Discrimination and geographical origin prediction of washed specialty Bourbon coffee from different coffee growing areas in Rwanda by using electronic nose and electronic tongue

Rapid classification of coffee origin by combining mass spectrometry analysis of coffee aroma with deep learning

Mislabeling the geographical origin of coffee is a prevalent form of fraud. In this study, a rapid, nondestructive, and high-throughput method combining mass spectrometry (MS) analysis and intelligence algorithms to classify coffee origin was developed. Specifically, volatile compounds in coffee aroma were detected using self-aspiration corona discharge ionization mass spectrometry (SACDI-MS), and the acquired MS data were processed using a customized deep learning algorithm to perform origin authentication automatically. To facilitate high-throughput analysis, an air curtain sampling device was designed and coupled with SACDI-MS to prevent volatile mixing and signal overlap. An accuracy of 99.78% was achieved in the classification of coffee samples from six origins at a throughput of 1 s per sample. The proposed approach may be effective in preventing coffee fraud owing to its straightforward operation, rapidity, and high accuracy and thus benefit consumers.

Impact of coffee and its bioactive compounds on the risks of type 2 diabetes and its complications: A comprehensive review

BACKGROUND

Coffee beans have a long history of use as traditional medicine by various indigenous people. Recent focus has been given to the health benefits of coffee beans and its bioactive compounds. Research on the bioactivities, applications, and effects of processing methods on coffee beans' phytochemical composition and activities has been conducted extensively. The current review attempts to provide an update on the biological effects of coffee on type 2 diabetes (T2D) and its comorbidities.

METHODS

Comprehensive literature search was carried out on peer-reviewed published data on biological activities of coffee on in vitro, in vivo and epidemiological research results published from January 2015 to December 2022, using online databases such as PubMed, Google Scholar and ScienceDirect for our searches.

RESULTS

The main findings were: firstly, coffee may contribute to the prevention of oxidative stress and T2D-related illnesses such as cardiovascular disease, retinopathy, obesity, and metabolic syndrome; secondly, consuming up to 400 mg/day (1-4 cups per day) of coffee is associated with lower risks of T2D; thirdly, caffeine consumed between 0.5 and 4 h before a meal may inhibit acute metabolic rate; and finally, both caffeinated and decaffeinated coffee are associated with reducing the risks of T2D.

CONCLUSION

Available evidence indicates that long-term consumption of coffee is associated with decreased risk of T2D and its complications as well as decreased body weight. This has been attributed to the consumption of coffee with the abundance of bioactive chemicals.

GC-MS Combined with Fast GC E-Nose for the Analysis of Volatile Components of Chamomile (Matricaria chamomilla L.)

Chamomile has become one of the world's most popular herbal teas due to its unique properties. Chamomile is widely used in dietary supplements, cosmetics, and herbal products. This study aimed to investigate the volatile aromatic components in chamomile. Two analytical techniques, gas chromatography-mass spectrometry (GC-MS) and an ultra-fast gas chromatography electronic nose, were employed to examine samples from Xinjiang (XJ), Shandong (SD), and Hebei (HB) in China, and imported samples from Germany (GER). The results revealed that all chamomile samples contained specific sesquiterpene compounds, including α-bisabolol, bisabolol oxide, bisabolone oxide, and chamazulene. Additionally, forty potential aroma components were identified by the electronic nose. The primary odor components of chamomile were characterized by fruity and spicy notes. The primary differences in the components of chamomile oil were identified as (E)-β-farnesene, chamazulene, α-bisabolol oxide B, spathulenol and α-bisabolone oxide A. Significant differences in aroma compounds included geosmin, butanoic acid, 2-butene, norfuraneol, γ-terpinene. This study demonstrates that GC-MS and the ultra-fast gas chromatography electronic nose can preliminarily distinguish chamomile from different areas, providing a method and guidance for the selection of origin and sensory evaluation of chamomile. The current study is limited by the sample size and it provides preliminary conclusions. Future studies with a larger sample size are warranted to further improve these findings.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

