Discrimination between defective and non-defective roasted coffees by diffuse reflectance infrared Fourier transform spectroscopy

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.

Metabolomics as a tool for geographic origin assessment of roasted and green coffee beans

Coffee is widely consumed across the globe. The most sought out varieties are Arabica and Robusta which differ significantly in their aroma and taste. Furthermore, varieties cultivated in different regions are perceived to have distinct characteristics encouraging some producers to adopt the denomination of origin label. These differences arise from variations on metabolite content related to edaphoclimatic conditions and post-harvest management among other factors. Although sensory analysis is still standard for coffee brews, instrumental analysis of the roasted and green beans to assess the quality of the final product has been encouraged. Metabolomic profiling has risen as a promising approach not only for quality purposes but also for geographic origin assignment. Many techniques can be applied for sample analysis: chromatography, mass spectrometry, and NMR have been explored. The data collected is further sorted by multivariate analysis to identify similar characteristics among the samples, reduce dimensionality and/or even propose a model for predictive purposes. This review focuses on the evolution of metabolomic profiling for the geographic origin assessment of roasted and green coffee beans in the last 21 years, the techniques that are usually applied for sample analysis and also the most common approaches for the multivariate analysis of the collected data. The prospect of applying a wide range of analytical techniques is becoming an unbiased approach to determine the origin of different roasted and green coffee beans samples with great correlation. Predictive models worked accurately for the geographic assignment of unknown samples once the variety was known.

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.

Spent coffee ground torrefaction for waste remediation and valorization

Spent coffee grounds (SCGs) are a noticeable waste that may cause environmental pollution problems if not treated appropriately. Torrefaction is a promising low-temperature carbonization technique to achieve waste remediation, recovery, and circular bioeconomy efficiently. This study aims to maximize lipids retained in thermally degraded SCGs, thereby upgrading their fuel quality to implement resource sustainability and availability. This work also analyzes the lipid contribution to biochar's calorific value under various carbonization temperatures and times. Torrefaction can retain 11-15 wt% lipids from SCG, but the lipid content decreases when the pyrolysis temperature is higher than 300 °C. Extracted lipid content consisting of fatty acids echoed the results of diesel adsorption capacity. The lipid content in the biochar from SCG torrefied at 300 °C for 30 min is 11.00 wt%, and its HHV is 28.16 MJ kg^-1. In this biochar, lipids contribute about 14.84% of the calorific value, and the other carbonized solid contributes 85.16%. On account of the higher lipid content in the biochar, it has the highest diesel adsorption amount per unit mass, with a value of 1.66 g g^-1. This value accounts for a 22.1% improvement compared to its untorrefied SCG. Accordingly, torrefaction can sufficiently remediate SCG-derived environmental pollution. The produced biochar can become a spilled oil adsorbent. Furthermore, oil-adsorbed biochar (oilchar) is a potential solid fuel. In summary, SCG torrefaction can simultaneously achieve pollution remediation, waste valorization, resource sustainability, and circular bioeconomy.

Infrared-Photoacoustic Spectroscopy and Multiproduct Multivariate Calibration to Estimate the Proportion of Coffee Defects in Roasted Samples

Infrared-photoacoustic spectroscopy (IR-PAS) and partial least squares (PLS) were tested as a rapid alternative to conventional methods to evaluate the proportion of coffee defects in roasted and ground coffees. Multiproduct multivariate calibration models were obtained from spectra of healthy beans of Coffea canephora and C. arabica (Arabica) and blends composed of defective and healthy beans of Arabica in different proportions. The blends, named selections, contained sour, black, broken, whole beans, skin, and coffee woods. Six models were built using roasted and ground coffee samples. The model was optimized through outlier evaluation, and the parameters of merit such as accuracy, sensitivity, limits of detection and quantification, the inverse of analytical sensitivity, linearity, and adjustment were computed. The models presented predictive capacity and high sensitivity in determining defects, all being predicted with suitable correlation coefficients (ranging from 0.7176 to 0.8080) and presenting adequate performance. The parameters of merit displayed promising results, and the prediction models developed for %defects can be safely used as an alternative to the reference method. Furthermore, the new method is fast, efficient, and suitable for in-line application in quality control industrial coffee processing.

