Exploiting near infrared spectroscopy as an analytical tool for on-line monitoring of acidity during coffee roasting

Prediction of coffee traits by artificial neural networks and laser-assisted rapid evaporative ionization mass spectrometry

BACKGROUND

Coffee is an important commodity in the worldwide economy and smart technologies are important for accurate quality control and consumer-oriented product development. Sensory perception is probably the most important information since it is directly related to product acceptance. However, sensory analysis is imprecise and present large deviation related to subjectivity and relying exclusively on the sensory panel. Thus, practical technologies may be developed to assist in making accurate decisions.

RESULTS

This study presents a new method applying laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) coupled with high-resolution mass spectrometry to fingerprint coffee samples. Predictive models have estimated sensory properties with accuracies between 87 and 96 % for test samples. The complex relationship between the MS profiles and modelled properties, artificial neural networks (ANN) outperformed partial least square-discriminant analysis (PLS-DA) on estimation of coffee properties. Tentatively identified compounds such as sugars, chlorogenic, and fatty acids were the ones that most affected coffee sensory properties according to a novel approach to evaluate ANN weights.

SIGNIFICANCE

The proposed method could analyse coffee samples with minimal sample preparation using an automated device. Predictive models can be applied to assist sensory panel on making decision due to accuracies up to 96 % Additionally, a novel algorithm for evaluate m/z importance in ANN models were presented, paving the way for a higher-level of interpretation by using this algorithm.

The Role of Near-Infrared Spectroscopy in Food Quality Assurance: A Review of the Past Two Decades

During food quality control, NIR technology enables the rapid and non-destructive determination of the typical quality characteristics of food categories, their origin, and the detection of potential counterfeits. Over the past 20 years, the NIR results for a variety of food groups-including meat and meat products, milk and milk products, baked goods, pasta, honey, vegetables, fruits, and luxury items like coffee, tea, and chocolate-have been compiled. This review aims to give a broad overview of the NIRS processes that have been used thus far to assist researchers employing non-destructive techniques in comparing their findings with earlier data and determining new research directions.

The effect of roast profiles on the dynamics of titratable acidity during coffee roasting

Coffee professionals have long known that the "roast profile," i.e., the temperature versus time inside the roaster, strongly affects the flavor and quality of the coffee. A particularly important attribute of brewed coffee is the perceived sourness, which is known to be strongly correlated to the total titratable acidity (TA). Most prior work has focused on laboratory-scale roasters with little control over the roast profile, so the relationship between roast profile in a commercial-scale roaster and the corresponding development of TA to date remains unclear. Here we investigate roast profiles of the same total duration but very different dynamics inside a 5-kg commercial drum roaster, and we show that the TA invariably peaks during first crack and then decays to its original value by second crack. Although the dynamics of the TA development varied with roast profile, the peak TA surprisingly exhibited almost no statistically significant differences among roast profiles. Our results provide insight on how to manipulate and achieve desired sourness during roasting.

Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed.

In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s).

Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties.

The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale.

Literary Review: Coffee Technologies

The following article is focused on technologies that can be used to increase or improve the production of coffee. In the modern days the most popular drink can be considered coffee. Its consumption is increasing each year with the increase of population of the planet. Therefore, it is important to use throughout the whole process of getting to the final product of coffee the best available techniques. The objective of this work is to review in the literature different technologies applied to coffee. Authors conclude that technologies that improve crop yields such as artificial intelligence are novel and need to be implemented. On the other hand, the production processes have robust machinery that is well known to coffee growers. Finally, the laboratory technologies to measure the phytochemical qualities of the coffee should be further refined to guarantee the results.

Near-infrared spectroscopy as a green technology to monitor coffee roasting

Wet chemistry methods are traditionally used to evaluate the quality of a coffee beverage and its chemical characteristics. These old methods need to be replaced with more rapid, objective, and simple analytical methods for routine analysis. Near-infrared spectroscopy is an increasingly popular technique for nondestructive quality evaluation called a green technology. Our study aimed to apply near-infrared spectroscopy to evaluate the quality of coffee samples of different origin (Brazil, Guatemala, Peru, and Kongo). Particularly, we analyzed the roasting time and its effect on the quality of coffee. The colorimetric method determined a relation between the coffee color and the time of roasting. Partial least squares regression analysis assessed a possibility of predicting the roasting conditions from the near-infrared spectra. The regression results confirmed the possibility of applying near-infrared spectra to estimate the roasting conditions. The correlation between the spectra and the roasting time had R2 values of 0.96 and 0.95 for calibration and validation, respectively. The root mean square errors of prediction were low – 0.92 and 1.05 for calibration and validation, respectively. We also found a linear relation between the spectra and the roasting power. The quality of the models differed depending on the coffee origin and sub-region. All the coffee samples showed a good correlation between the spectra and the brightness (L* parameter), with R2 values of 0.96 and 0.95 for the calibration and validation curves, respectively. According to the results, near-infrared spectroscopy can be used together with the chemometric analysis as a green technology to assess the quality of coffee.

