Use of non-volatile compounds for the classification of specialty and traditional Brazilian coffees using principal component analysis

Direct Hot Solid-Liquid Extraction (DH-SLE): A High-Yield Greener Technique for Lipid Recovery from Coffee Beans

Soxhlet extraction is a method recommended by the Association of Official Analytical Chemists (AOAC) to determine the lipid content in plant samples. Generally, n-hexane (toxicity grade 5) is used as the solvent (≈300 mL; ≈30 g sample) at boiling temperatures (69 °C) for long times (≤16 h) under a chilled water reflux (≈90 L/h), proportionally aggravated by the number of repetitions and samples determined. In this sense, the technique is neither safe nor sustainable for the analyst or the environment. This article presents the development of an alternative and more sustainable procedure for determining the lipid content in raw Arabica coffee beans. A 33 full factorial design was used to perform direct hot solid-liquid extractions in 4 mL vials, varying the ground grains and solvent ratios, temperatures, and times. An optimal condition resulted in an extractive yield statistically equivalent to Soxhlet, without variation in the composition of the oil fatty acids determined by GC-MS after hole oil transesterification. This procedure was presented as a sustainable alternative to Soxhlet extraction because it does not require water for cooling and needs a smaller volume of solvent (2 mL) and sample mass (0.2 g); it also has a smaller generated residue, as well as requiring a shorter time (1.5 h) and less energy expenditure for extraction.

Optimizing commercial Arabica coffee quality by integrating flavor precursors with anaerobic germination strategy

This study attempted to improve commercial Arabica coffee quality by integrating flavor precursors with anaerobic germination. Using raw coffee beans as materials, anaerobic germination was conducted with 5 g/100 g of flavor precursors (sucrose, glucose, fructose). The chemical composition and sensory quality of roasted coffee beans were analyzed. Results showed that adding flavor precursors facilitated the harmonization of water-soluble chemical components and altered aroma characteristics. Specifically, the inclusion of flavor precursors significantly increased the levels of 5-Hydroxymethylfurfural and volatile aldehydes. Principal component analysis (PCA) on chemical composition dataset revealed 48.7% variability. Sensory analysis, employing the Specialty Coffee Association (SCA) cupping protocol, demonstrated that combining flavor precursors with anaerobic germination transformed coffee flavor properties, enhanced quality, and substantially increased sensory scores (p < 0.05). Sucrose supplementation produced the highest sensory score and intensified fruity flavor attributes. Therefore, adding different flavor precursors forms distinct flavor characteristics, conducive to further improving the quality of germinated coffee.

Effects of geographical origin and post-harvesting processing on the bioactive compounds and sensory quality of Brazilian specialty coffee beans

Specialty coffee beans are those produced, processed, and characterized following the highest quality standards, toward delivering a superior final product. Environmental, climatic, genetic, and processing factors greatly influence the green beans' chemical profile, which reflects on the quality and pricing. The present study focuses on the assessment of eight major health-beneficial bioactive compounds in green coffee beans aiming to underscore the influence of the geographical origin and post-harvesting processing on the quality of the final beverage. For that, we examined the non-volatile chemical profile of specialty Coffea arabica beans from Minas Gerais state, Brazil. It included samples from Cerrado (Savannah), and Matas de Minas and Sul de Minas (Atlantic Forest) regions, produced by two post-harvesting processing practices. Trigonelline, theobromine, theophylline, chlorogenic acid derivatives, caffeine, caffeic acid, ferulic acid, and p-coumaric acid were quantified in the green beans by high-performance liquid chromatography with diode array detection. Additionally, all samples were roasted and subjected to sensory analysis for coffee grading. Principal component analysis suggested that Cerrado samples tended to set apart from the other geographical locations. Those samples also exhibited higher levels of trigonelline as confirmed by two-way ANOVA analysis. Samples subjected to de-pulping processing showed improved chemical composition and sensory score. Those pulped coffees displayed 5.8% more chlorogenic acid derivatives, with an enhancement of 1.5% in the sensory score compared to unprocessed counterparts. Multivariate logistic regression analysis pointed out altitude, ferulic acid, p-coumaric acid, sweetness, and acidity as predictors distinguishing specialty coffee beans obtained by the two post-harvest processing. These findings demonstrate the influence of regional growth conditions and post-harvest treatments on the chemical and sensory quality of coffee. In summary, the present study underscores the value of integrating target metabolite analysis with statistical tools to augment the characterization of specialty coffee beans, offering novel insights for quality assessment with a focus on their bioactive compounds.

Characteristics of Purified Horse Oil by Supercritical Fluid Extraction with Different Deodorants Agents

This study investigated the impact of activated carbon, palm activated carbon, and zeolite on horse oil (HO) extracted from horse neck fat using supercritical fluid extraction with deodorant-untreated HO (CON) as a comparison. The yield and lipid oxidation of deodorant untreated HO (CON) were not significantly affected by the three deodorants. However, deodorant-treated HOs exhibited significantly elevated levels of α-linolenic acid (C18:3n3) and eicosenoic acid (C20:1n9) compared to CON (p<0.05), while other fatty acids remained consistent. Zeolite-purified HO demonstrated significantly lower levels of volatile organic compounds (VOCs) than other treatments (p<0.05). Remarkably, zeolite decreased the concentration of pentane, 2,3-dimethyl (gasoline odor), by over 90%, from 177.17 A.U. ×106 in CON to 15.91 A.U. ×106. Zeolite also effectively eliminates sec-butylamine (ammonia and fishy odor) as compared to other deodorant-treated HOs (p<0.05). Additionally, zeolite reduced VOCs associated with the fruity citrus flavor, such as nonanal, octanal, and D-limonene in HO (p<0.05). This study suggests that integrating zeolite in supercritical fluid extraction enhances HO purification by effectively eliminating undesirable VOCs, presenting a valuable approach for producing high-quality HO production in the cosmetic and functional food industries.

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.

Changes of polyphenols and antioxidants of arabica coffee varieties during roasting

Coffee is the most consumed beverage in the world after water. Multiple benefits are attributed to it in human health due to the presence of antioxidant compounds, whose content depends, among other factors, on the processing conditions of the coffee bean. The objective of this study was to determine the kinetics of polyphenols and antioxidants during the roasting of three varieties of arabica coffee. For this, we worked with varieties of coffee, Catimor, Caturra, and Bourbon, from the province of La Convencion, Cuzco, Peru. The samples were roasted in an automatic induction roaster, and 12 samples were taken during roasting (at 0, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21 min of roasting) in triplicate. For green coffee beans, titratable acidity, total soluble solids, moisture and apparent density were determined. The change in polyphenol content was determined using the Folin-Ciocalteu method, and antioxidant activity was determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2-azino-bis- (3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS⁺) free radical capture technique during roasting. Polyphenol and antioxidant contents increased until minute 5 of roasting and then decreased until minute 20, and in some cases, there were slight increases in the last minute. The model that best described the changes in these bioactive compounds was the cubic model (R ² 0.634 and 0.921), and the best fits were found for the Bourbon variety, whose green grain had more homogeneous characteristics. The changes in the relative abundances of nine phenolic compounds were determined using high-performance liquid chromatography (HPLC). In conclusion, roasting modifies phenolic compounds and antioxidants differently in the coffee varieties studied. The content of some phenols increases, and in other cases, it decreases as the roasting time increases. The roasting process negatively affects the bioactive compounds and increases the fracturability of Arabica coffee beans, elements that should be taken into account at the moment of developing roasting models in the industry.

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.

Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy

Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

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.