Rapid authentication of coffee bean varieties of different forms by using a pocket-sized spectrometer and multivariate data modelling

Coffee is the most consumed beverage and the second most valuable traded commodity in the world. In this current study, a pocket-sized spectrometer and multivariate analysis were used for rapid authentication of coffee varieties (Arabica and Robusta) in three states to check mislabelling (food fraud). Two main coffee varieties were collected from different locations in Africa. The samples were scanned in the 740-1070 nm wavelength and the spectral data were pre-treated with several methods: mean centering (MC), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD) and standard normal variate (SNV) independently while partial least squares discriminate analysis (PLS-DA), K-nearest neighbour (KNN) and support vector machine (SVM) were used to comparatively build the prediction models for coffee beans (raw, roasted and powdered). The performances of the models were evaluated by using accuracy and efficiency. Among the classification methods developed, the best results were obtained for the following: raw coffee bean SD-SVM had an accuracy of 0.92 and efficiency of 0.82. For roasted coffee beans, SD-KNN had an accuracy of 0.92 and efficiency of 0.87, while for roasted powdered coffee, FD-KNN showed an accuracy of 0.97 and efficiency of 0.97. These finding reveals that for a more accurate differentiation of coffee beans, the roasted powder offers the best results. The obtained results showed that a pocket-sized spectrometer coupled with chemometrics could be employed to provide accurate and rapid authentication of different categories of coffee bean varieties.

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.

Metabolomics-Based Approach for Coffee Beverage Improvement in the Context of Processing, Brewing Methods, and Quality Attributes

Coffee is a worldwide beverage of increasing consumption, owing to its unique flavor and several health benefits. Metabolites of coffee are numerous and could be classified on various bases, of which some are endogenous to coffee seeds, i.e., alkaloids, diterpenes, sugars, and amino acids, while others are generated during coffee processing, for example during roasting and brewing, such as furans, pyrazines, and melanoidins. As a beverage, it provides various distinct flavors, i.e., sourness, bitterness, and an astringent taste attributed to the presence of carboxylic acids, alkaloids, and chlorogenic acids. To resolve such a complex chemical makeup and to relate chemical composition to coffee effects, large-scale metabolomics technologies are being increasingly reported in the literature for proof of coffee quality and efficacy. This review summarizes the applications of various mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based metabolomics technologies in determining the impact of coffee breeding, origin, roasting, and brewing on coffee chemical composition, and considers this in relation to quality control (QC) determination, for example, by classifying defected and non-defected seeds or detecting the adulteration of raw materials. Resolving the coffee metabolome can aid future attempts to yield coffee seeds of desirable traits and best flavor types.

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.

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.

Mid-Infrared Spectroscopy as a Valuable Tool to Tackle Food Analysis: A Literature Review on Coffee, Dairies, Honey, Olive Oil and Wine

Nowadays, food adulteration and authentication are topics of utmost importance for consumers, food producers, business operators and regulatory agencies. Therefore, there is an increasing search for rapid, robust and accurate analytical techniques to determine the authenticity and to detect adulteration and misrepresentation. Mid-infrared spectroscopy (MIR), often associated with chemometric techniques, offers a fast and accurate method to detect and predict food adulteration based on the fingerprint characteristics of the food matrix. In the first part of this review the basic concepts of infrared spectroscopy, sampling techniques, as well as an overview of chemometric tools are summarized. In the second part, recent applications of MIR spectroscopy to the analysis of foods such as coffee, dairy products, honey, olive oil and wine are discussed, covering a timespan from 2010 to mid-2020. The literature gathered in this article clearly reveals that the MIR spectroscopy associated with attenuated total reflection acquisition mode and different chemometric tools have been broadly applied to address quality, authenticity and adulteration issues. This technique has the advantages of being simple, fast and easy to use, non-destructive, environmentally friendly and, in the future, it can be applied in routine analyses and official food control.

