Attenuated Total Reflectance Fourier Transform Spectroscopy (ATR-FTIR) and chemometrics for discrimination of espresso coffees with different sensory characteristics

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.

Application of ATR-FTIR for Green Arabica Bean Shelf-Life Determination in Accelerated Storage

Coffee bean oxidation is associated with enzymatic and non-enzymatic browning, the degradation of desirable aromatic compounds, the development of undesirable flavors, increased susceptibility to microbial spoilage, and volatile compound losses. This study investigated natural dry process (DP) and honey process (HP) green coffee beans stored in GrainPro® bags for 0, 5, 10, and 20 days under accelerated storage conditions at 30 °C, 40 °C, and 50 °C with relative humidity of 50%. A kinetic model was used to estimate the shelf life of the green coffee beans. DP recorded durability of 45.67, 29.9, and 24.92 days at 30 °C, 40 °C, and 50 °C, respectively, with HP 60.34, 38.07, and 19.22 days. Partial least squares (PLS) analysis was performed to build the models in order to predict the shelf life of coffee based on peroxide (PV) and thiobarbituric acid reactive substances (TBARS) values. In terms of prediction with leave-one-out cross-validation (LOOCV), PLS provided a higher accuracy for TBARS (R2 = 0.801), while PV was lower (R2 = 0.469). However, the auto-prediction showed good agreement among the observed and predicted values in both PV (R2 = 0.802) and TBARS (R2 = 0.932). Based on the variable importance of projection (VIP) scores, the ATR-FTIR peaks as 3000-2825, 2154-2150, 1780-1712, 1487-2483, 1186-1126, 1107-1097, and 1012-949 cm-1 were identified to be the most related to PV and TBARS on green coffee beans shelf life. ATR-FITR showed potential as a fast and accurate technique to evaluate the oxidation reaction that related to the loss of coffee quality during storage.

SHS-GC-MS applied in Coffea arabica and Coffea canephora blend assessment

Considering the great economic significance of Coffea arabica (arabica) associated with the lower production cost of C. canephora (conilon), blends of these coffees are commercially available to reduce costs and combine sensory attributes. Thus, analytical tools are required to ensure consistency between real and labeled compositions. In this sense, chromatographic methods based on volatile analysis using static headspace-gas chromatography-mass spectrometry (SHS-GC-MS) and Fourier transform infrared (FTIR) spectroscopy associated with chemometric tools were proposed for the identification and quantification of arabica and conilon blends. The peak integration from the total ion chromatogram (TIC) and extracted ion chromatogram (EIC) was compared in multivariate and univariate scenarios. The optimized partial least squares (PLS) models with uninformative variable elimination (UVE) and chromatographic data (TIC and EIC) have similar accuracy according to a randomized test, with prediction errors between 3.3% and 4.7% and R_p² > 0.98. There was no difference between the univariate models for the TIC and EIC, but the FTIR model presented a lower performance than GC-MS. The multivariate and univariate models based on chromatographic data had similar accuracy. For the classification models, the FTIR, TIC, and EIC data presented accuracies from 96% to 100% and error rates from 0% to 5%. Multivariate and univariate analyses combined with chromatographic and spectroscopic data allow the investigation of coffee blends.

Ecological quality as a coffee quality enhancer. A review

As both coffee quality and sustainability become increasingly important, there is growing interest in understanding how ecological quality affects coffee quality. Here we analyze, for the first time, the state of evidence that ecological quality, in terms of biodiversity and ecosystem functions, impacts the quality of Coffea arabica and C. canephora, based on 78 studies. The following ecosystem functions were included: pollination; weed, disease, and pest control; water and soil fertility regulation. Biodiversity was described by the presence, percentage, and diversity of shade trees. Coffee quality was described by the green bean physical characteristics, biochemical compounds, and organoleptic characteristics. The presence and diversity of shade trees positively impacted bean size and weight and reduced the percentage of rejected beans, but these observations were not consistent over different altitudes. In fact, little is known about the diversity of shade trees and their influence on biochemical compounds. All biochemical compounds varied with the presence of shade, percentage of shade, and elevation. Coffee beans from more diverse tree shade plantations obtained higher scores for final total organoleptic quality than simplified tree shade and unshaded plantations. Decreasing ecological quality diminished ecosystem functions such as pollination, which in turn negatively affected bean quality. Shade affected pests and diseases in different ways, but weeds were reduced. High soil quality positively affected coffee quality. Shade improved the water use efficiency, such that coffee plants were not water stressed and coffee quality was improved. While knowledge on the influence of shade trees on overall coffee quality remains scarce, there is evidence that agroecosystem simplification is negatively correlated with coffee quality. Given global concerns about biodiversity and habitat loss, we recommend that the overall definition of coffee quality include measures of ecological quality, although these aspects are not always detectable in certain coffee quality characteristics or the final cup.

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.

Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees

The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster’s training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR—Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in order to confirm the quality classifications. The PLS models provided good predictability and classification of the samples. The models were able to accurately predict the scores of specialty coffees. In addition, the NIR spectra provided relevant information on chemical bonds that define specialty coffee in association with sensory aspects, such as the cleanliness of the beverage.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

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.

