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The quality and uniformity of green coffee beans significantly influence the overall flavor and value of the product. In the coffee industry, automated flaws and bean-type identification offer numerous advantages. This study examines the effectiveness of multiple YOLO (You Only Look Once) models for identifying and classifying green coffee beans. Various YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and custom models, are compared with a focus on computational efficiency, accuracy, and speed. Utilizing a dataset of 4,032 training and 506 testing images encompassing diverse bean types, defects, and lighting conditions, we assessed the performance. The bounding boxes generated by our models accurately encompass coffee beans, with the background typically uniform and set to black. Our analysis reveals the superior performance of the custom-YOLOv8n model, which we meticulously customized for green coffee bean detection. This model achieved high precision, recall, f1-score, and mAP, demonstrating its potential for real-world implementation in coffee bean quality control systems. The customization process involved fine-tuning the model to focus on significant features relevant to green coffee bean detection and employing specific labeling strategies. Customization allows you to fine-tune the model to focus on important features relevant to green coffee bean detection. This sensitivity ensures that the model can effectively distinguish between different bean types and detect even subtle defects. This paper clarifies our primary objective of evaluating YOLO models' performance in identifying and categorizing green coffee beans, with potential implications for enhancing efficiency and consistency in the coffee industry. A succinct key sentence underscores the benefits of our research for readers seeking efficient YOLO model selection and implementation in agricultural systems. [ABSTRACT FROM AUTHOR]

Abstract The quality and uniformity of green coffee beans significantly influence the overall flavor and value of the product. In the coffee industry, automated flaws and bean-type identification offer numerous advantages. This study examines the effectiveness of multiple YOLO (You Only Look Once) models for identifying and classifying green coffee beans. Various YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8, and custom models, are compared with a focus on computational efficiency, accuracy, and speed. Utilizing a dataset of 4,032 training and 506 testing images encompassing diverse bean types, defects, and lighting conditions, we assessed the performance. The bounding boxes generated by our models accurately encompass coffee beans, with the background typically uniform and set to black. Our analysis reveals the superior performance of the custom-YOLOv8n model, which we meticulously customized for green coffee bean detection. This model achieved high precision, recall, f1-score, and mAP, demonstrating its potential for real-world implementation in coffee bean quality control systems. The customization process involved fine-tuning the model to focus on significant features relevant to green coffee bean detection and employing specific labeling strategies. Customization allows you to fine-tune the model to focus on important features relevant to green coffee bean detection. This sensitivity ensures that the model can effectively distinguish between different bean types and detect even subtle defects. This paper clarifies our primary objective of evaluating YOLO models’ performance in identifying and categorizing green coffee beans, with potential implications for enhancing efficiency and consistency in the coffee industry. A succinct key sentence underscores the benefits of our research for readers seeking efficient YOLO model selection and implementation in agricultural systems.

Coffee is one of the most consumed beverages worldwide, recognized for its unique taste and aroma and for its social and health impacts. Coffee contains a plethora of nutritional and bioactive components, whose content can vary depending on their origin, processing, and extraction methods. Gathered evidence in literature shows that the regular coffee consumption containing functional compounds (e.g., polysaccharides, phenolic compounds, and melanoidins) can have potential beneficial effects on cardiometabolic risk factors such as abdominal adiposity, hyperglycemia, and lipogenesis. On the other hand, coffee compounds, such as caffeine, diterpenes, and advanced glycation end products, may be considered a risk for cardiometabolic health. The present comprehensive review provides up‐to‐date knowledge on the structure–function relationships between different chemical compounds present in coffee, one of the most prevalent beverages present in human diet, and cardiometabolic health. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Coffee is a widely consumed beverage, and sorting coffee beans is a critical process that ensures high-quality graded coffee products. Coffee beans were graded into nine grades in robusta types. To automate the grading process, a deep learning-based approach was developed using a large dataset of highresolution images and data augmentation techniques. In contrast to previous studies focusing on robusta type graded into six coffee bean grads, our research extends this framework by employing robusta type into nine grades with an outperformed accuracy. The proposed work uses four deep learning models, namely residual network 34(ResNet34), inception version 3 (Inception v3), efficient network Bayesian optimization (EfficientNet-B0), and visual geometry group-16 (VGG-16), where trained and evaluated for coffee bean classification into nine grades. The EfficientNet-B0 model exhibited outperformed accuracy, achieving 100% in distinguishing good and bad coffee beans, even in challenging lighting and background conditions. [ABSTRACT FROM AUTHOR]