Improving the Recovery of Phenolic Compounds from Spent Coffee Grounds (SCG) by Environmentally Friendly Extraction Techniques

The aim of this study was to investigate and compare the effects of different extraction techniques (high hydrostatic pressure-assisted extraction (HHPE), ultrasound-assisted extraction (UAE), and classical solvent extraction (CSE)) on phenolic compounds from spent coffee grounds (SCG). Different HHPE parameters (300, 400 and 500 MPa at 25 °C for 5, 10 and 15 min) and UAE parameters (40%, 50%, and 60% amplitude at 25 °C for 5, 10 and 15 min) were used. These techniques were compared with CSE (at 50 °C for 30 min) according to total phenolic content (TPC), antioxidant activity (AA), high-performance liquid chromatography (HPLC), scanning electron microscopy (SEM), and infrared (IR) spectroscopy. The results showed that eco-friendly techniques increased the TPC and AA compared to CSE and morphological changes were verified by SEM results. Furthermore, chlorogenic and caffeic acid were also quantified by using HPLC. Chlorogenic acid was found as the main phenolic compound in spent coffee grounds (SCG). The highest chlorogenic acid was detected as 85.0 ± 0.6 mg/kg FW with UAE at 60% amplitude for 15 min. In brief, for the extraction of phenolic compounds from waste SCG eco-friendly techniques such as HHPE and/or UAE were more convenient than CSE.

First order derivative spectra to determine caffeine and chlorogenic acids in defective and nondefective coffee beans

In this research, the application of the first order derivative spectra was employed to determine the levels of caffeine (CAF) and chlorogenic acids (CGA) in defective (immature, black, and sour) and nondefective coffee beans without using extraction or background correction techniques. The extreme points of first order derivate spectra of these compounds were at the wavelength of 260 and 292 nm enable to quantify the contents of CAF and CGA, respectively. The level of CAF and CGA in coffee beans determined by this method is ranged from 1.2 ± 0.12-1.46 ± 0.47% and 4.04 ± 0.44-4.43 ± 0.43%, respectively. The study results also indicated total contents of CAF and CGA levels discriminate the defective and nondefective coffee beans with higher CAF and CGA contents being observed in defective coffee beans. As the method is extremely rapid, easy, and inexpensive and also requires minimal sample preparation for the quantification of CAF and CGA contents in coffee, it could be a valuable quality control technique.

Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics

Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R²c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry.

Prediction of black, immature and sour defective beans in coffee blends by using Laser-Induced Breakdown Spectroscopy

One of the most important factors that interfere negatively in coffee global quality has been blends with defective beans, especially those called Black, Immature and Sour (BIS). The methods based on visual-manual estimation of defective beans have shown their inefficiency in coffee value chain for large-scale analysis. The lack of fast, accurate and robust analytical methods for BIS determination is still a research gap. Laser-Induced Breakdown Spectroscopy (LIBS) is a fast, low-cost and residue-free technique capable of performing multielemental determination and investigating organic composition of samples. In the present work, LIBS together with spectral processing and variable selection were evaluated to fit linear regression models for predicting BIS in blends. Models showed high capacity of prediction with RMSEP smaller than 3.8% and R² higher than 80%. Most importantly, measurements are guided by chemical responses, which make LIBS-based methods less susceptible to the visual indistinguishability that occurs in manual inspections.

Flavor and Acceptance of Roasted California Almonds During Accelerated Storage

Monitoring oxidative flavor changes in almonds is possible only if the chemical and sensory profile during roasting and storage is first established. Herein, almonds roasted at two different temperatures (115 and 152 °C) were stored at 39 °C for 0 to 12 months and were analyzed by headspace solid-phase microextraction gas chromatography-mass spectrometry, descriptive analysis, and consumer hedonic analysis. Volatile profiles, descriptive sensory profiles, and consumer hedonic scores were analyzed for predictive relationships. Descriptive attributes involving Roasted and Nutty as well as consumer liking were highest in fresh almonds, while flavors typically associated with oxidative rancidity such as Cardboard, Painty/Solvent, Soapy, and Total Oxidized increased during storage. Compounds most important for predicting rancidity-related attributes were lipid oxidation products, including pentanal, hexanal, heptanal, and octanal. Consumer liking was best predicted by similar compounds to those predicting Clean Nutty flavor, including Maillard reaction products such as 2- and 3-methylbutanal, 2-methylpyrazine, and 2,5-dimethylpyrazine.