Evaluation of Industrial Roasting Degree of Coffee Beans by Using an Electronic Nose and a Stepwise Backward Selection of Predictors

Evaluation of roasting and storage conditions as a strategy to improve the sensory characteristics and shelf life of coffee

Coffee is one of the most consumed products worldwide. Among the varieties of this product, specialty coffee is a type of coffee that has been growing in the world market. This paper aims to assess the effects that the conditions derived from coffee roasting at different altitude levels have on the quality of the product. It was discovered that processing coffee at a higher altitude level yielded a smaller increase in bitterness. This led to a better Specialty Coffee Association (SCA) score in cupping and, consequently, to better preservation of the coffee quality. The storage time affected the aroma by associating roaster aromas with older coffees. Although the assessed origins had the same NIR spectra, differences in peak intensity lead to variations in the flavor and aroma of the coffee. Furthermore, although green beans prolong quality allowing a SCA score of 84.73 ± 2.81 after 4 months of storage, roasted coffee at higher altitudes could also maintain the quality between production and consumption (SCA score of 80.22 ± 0.91 after 2 months). Finally, this research found that the instrumental equipment helped to find minor changes in the sensorial profile, and with these changes correlated with the sensorial panel, the best conditions to preserve coffee quality were found.

Functional properties and sensory profile of coffee prepared by different brewing methods

Coffee is one of the main sources of antioxidants in the diet of many countries. The purpose of this research was to assess the effect of different brewing methods, particle size, and coffee quality on the total phenolic content, antioxidant capacity (AC), and sensory profile of the beverage. The brewing methods yielded differences in taste with higher bitterness in immersion methods. However, the main factors that influenced coffee extraction and taste were particle size and coffee type. A finer particle size allowed for greater phenolic and caffeine (CA) extraction (2.82 mg GA/mL; 1.01 mg CA/mL), resulting in higher bitterness and astringency. Additionally, the type of coffee resulted in a higher CA content in commercial coffee (Specialty: 0.72 ± 0.10 mg CA/mL; Commercial: 1.13 ± 0.14 mg CA/mL). The results showed that using a ratio of 1:20 and 1:15 for commercial and specialty coffee, respectively, yielded differences in AC using the DPPH method (Specialty: 11.54 ± 1.12 µmol/mL; Commercial: 10.20 ± 1.88 µmol/mL) but not with the ABTS method (Specialty: 10.38 ± 1.23 µmol/mL; Commercial: 10.37 ± 1.13 µmol/mL). Similarly to the ABTS method, no differences in the total phenol content of the coffee cup were observed (Specialty: 2.52 ± 0.40 mg/mL; Commercial: 2.43 ± 0.28 mg/mL). Thus, the findings suggest that specialty coffee offers consumers a more balanced cup with less CA content. This allows for more coffee consumption without an excessive intake of CA. However, consumers can adjust the functionality, sensory profile, and CA content of a coffee cup by modifying the particle size and the brewing method used.

Karakteristik Fisikokimia dan Kapasitas Antioksidan Kopi Liberika dari Kabupaten Tanjung Jabung Barat, Jambi

Liberica coffee is one of the coffee species in commercial trade in Indonesia. The coffee is produced in Tanjung Jabung Barat Regency, Jambi, Indonesia which distributed into 5 sub-districts (Betara, Bram Itam, Kuala Betara, Pengabuan, Senyerang). Information about liberica coffee from Jambi is still limited, thus more exploration is needed. The objectives of this study were to characterize the morphology of the leaf and fruit, the physicochemical characteristics which include the dimension (length, width, thickness), mass, bulk density, colour (L*, a*, b*), moisture contents, TSS (total soluble solids), pH, and antioxidant capacity (DPPH IC50, FRAP) of green and roasted (commercial level) liberica coffee from the above 5 sub-districts. The studies showed that liberica coffee from 5 sub-districts in Tanjung Jabung Barat Rgency, Jambi had various leaf and fruit appearances which were characterized by various size and colour of coffee cherries. Green coffee from different sub-districts owned various physicochemical (width, volume, mass, bulk density, moisture content, TSS) and antioxidant capacity of green coffee. Green coffee from Betara and Pengabuan were associated with high TSS, L* and b* value, while green coffee from Bram Itam and Senyerang were associated with high mass, moisture content and a* value. The highest anti-oxidant capacity was produced by green coffee from Betara and Kuala Betara (DPPH IC50). Meanwhile, roasted coffee produced from green coffee from the 5 sub-districts with similar roasting level (similar L*) produced similar a*, b* value, mass and TSS. However, physicochemical characteristics (length, width, volume, bulk density, moisture content) and antioxidant capacity of these roasted beans varied.

A comprehensive insight on modern green analyses for quality control determination and processing monitoring in coffee and cocoa seeds

Green analysis is defined as the analysis of chemicals in a manner where sample extraction and analysis are performed with least amounts of steps, low hazardous materials, while maintaining efficiency in terms of analytes detection. Coffee and cocoa represent two of the most popular and valued beverages worldwide in addition to their several products i.e., cocoa butter, chocolates. This study presents a comprehensive overview of green methods used to evaluate cocoa and coffee seeds quality compared to other conventional techniques highlighting advantages and or limitations of each. Green techniques discussed in this review include solid phase microextraction, spectroscopic techniques i.e., infra-red (IR) spectroscopy and nuclear magnetic resonance (NMR) besides, e-tongue and e-nose for detection of flavor. The employment of multivariate data analysis in data interpretation is also highlighted in the context of identifying key components pertinent to specific variety, processing method, and or geographical origin.

VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials

This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2, TiO2 WO3), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4-ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their advantages towards their integration with microsystems for practical applications are also remarked on. The literature survey shows the most successful systems include structured morphologies, particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable "decorative impurities" or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. The work also identifies the need to explore new hybrid material combinations, as well as the convenience of incorporating other transducing principles further than resistive that allow the exploitation of mixed output concepts (e.g., electric, optic, mechanic).

Detection of Lethal Bronzing Disease in Cabbage Palms (Sabal palmetto) Using a Low-Cost Electronic Nose

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO^® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Historical Evolution and Food Control Achievements of Near Infrared Spectroscopy, Electronic Nose, and Electronic Tongue-Critical Overview

Amid today's stringent regulations and rising consumer awareness, failing to meet quality standards often results in health and financial compromises. In the lookout for solutions, the food industry has seen a surge in high-performing systems all along the production chain. By virtue of their wide-range designs, speed, and real-time data processing, the electronic tongue (E-tongue), electronic nose (E-nose), and near infrared (NIR) spectroscopy have been at the forefront of quality control technologies. The instruments have been used to fingerprint food properties and to control food production from farm-to-fork. Coupled with advanced chemometric tools, these high-throughput yet cost-effective tools have shifted the focus away from lengthy and laborious conventional methods. This special issue paper focuses on the historical overview of the instruments and their role in food quality measurements based on defined food matrices from the Codex General Standards. The instruments have been used to detect, classify, and predict adulteration of dairy products, sweeteners, beverages, fruits and vegetables, meat, and fish products. Multiple physico-chemical and sensory parameters of these foods have also been predicted with the instruments in combination with chemometrics. Their inherent potential for speedy, affordable, and reliable measurements makes them a perfect choice for food control. The high sensitivity of the instruments can sometimes be generally challenging due to the influence of environmental conditions, but mathematical correction techniques exist to combat these challenges.

Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree

Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

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.

Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses

The aim of this study is to improve the discrimination performance of electronic noses by introducing a new method for measuring the similarity of the signals obtained from the electronic nose. We constructed abstract odor factor maps (AOFMs) as the characteristic maps of odor samples by decomposition of three-way signal data array of an electronic nose. A similarity measure for two-way data was introduced to evaluate the similarities and differences of AOFMs from different samples. The method was assessed by three types of pipe and powder tobacco samples. Comparisons were made with other techniques based on PCA, SIMCA, PARAFAC and PARAFAC2. The results showed that our method had significant advantages in discriminating odor samples with similar flavors or with high VOCs release.

Characterization of dried and freeze-dried sea fennel (Crithmum maritimum L.) samples with headspace gas-chromatography/mass spectrometry and evaluation of an electronic nose discrimination potential

Processed samples (air-dried @ 40 and @ 60 °C and freeze-dried) of sea fennel (Crithmum maritimum L.), an autochthonous spice with interesting market potential, were analyzed by headspace gas-chromatography/mass spectrometry and classification capabilities of an electronic nose in discriminating between samples with stepwise forward statistics were evaluated as well. Freeze-drying process was the most preservative in terms of limiting darkening without compromising appearance of the final product, providing weight loss of about 85% and water activity below the limit for mold growth issues. Headspace analysis of samples highlighted the presence of 35 volatiles grouped as terpene hydrocarbons, oxygenated terpenes, sesquiterpen hydrocarbons, phenyl propanoids, not-terpenic aldehydes and not-terpenic ketones. Correlations emerged between selected sensors and some detected volatile organic compounds. Stepwise linear discriminant analysis and simple K-nearest neighbors obtained a 100% overall correct classification rate in cross-validation of the electronic nose in classifying samples, whereas stepwise quadratic discriminant analysis and Naive-Bayes gave 93.3%. The sea fennel could be a new interesting spice to launch in the food market and the electronic nose showed the potential to be used in monitoring the industrial process aimed at extending its shelf-life.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR.

Conformal Prediction Based on K-Nearest Neighbors for Discrimination of Ginsengs by a Home-Made Electronic Nose

An estimate on the reliability of prediction in the applications of electronic nose is essential, which has not been paid enough attention. An algorithm framework called conformal prediction is introduced in this work for discriminating different kinds of ginsengs with a home-made electronic nose instrument. Nonconformity measure based on k-nearest neighbors (KNN) is implemented separately as underlying algorithm of conformal prediction. In offline mode, the conformal predictor achieves a classification rate of 84.44% based on 1NN and 80.63% based on 3NN, which is better than that of simple KNN. In addition, it provides an estimate of reliability for each prediction. In online mode, the validity of predictions is guaranteed, which means that the error rate of region predictions never exceeds the significance level set by a user. The potential of this framework for detecting borderline examples and outliers in the application of E-nose is also investigated. The result shows that conformal prediction is a promising framework for the application of electronic nose to make predictions with reliability and validity.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.