Three centuries on the science of coffee authenticity control

Fast authentication of Indonesian ground-roasted Arabica coffee adulterated with roasted soybean by portable LED-based fluorescence spectroscopy and chemometrics analysis

The authentic Arabica coffee renders it vulnerable to fraud and adulteration. Arabica, Robusta, and Liberica green beans appear different. After roasting and grinding, authentic coffee seems identical to inferior varieties. A proper analytical technique for ground-roasted coffee authentication is needed. This study uses portable LED-based fluorescence spectroscopy and chemometrics to identify and quantify roasted soybeans in ground-roasted Arabica. Supervised classification algorithms PLS-DA, LDA, PCA-LDA, and SVMC are compared. Three easy-to-use regression approaches were employed to predict soybean adulteration in adulterated Arabica coffee: PLSR, PCR, and MLR. Classification accuracy was 100 % for the linear kernel-SVMC model, outperforming PLS-DA, LDA, and PCA-LDA. PLSR had the lowest LOD of 4.96 % (w/w) and the best regression model for soybean adulteration (RMSEC of 2.01 % (w/w), RMSECV of 2.10 % (w/w), R2c of 0.99, and R2cv of 0.99). The RPD and RER were 11.39 and 31.07, beyond practical applicability. The proposed method is simpler, non-destructive, and cost-effective authentication method.

Identifying the origin of Yemeni green coffee beans using near infrared spectroscopy: a promising tool for traceability and sustainability

Yemeni smallholder coffee farmers face several challenges, including the ongoing civil conflict, limited rainfall levels for irrigation, and a lack of post-harvest processing infrastructure. Decades of political instability have affected the quality, accessibility, and reputation of Yemeni coffee beans. Despite these challenges, Yemeni coffee is highly valued for its unique flavor profile and is considered one of the most valuable coffees in the world. Due to its exclusive nature and perceived value, it is also a prime target for food fraud and adulteration. This is the first study to identify the potential of Near Infrared Spectroscopy and chemometrics-more specifically, the discriminant analysis (PCA-LDA)-as a promising, fast, and cost-effective tool for the traceability of Yemeni coffee and sustainability of the Yemeni coffee sector. The NIR spectral signatures of whole green coffee beans from Yemeni regions (n = 124; Al Mahwit, Dhamar, Ibb, Sa'dah, and Sana'a) and other origins (n = 97) were discriminated with accuracy, sensitivity, and specificity ≥ 98% using PCA-LDA models. These results show that the chemical composition of green coffee and other factors captured on the spectral signatures can influence the discrimination of the geographical origin, a crucial component of coffee valuation in the international markets.

Spectrophotometry and chromatography analyses combined with chemometrics tools to differentiate green coffee beans into special or traditional

Green coffee is the hulled coffee bean, rich in chemical compounds indicative of quality before roasting, making the classification special or traditional. This work aimed to determine compounds in green coffee beans and find the differentiation of green coffee beans into special or traditional ones through chemometrics. For that, the levels of phenolic compounds, reducing, nonreducing, and total sugars were quantified by spectrophotometry: caffeine, trigonelline, 5-hydroxymethylfurfural (5-HMF), 3-hydroxybenzoic, 4-hydroxybenzoic, chlorogenic, caffeic, and nicotinic acids (NAs) by high-performance liquid chromatography-UV-Vis; acetaldehyde, acetone, methanol, ethanol, and isoamyl by HS-GC-FID. Principal component analysis (PCA) was used to differentiate green coffee beans through the levels obtained in spectrophotometric and chromatographic analyses. Statistically, the contents of total phenolic compounds, caffeine, nonreducing sugars, total sugars, NA, 5-HMF, acetaldehyde, ethanol, and ethanol/methanol showed significant differences. The PCA made it possible to classify green coffee beans into special and traditional, in addition to understanding the attributes that influenced the differentiation between coffees. In addition, it was possible to classify green coffee beans into special and traditional, either using all parameters evaluated or only using spectrophotometric analyses. In this way, some advantages allow classification without using a trained and experienced evaluator as their previous experience can influence the results due to their expertise in a certain type of coffee, in addition to being faster and cheaper, especially regarding spectrophotometric analyses.