Arabica, Robusta, and Liberica are the three main coffee species cultivated globally. Liberica coffee is a minor species, accounting for less than 1% of global cultivation. Due to favorable climatic conditions in Malaysia, Liberica coffee dominates coffee production, accounting for 73%, while Robusta makes up the remaining 27%. Nevertheless, the substantial coffee production resulted in approximately 15 million tons of discarded skin and pulp, contributing to environmental pollution. This study was conducted due to insufficient information and research on the proximate composition and phytochemical compounds of the coffee bean and pulp from Liberica sp. This study aims to determine the proximate composition of coffee beans and pulp extracts from Liberica sp. and to identify the phytochemical composition using liquid chromatography-mass spectrometry (LC-MS) analysis. The nutritional values of carbohydrates, protein, crude fiber, crude fat, and ash were obtained using proximate analysis. Coffee beans exhibited the highest value for crude protein (11.96%) and crude fiber (11.83%), whereas coffee pulp has the highest significant value for moisture content (68.81%) and ash (7.31%). LC-MS analysis shows emmotin A and deoxymiroestrol were the major phytochemical compounds. These findings contribute to understanding the nutritional value and phytochemical compounds of coffee beans and pulp from Liberica sp. that may contribute to sustainable waste management and other applications in the food and beverage industry. [ABSTRACT FROM AUTHOR]

In order to assess the impact of each of the factors that affect the quality and uniformity of grinding coffee beans and to compare the impact of these factors, it is worth establishing a quantitative indicator of this impact. To solve this problem, dispersion analysis was used as a method of organizing sample data according to possible sources of dispersion. The chosen method made it possible to decompose the total dispersion into components caused by the influence of factor levels. Grinding time, geometric dimensions of the grain, moisture content of the grain, speed of rotation of the motor shaft were selected as factors influencing the homogeneity of grinding. The justification and assessment of the reliability of statistical conclusions about the informational significance of indicators affecting the homogeneity of coffee grinding was carried out to ensure the highest possible probability of the obtained result. [ABSTRACT FROM AUTHOR]

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

This study aimed to evaluate the efficacy of the coffee bean residue extract (ECBR) in promoting hair growth in human hair follicle dermal papilla cells (HFDPCs). Increased viability was observed in ECBR-treated cells that was almost equivalent to that in the cells treated with minoxidil, a positive control. Moreover, significant increase in ATP production, related to cell proliferation, was observed in ECBR-treated cells, suggesting its hair growth-promoting effect. Further analysis of the mechanisms underlying the hair growth-promoting effects of ECBR using microarrays revealed a significant increase in various autophagy-related biological processes in HFDPCs. Additionally, ECBR significantly increased the expression levels of the autophagy-associated factors, vascular endothelial growth factor A, and autophagy-related (ATG)-2, ATG5, ATG12, ATG14, and ATG16L1, in HFDPCs by real-time PCR. Our findings revealed that ECBR promoted the proliferation of HFDPCs via the activation of autophagy, suggesting its potential for the treatment of hair growth disorders and hair loss.