Loss of Sensory Cup Quality: Physiological and Chemical Changes during Green Coffee Storage

Coffee is one of the most valued consumer products. Surprisingly, there is limited scientific knowledge about the biochemical processes during the storage of green coffee that affects its sensory quality. This review analyzes the impact of the different variables involved in the green coffee storage on quality from a chemical point of view. Further, it highlights the relationship between the physiological processes of the grain, its viability, and shelf-life. Notably, the storage conditions and postharvest treatment affect both the longevity and the sensory quality of the coffee, probably due to the biological behavior of green coffee. Various studies found modifications in their chemical profiles involving carbohydrates, lipids, proteins/amino acids, and phenolic compounds. To make future studies more comparable, we recommend standardized protocols for evaluating and linking the sensory coffee quality with instrumental analysis methods and pre-defined settings for experimental storage conditions.

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.

Evaluation of chemical properties of intact green coffee beans using near-infrared spectroscopy

BACKGROUND

The chemical compounds in coffee are important indicators of quality. Its composition varies according to several factors related to the planting and processing of coffee. Thus, this study proposed to use near-infrared spectroscopy (NIR) associated with partial least squares (PLS) regression to estimate quickly some chemical properties (moisture content, soluble solids, and total and reducing sugars) in intact green coffee samples. For this, 250 samples produced in Brazil were analyzed in the laboratory by the standard method and also had their spectra recorded.

RESULTS

The calibration models were developed using PLS regression with cross-validation and tested in a validation set. The models were elaborated using original spectra and preprocessed by five different mathematical methods. These models were compared in relation to the coefficient of determination, root mean square error of cross-validation (RMSECV), root mean square error of test set validation (RMSEP), and ratio of performance to deviation (RPD) and demonstrated different predictive capabilities for the chemical properties of coffee. The best model was obtained to predict grain moisture and the worst performance was observed for the soluble solids model. The highest determination coefficients obtained for the samples in the validation set were equal to 0.810, 0.516, 0.694 and 0.781 for moisture, soluble solids, total sugar, and reducing sugars, respectively.

CONCLUSION

The statistics associated with these models indicate that NIR technology has the potential to be applied routinely to predict the chemical properties of green coffee, and in particular, for moisture analysis. However, the soluble solid and total sugar content did not show high correlations with the spectroscopic data and need to be improved. © 2020 Society of Chemical Industry.

An untargeted metabolomic insight into the high-pressure stress effect on the germination of wholegrain Oryza sativa L

High hydrostatic pressure (HHP) technique is used as a novel abiotic stress factor for efficiently enhancing the biosynthesis of selected bioactive phytochemicals in germinated wholegrain, but the information about HHP stress-induced metabolic changes remains rather limited. Thus, the current work employed an untargeted gas chromatography-mass spectrometry-based metabolomic approach combining with multivariate models to analyze the effect of mild HHP stress (30 MPa/5 min) on the overall metabolome shifts of wholegrain brown rice (WBR) during germination. Simultaneously, major phenolics in germinated WBR (GBR) were detected by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry, to explore the potential relationship between HHP stress-induced rice metabolome alternations and the biotransformation of bioactive components. The results demonstrated that the influence of HHP stress on GBR metabolite profiles was defined by germination durations, as revealed by the differentiation of the stressed grains from the naturally germinated grains at different germination points according to principal component analysis. This was further confirmed by the results of orthogonal projections to latent structures discriminant analysis, in which the discriminating metabolites between naturally germinated and HHP-stressed grains varied across the germination process. The metabolite signatures differentiating natural and HHP-stressed germination included glycerol-3-phosphate, monosaccharides, gamma-aminobutyric acid, 2,3-butanediol, glyceryl-glycoside, amino acids and myo-inositol. Besides, HHP stress led to the increase in ribose, arabinitol, salicylic acid, azelaic acid and gamma-aminobutyric acid, as well as the reduced phenolic acids. These results demonstrated that HHP stress before germination matched with appropriate process parameters could be used as a promising technology to tailor metabolic features of germinated products, thus exerting targeted nutrition and health implications.