Bioactive profile and microbiological safety of Coffea arabica and Coffea canephora beverages obtained by innovative cold extraction methods (cold brews)

Coffee cold brews have been gaining prominence and popularity among consumers worldwide. However, only a few studies have systematically analyzed their chemical composition or evaluated microbiological safety aspects. This study aimed to evaluate the survival of Bacillus cereus and Escherichia coli in cold brews prepared from roasted and ground Coffea arabica and C. canephora seeds using the following preparation methods: immersion without filter (INF), immersion in a cotton filter bag (ICF), vacuum (Vac.) and cold dripping (Drip.). Traditional hot dripping methods using filter paper (HDFP) and cotton filter (HDCF) were also tested for comparison. Water at 4 °C or 25 °C was intentionally contaminated (105 CFU/mL) with cells of Escherichia coli ATCC 25922 (EC) and Bacillus cereus F4433 (BC) before coffee extraction and refrigeration at 4 °C. Coffee concentrations of 5, 10, and 15% were tested. Analyses of pH, soluble solids, nine chlorogenic acids and two lactones (CGA), caffeine, trigonelline, and melanoidins were performed. Results were compared by ANOVA, followed by the Fisher's test, Pearson correlation, Variable Importance in Projection (VIP), and Cluster analyses, with a significance level of 5%. EC and BC were not detected (<10 CFU/mL and < 1 CFU/mL, respectively) after preparing C. arabica and C. canephora hot brews. In cold brews, the higher the extraction of soluble solids and bioactive compounds (with the highest occurring at 25 °C), the lower the counts of inoculated microorganisms during 24 h of storage. BC was not detected after 24 h of extraction and/or storage in the drinks obtained by ICF and Drip. at 5%, 10%, and 15% and INF and Vac. at 15%. EC was not detected in ICF and Drip. at 10 and 15%, and in INF at 15%. C. canephora brews exhibited higher levels of soluble solids, CGA, caffeine, and melanoidins than C. arabica brews. Based on these results, it can be concluded that in the absence of thermal processing as in hot brews, more concentrated cold brews, such as 15%, produced at 25 °C by dripping and immersion methods, are preferable for later dilution due to the higher content of soluble solids and bioactive compounds that contribute reducing the number of microorganisms in the beverage.

Specialty and high-quality coffee: discrimination through elemental characterization via ICP-OES, ICP-MS, and ICP-MS/MS of origin, species, and variety

BACKGROUND

This study aimed to establish the elemental profiling and origin combined with the genetic asset of coffee samples collected from major coffee-producing countries. A total of 76 samples were analysed for 41 elements using inductively coupled plasma-optical emission spectroscopy (ICP-OES), inductively coupled plasma-mass spectrometry (ICP-MS), and inductively coupled plasma-triple quadrupole mass spectrometry (ICP-MS/MS). The mineral composition of the silver skin detachment during the roasting process was also evaluated to verify the loss of minerals during roasting, differences in composition with beans, and between species.

RESULTS

Application of linear discriminant analysis provided models with an accuracy of 93.3% for continents, 97.8% for countries of cultivation, and 100% for species. Discrimination between Arabica, Canephora coffee, and Eugenoides, and different varieties of Arabica species were identified in both models with calcium (Ca), barium (Ba), cadmium (Cd), rubidium (Rb), and strontium (Sr) as significant discriminant elements. Rb, Sr, sulphur (S), and thulium (Tm) were significant discriminant elements in both models for geographical distinction at different scales. Most of the elements had significantly higher values in silver skin than those in roasted coffee at different magnitudes, with exceptions of P and Rb.

CONCLUSION

In summary, determination of mineral elements, processed by multivariate statistical analysis, was demonstrated to be discriminant for different coffee species. Linear discriminant analysis of the elemental analysis of samples from the seven major producing countries provided a reliable prediction model. Elemental analysis of major and minor elements is relatively easy and can be used together with other traceability systems and sensory evaluations to authenticate the origin of roasted coffee, different species, and varieties. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Coffee bean processing: Emerging methods and their effects on chemical, biological and sensory properties

Emerging processing methods have been applied in coffee bean processing for improved sensory quality. The processes focus on optimizing the fermentation process of the coffee cherries and beans. This involves various pathways, including the formation of volatiles, flavor precursors and organic acids and the reduction in the concentrations of bioactive compounds. Comprehensive information regarding the effect of these emerging processes on the chemical, biological and sensory properties of the coffee beans is summarized. Emerging processes affected the coffee bean to various degrees depending on the raw material and the method used. The emerging methods promoted the reduction of bioactives such as caffeine and phenolics in coffee beans. Substantial improvement of these processes is needed to obtain coffee beans with improved biological activities. Effort to simplify the methods and optimize the post-fermentation process is crucial for the methods to be easily accessible by the producers and to produce defect-free coffee beans.