In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification. [ABSTRACT FROM AUTHOR]

Objective: Extraction of coffee beanusing supercritical carbon dioxide (SC-CO2) was investigated using Taguchi method to provide information about the optimum extraction condition. Methods: L9 Taguchi orthogonal array design was used to determine the optimum extraction condition. Effects of extraction pressure and temperature and moisture content of material on oil yield were investigated at levels ranging between 250-350 bar, 60-80°C and 20-40% w.b., respectively. Results: According to Taguchi method, the highest oil yield from SC-CO2 extraction was obtained at a pressure of 350 bar, temperature of 80°C and coffee bean's moisture content of 30% w.b. Based on S/N ratio calculation and ANOVA, the most influencing parameters in maximizing coffee bean oil yield were moisture content, followed by extraction temperature and pressure. Conclusion: In this study, L9 array of Taguchi Method was successfully applied to optimize the SC-CO2 extraction process on coffee bean. [ABSTRACT FROM AUTHOR]

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans. [ABSTRACT FROM AUTHOR]

The method of roasting coffee beans was invented by the Arabs of Yemen, who then spread the method to Egypt and Turkey, and finally worldwide. This paper provides a detailed understanding of coffee beans from a chemical perspective, including discussions on identification of two coffee bean varieties--Arabica and Robusta, the chemical process of coffee roasting, and the pore structure of coffee beans. [ABSTRACT FROM AUTHOR]

Civet coffee is one of the world's most popular and expensive coffee types. The problem recently is the difficulty of finding natural civet coffee. The study of artificial civet coffee production using microorganisms to ferment the coffee in recent years has attracted the attention of many researchers. This study aims to characterize and identify yeast from civets and evaluate its potential as a starter in coffee fermentation. Three types of yeast isolated from civet feces have been isolated and characterized. They have varied morphological and biochemical characteristics. Only two yeast isolates Kh1 and Kh2 have the potential to be used as starters in fermentation due to the ability to degrade pectin and protein. The 18S rRNA gene fragment analysis results indicated that the isolates Kh1 and Kh2 were identified as Trichosporon asahii isolate E22922_ITS and Wickerhamomyces anomalus CNRMA10.1139. Applying both isolates as starters in coffee fermentation can improve the quality of coffee flavor and produce a higher score. Fermented coffee using W. anomalus starter provides more flavor, aftertaste, body, and overall advantages, while coffee fermentation using T. asahii isolate gives more value to the aroma. The highest cupping score was obtained for processed coffee using W. anomalus, reaching 84.42. Furthermore, it is necessary to carry out a more complex metabolite analysis to ensure the performance of the two isolates in modifying the chemical composition and taste of coffee. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The thermophysical properties of coffee have a special partaking during the drying process since a material‐depending heat and mass transfer occurs between the bean and the drying air. Conditional to the thermophysical properties of the parchment coffee, the drying can be more or less efficient, affecting the final quality and seed safety. Several coffee varieties have been studied; however, the National Coffee Research Center of Colombia has developed new highly productive coffee varieties resistant to different diseases: Cenicafé 1 and Castillo®. Nevertheless, the thermophysical properties of these specific varieties were not yet investigated; moreover, the availability of information related to these properties of different coffee varieties in the literature is relatively scarce. Thus, this study targeted to determine the parchment coffee thermophysical properties of these new varieties at five different moisture contents % (wb): 53%, 42%, 32%, 22% and 11%, using optimized techniques and methods to ensure high accuracy and exactness. It was found that the new varieties have larger, heavier, and denser beans; it was also seen that the bulk thermal conductivity and the bulk‐specific heat are higher in these varieties than in the older ones. It was also revealed that the length, width, thickness, and surface area did not change as the moisture was removed, whereas the bulk density, kernel density, mass, bulk‐specific heat, and bulk thermal conductivity decreased as the moisture was reduced. Displaying better thermophysical properties will improve the drying and roasting processes; hence, a better final product can be expected from these varieties. Practical applications: Knowing the thermal and physical properties of these new varieties will allow the growers and coffee processing facilities to predict, simulate and control different post‐harvesting processes such as pulping, fermentation, drying, storing, and roasting. Also, the already developed mathematical models to estimate coffee drying times can be updated, improving the accuracy of the predictions, bed porosities, mass, and heat transfer in order to safeguard the innocuousness of the product. [ABSTRACT FROM AUTHOR]