Chemical and sensory evaluation of cold brew coffees using different roasting profiles and brewing methods

This study evaluated the effects of different roasting profiles (time/temperature) and brewing methods on the physicochemical and sensory characteristics of coffee brews. Cold brewing (dripping and immersion) and hot brewing (French press) methods were studied to understand the effects of water temperature and technical brewing method conditions on the chemical compound extraction in coffees roasted at high-temperature short time (HTST) and low-temperature long time (LTLT). The results showed that coffee beverages were clearly differentiated concerning the roasting profile when hot water was used (90 ± 3 °C) in brewing. Separation of beverages according to the water temperature used in brewing was observed. Notably, hot brewing coffees were distinguished from cold brewing (19 ± 2 °C) based on a higher titratable acidity and abundance of some furan compounds. The non-volatile extraction rate increased at higher brewing temperatures. At the same brewing temperature, dripping exhibited a higher extraction rate than immersion brewing, which suggests that the coffee extraction process is affected by the design and operation of the cold brewing system. Coffee beverages brewed with HTST and cold dripping displayed the highest value in total dissolved solids (TDS), extraction yield, as well as the highest caffeine, trigonelline, 4- and 5-caffeoylquinic acids (CQAs) contents. Regardless of the roasting profile, coffees brewed by cold dripping were perceived with more bitter and roasted flavors. In contrast, cold immersion and hot coffee beverages showed remarkable sweetness, nutty, caramel, and malt attributes. In turn, these attributes showed an inverse correlation with caffeine concentration, trigonelline, CQAs, and TDS. The findings of this study demonstrate that volatile and non-volatile compounds present in roasted coffee depend on time-temperature roasting conditions; in turn, their presence in the resulting beverages are related to the extraction of the operational conditions of coffee brewing methods.

Degradation of benzopyrene and acrylamide in roasted coffee beans by corona discharge plasma jet (CDPJ) and its effects on biochemical and sensory properties

This study was aimed to reduce the concentrations of benzopyrene (BaP) and acrylamide (ACR) in roasted coffee beans by corona discharge plasma jet (CDPJ). The initial concentrations of BaP and ACR in roasted beans were decreased by 53.6% and 32.0%, respectively, following CDPJ (powered by 20 kV DC/1.5 A) treatment for 60 min. The levels of total solid, total acid, chlorogenic acid, caffeine, trigonelline, and pH were insignificantly changed upon CDPJ treatment compared to controls. However, the concentration of total phenolic content and Agtron color values were altered significantly. The treatment of beans did not alter descriptive sensory properties of the corresponding coffee brews, except aroma and aftertaste characteristics. As the treatment time increased from 15 to 60 min, scores for aroma profiles in PCA plot were shifted from right to left, although overlapping was observed between 15- and 30-min-treated samples. Additionally, none of the treated samples were discriminated from the control by electronic tongue.

Smart Online Coffee Roasting Process Control: Modelling Coffee Roast Degree and Brew Antioxidant Capacity for Real-Time Prediction by Resonance-Enhanced Multi-Photon Ionization Mass Spectrometric (REMPI-TOFMS) Monitoring of Roast Gases

Process control with high time resolution is essential to maintain high product quality in coffee roasting. However, analytical techniques for quality assurance or measurements of desired coffee properties are often labor-intensive and can only be conducted after dropping the coffee beans. Resonance-enhanced multi-photon ionization time-of-flight mass spectrometry (REMPI-TOFMS) at 248 nm and 266 nm was applied to analyze the composition of the roast gas from small-scale Arabica coffee roasting. Coffee beans were dropped after different roasting times, ground and analyzed by Colorette to obtain the roast degree. Additionally, the antioxidant capacity of the coffee brew was determined by Folin–Ciocalteu (FC) assay. Models for the prediction of Colorette and FC values from REMPI mass spectra were constructed by partial least squares (PLS) regression. REMPI-TOFMS enables the prediction of Colorette values with a root-mean-square error in prediction (RMSE_P) below 5 for both wavelengths. FC values could be predicted using REMPI at 248 nm with an RMSE_P of 80.3 gallic acid equivalents (GA-eq) mg L⁻¹, while REMPI at 266 nm resulted in RMSE_P of 151 GA-eq mg L⁻¹. Finally, the prediction of Colorette and FC value at 5 s time resolution were demonstrated with online measurements.