Preliminary Analysis of Unsaturated Fatty Acid Profiles of Coffea arabica L., in Samples with a Denomination of Origin and Speciality of Oaxaca, Mexico

In February 2020, Coffea arabica L. grown on the coast and in the Southern Sierra Madre of the state of Oaxaca, Mexico obtained the denomination of origin. Which does not have data on color and chemical composition, the first associated with the degree of roasting and the second with lipids (17-18%), as the group of compounds responsible, in part, for flavor, consistency, and may contribute to health benefits. In the present work, color was determined on the CIE L*a*b* scale and the unsaturated fatty acids by Nuclear Magnetic Resonance (NMR) of ¹H and ¹³C in samples of medium roasted specialty coffee from the "Pluma" coffee-growing region, Oaxaca, Mexico. The average value of L* luminosity in ground coffee was 42.1 ± 0.1 reported for a light roast. Unsaturated fatty acids were quantified from the lipid fraction of the gr₁ ound grain by NMR ¹H and ¹³C, obtaining on average the highest abundance of linoleic (41.7 ± 0.5 by ₁ H and 41.24 ± 0.5 by ¹³C), followed by oleic (9.2 ± 0.2 by ¹H and 7.4 ± 0.2 by ¹³C) and linolenic (1.5 ± 0.1 by H and 1.1 ± 0.2 by ¹³C). This study indicates that ¹H and ¹³C NMR spectroscopy is a useful tool for the quantification of linolenic, linoleic, and oleic fatty acids by the method of key signal shifts of these acids found in lipid samples in roasted coffee grains.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Targeted and Non-Targeted HPLC Analysis of Coffee-Based Products as Effective Tools for Evaluating the Coffee Authenticity

Coffee is a very popular beverage worldwide. However, its composition and characteristics are affected by a number of factors, such as geographical and botanical origin, harvesting and roasting conditions, and brewing method used. As coffee consumption rises, the demands on its high quality and authenticity naturally grows as well. Unfortunately, at the same time, various tricks of coffee adulteration occur more frequently, with the intention of quick economic profit. Many analytical methods have already been developed to verify the coffee authenticity, in which the high-performance liquid chromatography (HPLC) plays a crucial role, especially thanks to its high selectivity and sensitivity. Thus, this review summarizes the results of targeted and non-targeted HPLC analysis of coffee-based products over the last 10 years as an effective tool for determining coffee composition, which can help to reveal potential forgeries and non-compliance with good manufacturing practice, and subsequently protects consumers from buying overpriced low-quality product. The advantages and drawbacks of the targeted analysis are specified and contrasted with those of the non-targeted HPLC fingerprints, which simply consider the chemical profile of the sample, regardless of the determination of individual compounds present.

Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UV-Vis Spectroscopy and Chemometrics

In this present research, a spectroscopic method based on UV–Vis spectroscopy is utilized to quantify the level of corn adulteration in peaberry ground roasted coffee by chemometrics. Peaberry coffee with two types of bean processing of wet and dry-processed methods was used and intentionally adulterated by corn with a 10–50% level of adulteration. UV–Vis spectral data are obtained for aqueous samples in the range between 250 and 400 nm with a 1 nm interval. Three multivariate regression methods, including partial least squares regression (PLSR), multiple linear regression (MLR), and principal component regression (PCR), are used to predict the level of corn adulteration. The result shows that all individual regression models using individual wet and dry samples are better than that of global regression models using combined wet and dry samples. The best calibration model for individual wet and dry and combined samples is obtained for the PLSR model with a coefficient of determination in the range of 0.83–0.93 and RMSE below 6% (w/w) for calibration and validation. However, the error prediction in terms of RMSEP and bias were highly increased when the individual regression model was used to predict the level of corn adulteration with differences in the bean processing method. The obtained results demonstrate that the use of the global PLSR model is better in predicting the level of corn adulteration. The error prediction for this global model is acceptable with low RMSEP and bias for both individual and combined prediction samples. The obtained RPD_p and RER_p in prediction for the global PLSR model are more than two and five for individual and combined samples, respectively. The proposed method using UV–Vis spectroscopy with a global PLSR model can be applied to quantify the level of corn adulteration in peaberry ground roasted coffee with different bean processing methods.