In this study, terahertz time-domain spectroscopy (THz-TDS) was proposed to identify coffee of three different varieties and three different roasting degrees of one variety. Principal component analysis (PCA) was applied to extract features from frequency-domain spectral data, and the extracted features were used for classification prediction through linear discrimination (LD), support vector machine (SVM), naive Bayes (NB), and k-nearest neighbors (KNN). The classification effect and misclassification of the model were analyzed via confusion matrix. The coffee varieties, namely Catimor, Typica 1, and Typica 2, under the condition of shallow drying were used for comparative tests. The LD classification model combined with PCA had the best effect of dimension reduction classification, while the speed and accuracy reached 20 ms and 100%, respectively. The LD model was found with the highest speed (25 ms) and accuracy (100%) by comparing the classification results of Typica 1 for three different roasting degrees. The coffee bean quality detection method based on THz-TDS combined with a modeling analysis method had a higher accuracy, faster speed, and simpler operation, and it is expected to become an effective detection method in coffee identification.

This study aims to determine the design of the roasting machine according to the needs of small business (IKM), IKM Republik Tani Mandiri is a small businesses engaged in processing coffee beans in Kucur Village. This research is focused on increasing production capacity by designing a roaster machine. Research methods The method used is Quality Function Deployment (QFD) phase 1 and product planning and development methods in determining the design of the roasting machine according to the needs of IKM. The calculation of the QFD method that has been carried out will be used to determine the concept of a roasting machine design with product planning and development methods. The design concept can be identified as follows: a gas burner, a rectangular engine cover, a manual temperature gauge, a 3-5kg tube volume, and a two-way dynamo. In the design of the roasting machine, it can be seen that in one roasting cycle with a capacity of 5 kg it takes 40 minutes, while the old machine takes 210 minutes, resulting in a 525% increase in capacity.

Background. For developing countries such as Ethiopia, coffee is a commodity of great economic, social, and environmental importance. No detailed investigations have been performed on the contents of essential and toxic metals in coffee beans and soil in this study area. Methods. The levels of essential metals (Na, K, Ca, Zn, Mn, Cu, Co, Cr, Ni) and toxic elements (Pb and Cd) were investigated in coffee beans (coffee growing farmland and coffee washed plants) and soil samples (from farmland) using flame atomic absorption spectrometry (FAAS) and flame emission atomic spectroscopy. We selected six (20%) administrative units (kebele) with purposive sampling techniques based on their coffee production capacity in Dale Woreda for soil testing. After coffee sample preparation in a microwave system with HNO3and H2O2 reagents, the accuracy of the optimized procedure was evaluated by analysing the digest of the spiked samples. Soil samples were abridged with a slight revision of the EPA 3050B acid digesting method. ANOVA was used to determine the significant differences in the mean concentration of metal within coffee beans from farmland at the various sampled sites at the p<0:05 significance level. To correlate the effect of one metal concentration on other metals in the coffee bean samples, Pearson correlation matrices were used. Results. Calcium had the highest concentration (1,355 ± 18.02 mg kg-1) of macro-elements in soil samples, followed by K (681.43 ± 1.52 mg kg-1). Similarly, Na (111.63 ± 0.35 mg kg-1), Cu (49.96 ± 0.99 mg kg-1), Co (5.43 ± 0.31 mg kg-1), Mn (0.62 ± 0.238 mg kg-1), Ni (0.194 ± 0.01 mg kg-1), and Zn (0.163 ± 0.007 mg kg-1) were detected among the microelements in the soil samples. Pb and Cr were not detected in all soil samples. Potassium (K) was found to have the highest concentration (99.93±0.037 mg kg-1), followed by Ca (17.23±0.36 mg kg-1), among the macroelements in coffee beans from farmers' farms. Similar to coffee beans from farmland, samples from washed plants also contained the highest K (77.93 ± 0.115 mg kg-1), followed by Ca (4.33±0.035 mg kg-1). Metal levels in coffee bean samples from farmland are in the following order: K>Na>Ca >Mn>Cu> Ni>Zn. Metal levels were found to be K>Na>Ca>Mn>Cu>Zn>Ni in coffee beans from the washed plants. Co, Cr, Pb and Cd were no detected in all coffee bean samples. Except for calcium, potassium and manganese, the levels of metals in coffee beans from farmland and washed plants were not significantly different at the 95% confidence level within a kebele. Conclusions. We observed permitted levels of macro- and trace elements in coffee beans from farmlands and washed plants. Only in the soil samples are cadmium concentrations higher than those permitted for agricultural soil recommended by the WHO and FAO. Overall, there is no health danger linked with the use of coffee beans due to detrimental and trace heavy metals. [ABSTRACT FROM AUTHOR]

