), including robusta coffees or coffee husks, and independently of being single or multiple adulterations. The identification of the adulterant in the sample was only feasible for single or double adulterations and in concentrations ≥10%. NIR spectroscopy also showed potential for the geographical discrimination of arabica coffees (South and Central America).

Quantification of Chlorogenic Acid and Vanillin from Coffee Peel Extract and its Effect on α-Amylase Activity, Immunoregulation, Mitochondrial Oxidative Stress, and Tumor Suppressor Gene Expression Levels in H2O2-Induced Human Mesenchymal Stem Cells

Background: Polyphenols and flavonoid-rich foods help in arresting reactive oxygen species development and protecting DNA from oxidative damage. Coffee peel (CP) preparations are consumed as beverages, and their total polyphenol or flavonoid content and their effect on oxidative stress-induced human mesenchymal stem cells (hMSCs) are poorly understood. Method: We prepared hot water extracts of CP (CPE) and quantified the amount of total polyphenol and flavonoid using HPLC analysis. In addition, CPE have been studied for their α-amylase inhibitory effect and beneficial effects in oxidative stress-induced hMSCs. Results: The obtained results show that the availability of chlorogenic acid, vanillin, and salicylic acid levels in CPE is more favorable for enhancing cell growth, nuclear integrity, and mitochondrial efficiency which is confirmed by propidium iodide staining and JC-1 staining. CPE treatment to hMSCs for 48 h reduced oxidative stress by decreasing mRNA expression levels of LPO and NOX-4 and in increasing antioxidant CYP1A, GSH, GSK-3β, and GPX mRNA expressions. Decreased pro-inflammatory (TNF-α, NF-κβ, IL-1β, TLR-4) and increased tumor suppressor genes (except Bcl-2) such as Cdkn2A, p53 expressions have been observed. Conclusions: The availability of CGA in CPs effectively reduced mitochondrial oxidative stress, reduced pro-inflammatory cytokines, and increased tumor suppressor genes.

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

BACKGROUND

The adulteration of milk by hazardous chemicals like surfactants has recently increased. It conceals the quality of the product to gain profit. As milk and milk-based products are consumed by many people, novel analytical procedures are needed to detect these adulterants. This study focused on Fourier-transform infrared (FTIR) spectroscopy equipped with an attenuated total reflection (ATR) accessory, and near-infrared (NIR) spectroscopy for the determination of milk-surfactant adulteration using a genetic algorithm (GA) coupled with multivariate methods. The model surfactant was sodium dodecyl sulfate (SDS), and its concentration varied from 1.94-19.4 gkg^-1 in adulterated samples.

RESULTS

Prominent peaks in the spectral range of 5500-6400 cm^-1 , 1160-1260 cm^-1 and 1049-1080 cm^-1 may correspond to the sulfonate group in SDS. A genetic algorithm could significantly reduce the number of variables to almost one third by selecting the specific wavenumber region. Principal component analysis (PCA) for ATR and NIR data indicated separate clusters of samples in terms of the concentration level of SDS (P ≤ 0.05). Partial least squares regression (PLSR) was used to determine the maximum R² value for ATR and NIR data for calibration, cross-validation and prediction, which were 0.980, 0.972, 0.980, and 0.970, 0.937, and 0.956 respectively. The results showed apparent differences between unadulterated and adulterated samples using partial least squares-discriminant analysis (PLS-DA), which was validated by the permutation test.

CONCLUSION

The results clearly show the successful application of the proposed methods with multivariate analysis in the selection of variables, classification, clustering, and identification of the adulterant in amounts as low as 1.94 gkg^-1 in milk. © 2020 Society of Chemical Industry.

Authenticity Assessment and Fraud Quantitation of Coffee Adulterated with Chicory, Barley, and Flours by Untargeted HPLC-UV-FLD Fingerprinting and Chemometrics

Coffee, one of the most popular drinks around the world, is also one of the beverages most susceptible of being adulterated. Untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) fingerprinting strategies in combination with chemometrics were employed for the authenticity assessment and fraud quantitation of adulterated coffees involving three different and common adulterants: chicory, barley, and flours. The methodologies were applied after a solid–liquid extraction procedure with a methanol:water 50:50 (v/v) solution as extracting solvent. Chromatographic fingerprints were obtained using a Kinetex^® C18 reversed-phase column under gradient elution conditions using 0.1% formic acid aqueous solution and methanol as mobile phase components. The obtained coffee and adulterants extract HPLC-UV-FLD fingerprints were evaluated by partial least squares regression-discriminants analysis (PLS-DA) resulting to be excellent chemical descriptors for sample discrimination. One hundred percent classification rates for both PLS-DA calibration and prediction models were obtained. In addition, Arabica and Robusta coffee samples were adulterated with chicory, barley, and flours, and the obtained HPLC-UV-FLD fingerprints subjected to partial least squares (PLS) regression, demonstrating the feasibility of the proposed methodologies to assess coffee authenticity and to quantify adulteration levels (down to 15%), showing both calibration and prediction errors below 1.3% and 2.4%, respectively.

HPLC-Based Chemometric Analysis for Coffee Adulteration

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.