Development of a fast and simple method to identify pure Arabica coffee and blended coffee by Infrared Spectroscopy

ABSTRACT

Coffee is the second most consumed beverage in the world and Brazil is the biggest coffee producer. The main coffee species are Arabica (Coffea arabica) and Robusta (Coffea canephora). Arabica presents superior sensorial characteristics, as flavor and aroma, and Robusta is less expensive to produce. Pure Arabica coffee presents a market share of 70% and Arabic and Robusta are mixed to produce blended coffee. In this work, a fast and simple method to identify Arabica and blended coffee was proposed. The samples were analyzed by Infrared Spectroscopy in the mid and near-infrared regions and the spectra were used to develop a discriminant method. Using the method, the purity varied from 99.44 to 99.94% for pure Arabica coffees. To evaluate the method, the samples were characterized by gas chromatography coupled to mass spectrometry. It was possible to identify Arabica and blended coffee with high accuracy, in one minute, without complex analyses or sample preparations. The method is useful when Arabica is blended with more than 20% of Robusta and the practical application of the method can be extended to all coffee producers and distributors to ensure quality and to identify frauds or blended coffees and pure Arabica coffees.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13197-021-05176-4.

Prediction of specialty coffee flavors based on near-infrared spectra using machine- and deep-learning methods

BACKGROUND

Specialty coffee fascinates people with its bountiful flavors. Currently, flavor descriptions of specialty coffee beans are only offered by certified coffee cuppers. However, such professionals are rare, and the market demand is tremendous. The hypothesis of this study was to investigate the feasibility to train machine learning (ML) and deep learning (DL) models for predicting the flavors of specialty coffee using near-infrared spectra of ground coffee as the input. Successful model development would provide a new and objective framework to predict complex flavors in food and beverage products.

RESULTS

In predicting seven categories of coffee flavors, the models developed using the ML method (i.e. support vector machine) and the deep convolutional neural network (DCNN) achieved similar performance, with the recall and accuracy being 70-73% and 75-77% respectively. Through the proposed visualization method - a focusing plot - the potential correlation among the highly weighted spectral region of the DCNN model, the predicted flavor categories, and the corresponding chemical composition are presented.

CONCLUSION

This study has proven the feasibility of applying ML and DL methods on the near-infrared spectra of ground coffee to predict specialty coffee flavors. The effective models provided moderate prediction for seven flavor categories based on 266 samples. The results of classification and visualization indicate that the DCNN model developed is a promising and explainable method for coffee flavor prediction. © 2021 Society of Chemical Industry.

Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees

Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control.

Co-inoculation of yeasts starters: A strategy to improve quality of low altitude Arabica coffee

The study aimed to improve the quality of dry-processed coffee grown at low altitudes through yeast inoculation, using three species (Saccharomyces cerevisiae CCMA 0543, Torulaspora delbrueckii CCMA 0684, and Candida parapsilosis CCMA 0544) singly and with co-inoculation for fermentation. Important chemical compounds and groups were analyzed by liquid and gas chromatography and Fourier-transform infrared spectroscopy (FTIR). The inoculated coffees with yeast populations around 10⁶ cell/g obtained the highest scores, and the co-inoculation with C. parapsilosis CCMA 0544 and T. delbrueckii CCMA 0684 had the highest score in the sensory analysis (85). Different descriptors were observed in each treatment, and body, flavor, balance, and aftertaste are strongly related to C. parapsilosis CCMA 0544. The fermentation process improved the quality of low-altitude coffees, and the combination of non-Saccharomyces yeasts (C. parapsilosis CCMA 0544 and T. delbrueckii CCMA 0684) was the most indicated as starter cultures.

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

Coffee beans contain different volatile and non-volatile compounds that are responsible for their flavor and aroma. Herein, principal component analysis (PCA) was employed to correlate the non-volatile composition of specialty and traditional coffees with drink quality. The quantified non-volatile compounds included caffeine, chlorogenic acid, caffeic acid, and nicotinic acid in both types of coffee samples, while 5-hydroxymethylfurfural was only quantified in the specialty coffee samples. The most abundant compounds present in specialty coffees were associated with the aroma and flavor, affording a high drink quality. In traditional coffees, the most abundant compounds included nicotinic acid and caffeine, indicating a stronger roasting process, loss of sensory characteristics, and blended formulations. PCA showed a distinction between the traditional and specialty coffees such that a relationship between the contents of the compounds in each type of coffee, quality, and classification could be established.

Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products

Chemometrics offers an important complementary tool to enhance the searching and isolation of bioactive natural products from natural sources.

Comprehensive quality assessment of Dendrubium officinale using ATR-FTIR spectroscopy combined with random forest and support vector machine regression

Dendrobium officinale, as a tonic herb, has attracted more and more consumers to consume in daily life. In order to protect the wild resource, the herb has made great progress though cultivation in vitro. However, the quality is fluctuated in Chinese herbal medicine market due to influence such as cultivated areas and harvesting period. Therefore, the herbal samples from different cultivated locations were evaluated with high-performance liquid chromatography with diode array detector (HPLC-DAD) in terms of two chemical components, quercetin and erianin. In addition, two markers in leaf and stem also were used for support vector machine regression (SVMR) prediction. Samples from different harvesting periods were also classified using attenuated total reflectance mid-infrared spectroscopy coupled with random forest model. The results indicated that Pu'er and Menghai in Yunnan Province were suitable places for the herb cultivation and the leaf of the herb was also an exploitable resource just in light of the content of two components. What's more, combination of suitable spectra pretreatment and grid search method efficiently improved the prediction performance of the regression model. The results of random forest model indicated that important variables combination between stem and leaf was an effective tool to predict the harvesting time of the herb with 94.44% accuracy in calibration set and 97.92% classification correct rate in validation set. The results of combination were better than the models using individual stem and leaf spectra. In addition, the suitable harvesting time (December) could be classified efficiently. Our study provides a reference for quality control of raw materials from D. officinale herb.