Toward Smart Online Coffee Roasting Process Control: Feasibility of Real-Time Prediction of Coffee Roast Degree and Brew Antioxidant Capacity by Single-Photon Ionization Mass Spectrometric Monitoring of Roast Gases

Precise controlling and monitoring the status of the coffee roasting process is essential for consistent product quality and optimization toward targeted coffee properties. In small-scale roasting experiments, the chemical composition of the roasting off-gas was analyzed by online single-photon ionization time-of-flight mass spectrometry (SPI-TOFMS) at 118 nm with 5 s time resolution. Subsequently, mass spectra at the drop of the coffee beans were combined with off-line measurements of roast degree, described by color value "Colorette", and the antioxidant capacity, obtained from the Folin-Ciocalteu (FC) assay, in an explanatory projection on latent structure regression model. While the roast degree gives an indication of the coffee flavor, antioxidants in brewed coffee are regarded as beneficial for human health. Colorette and FC values could be derived from the SPI mass spectra with root-mean-square errors from Monte Carlo cross-validation of 6.0 and 139 mg of gallic acid equiv L^-1, respectively, and explained covariance (R²_CV) better than 89%. Finally, the regression models were applied to the SPI mass spectra over the entire roast to demonstrate the predictive ability for online process control in real time.

Determination of pH and acidity in green coffee using near-infrared spectroscopy and multivariate regression

BACKGROUND

Coffee is a raw material of global interest. Due to its relevance, this work evaluated the performance of calibration models constructed from spectral data obtained using near-infrared spectroscopy (FT-NIR) to determine the pH values and acidity in coffee beans in a practical and non-destructive way. Partial least squares regression was used during the calibration and the cross-validation to optimize the number of latent variables. The predictive capacity of the spectral pre-processing methods was also accessed.

RESULTS

The results obtained showed that the best methods of pre-processing were the first derivative for the pH variable and the standard normal variate for the acidity, which produced models with correlations of 0.78 and 0.92, ratios of prediction to deviation of 2.061 and 2.966 and biases of -0.00011 and -0.152 to test set validation, respectively. The average errors between predicted and experimental values were lower than 7%.

CONCLUSIONS

FT-NIR was successfully applied to predict properties related to the quality of coffee. The method was demonstrated to be a fast and non-destructive tool which allows the rapid inline evaluation of samples facilitating industrial and commercial processing. © 2020 Society of Chemical Industry.

Farm to Consumer: Factors Affecting the Organoleptic Characteristics of Coffee. II: Postharvest Processing Factors

The production and consumption of coffee are increasing despite the roadblocks to its agriculture and global trade. The unique, refreshing, and stimulating final cupping quality of coffee is the only reason for this rising production and consumption. Coffee quality is a multifaceted trait and is inevitably influenced by the way it is successively processed after harvesting. Reportedly, 60% of the quality attributes of coffee are governed by postharvest processing. The current review elaborates and establishes for the first time the relationship between different methods of postharvest processing of coffee and its varying organoleptic and sensory quality attributes. In view of the proven significance of each processing step, this review has been subdivided into three sections, secondary processing, primary processing, and postprocessing variables. Secondary processing addresses the immediate processing steps on the farm after harvest and storage before roasting. The primary processing section adheres specifically to roasting, grinding and brewing/extraction, topics which have been technically addressed more than any others in the literature and by industry. The postprocessing attribute section deals generally with interaction of the consumer with products of different visual appearance. Finally, there are still some bottlenecks which need to be addressed, not only to completely understand the relationship of varying postharvest processing methods with varying in-cup quality attributes, but also to devise the next generation of coffee processing technologies.

Differentiation of Chinese robusta coffees according to species, using a combined electronic nose and tongue, with the aid of chemometrics

Electronic nose and tongue sensors and chemometric multivariate analysis were applied to characterize and classify 7 Chinese robusta coffee cultivars with different roasting degrees. Analytical data were obtained from 126 samples of roasted coffee beans distributed in the Hainan Province of China. Physicochemical qualities, such as the pH, titratable acidity (TA), total soluble solids (TSS), total solids (TS), and TSS/TA ratio, were determined by wet chemistry methods. Data fusion strategies were investigated to improve the performance of models relative to the performance of a single technique. Clear classification of all the studied coffee samples was achieved by principal component analysis, K-nearest neighbour analysis, partial least squares discriminant analysis, and a back-propagation artificial neural network. Quantitative models were established between the sensor responses and the reference physicochemical qualities, using partial least squares regression (PLSR). The PLSR model with a fusion data set was considered the best model for determining the quality parameters.