Juice Quality Evaluation with Multisensor Systems-A Review

E-nose and e-tongue are advanced technologies that allow for the fast and precise analysis of smells and flavours using special sensors. Both technologies are widely used, especially in the food industry, where they are implemented, e.g., for identifying ingredients and product quality, detecting contamination, and assessing their stability and shelf life. Therefore, the aim of this article is to provide a comprehensive review of the application of e-nose and e-tongue in various industries, focusing in particular on the use of these technologies in the fruit and vegetable juice industry. For this purpose, an analysis of research carried out worldwide over the last five years, concerning the possibility of using the considered multisensory systems to test the quality and taste and aroma profiles of juices is included. In addition, the review contains a brief characterization of these innovative devices through information such as their origin, mode of operation, types, advantages and disadvantages, challenges and perspectives, as well as the possibility of their applications in other industries besides the juice industry.

The potential of different ripeness of blood oranges (Citrus sinensis L. Osbeck) for sale in advance after low-temperature storage: Anthocyanin enhancements, volatile compounds, and taste attributes

To explore the optimal early harvest time similar to the ripe fruit qualities, the effects of storage temperatures on maturity indexes, weight losses, colour parameters, anthocyanin profiles, volatile and taste components of blood oranges at six different maturity levels were investigated. Total anthocyanin contents of cold-treated fruits increased to or exceed that of ripe fruits (0.24 ± 0.12 mg/100 g), and fruits harvested from 260 d and 280 d after anthesis shared similar individual anthocyanin profiles to ripe fruits during storage at 8 °C for 30 d and 20 d (III-30 d and IV-20 d groups), respectively. Moreover, comparative analyses of e-nose and e-tongue demonstrated the distances of volatile components and scores of taste attributes including sourness, saltiness, bitterness, sweetness, and umami in III-30 d and IV-20 d groups were close to that of ripe fruits, indicating that the fruits could be sold about 20 to 30 d ahead of the season.

The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies

Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.

Coffee consumption, health benefits and side effects: a narrative review and update for dietitians and nutritionists

Coffee is one of the most popular beverages worldwide; however, its impact on health outcomes and adverse effects is not fully understood. The current review aims to establish an update about the benefits of coffee consumption on health outcomes highlighting its side effects, and finally coming up with an attempt to provide some recommendations on its doses. A literature review using the PubMed/Medline database was carried out and the data were summarized by applying a narrative approach using the available evidence based on the literature. The main findings were the following: first, coffee may contribute to the prevention of inflammatory and oxidative stress-related diseases, such as obesity, metabolic syndrome and type 2 diabetes; second, coffee consumption seems to be associated with a lower incidence of several types of cancer and with a reduction in the risk of all-cause mortality; finally, the consumption of up to 400 mg/day (1-4 cups per day) of caffeine is safe. However, the time gap between coffee consumption and some drugs should be taken into account in order to avoid interaction. However, most of the data were based on cross-sectional or/and observational studies highlighting an association of coffee intake and health outcomes; thus, randomized controlled studies are needed in order to identify a causality link.

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.

Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods

Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.

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.

Potential use of electronic tongue coupled with chemometrics analysis for early detection of the spoilage of Zygosaccharomyces rouxii in apple juice

Apple juice spoilage by Zygosaccharomyces rouxii could scarcely be identified at early stage. It is crucial to recognize the spoilage at early stage to prevent waste of products. In present study, electronic tongue was applied to detect the spoilage of Z. rouxii in apple juice, using taste evaluation by panelists as reference. Combined with linear discriminant analysis, identification of the contaminated juice was fulfilled after 12 h, equivalent to yeast population of less than 2.0 lg colony forming units/mL. At the level, panelists were not capable of discerning the spoilage. Sensors HA, ZZ, BB and BA were relatively more sensitive to the changes in overall taste of apple juice. Moreover, cell number of Z. rouxii could be properly quantified by partial least squares regression models with high determination coefficient of 0.98-0.99. Electronic tongue appears to be a powerful approach to realize early detection of contamination of Z. rouxii.