ABSTRAK Ukuran gilingan biji dan teknik penyeduhan kopi merupakan penentu kualitas seduhan kopi. Tujuan penelitian ini adalah untuk mengetahui pengaruh perbedaan ukuran gilingan (grind size) terhadap kualitas seduhan kopi arabika batumirah dengan teknik vietnam drip. Pada penelitian ini dilakukan metode eksperimen untuk menentukan hasil seduhan yang berkualitas paling baik dari seduhan kopi arabika batumirah teknik vietnam drip dengan jenis ukuran biji kopi gilingan kasar, sedang dan halus. Penelitian dilakukan dengan mengidentifikasi alat vietnam drip, menentukan suhu air, uji coba seduhan dengan rasio 1:10, 1:12 dan 1:15 dan dilanjutkan dengan uji coba organoleptik dengan tiga jenis ukuran gilingan berbeda kepada 30 panelis yang meliputi aspek aroma, flavor, aftertaste, acidity, body dan balance. Dari uji organoleptik diperoleh hasil bahwa perbedaan ukuran gilingan berpengaruh terhadap kualitas seduhan kopi arabika batumirah pada aspek acidity, body dan balance. Kata kunci: Arabika, Biji Kopi, Kualitas, Organoleptik ABSTRACT The size of the ground bean and the brewing technique are determinants of the quality of brewing coffee. The aims of this study to determine the effect of differences in grind size on the quality of Batumirah Arabica coffee brewed by the Vietnam drip technique. Experimental method was used in this study to determine the best quality of brewing Arabica Batumirah coffee with the Vietnam drip technique with coarse, medium and finely ground coffee bean sizes. The research was conducted by identifying the Vietnam drip device, determining the water temperature, brewing trials with a ratio of 1:10, 1:12 and 1:15, continued with organoleptic trials with three different mill sizes to 30 panelists involve aspects of aroma, flavor, aftertaste, acidity, body and balance. From the organoleptic test, it was found that the differences of grind size affected the quality of brewing Arabica Batumirah coffee on the acidity, body and balance aspect. Keywords: Arabica, Coffee bean, Organoleptics, Quality

The influence of the roasting process on the mechanical and elastic properties of coffee has not been systematically investigated yet, although they change substantially. This is due to the initial drying of the beans under water release, the following structural changes at increasing temperature, the onset of chemical transformations and the release of gases and volatile ingredients. The resulting cellular structures determine the roasting result in a significant way. As measuring the elastic bean properties during roasting is easily and reproducibly possible, informative relationships to other roasting parameters can be established. It is thus possible to carry out an accompanying and up-to-date roasting control, which currently takes place via the color perception of the roasting master only. The measured values provide access to further information on changes in the internal bean structure and the processes during roasting. Elasticity measurements can therefore trace the roasting progress down to the depth of the beans, without being limited to the surface information of the color measurement as actually practiced. [ABSTRACT FROM AUTHOR]

Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries.

Background For developing countries such as Ethiopia, coffee is a commodity of great economic, social, and environmental importance. No detailed investigations have been performed on the contents of essential and toxic metals in coffee beans and soil in this study area. Methods The levels of essential metals (Na, K, Ca, Zn, Mn, Cu, Co, Cr, Ni) and toxic elements (Pb and Cd) were investigated in coffee beans (coffee growing farmland and coffee washed plants) and soil samples (from farmland) using flame atomic absorption spectrometry (FAAS) and flame emission atomic spectroscopy. We selected six (20%) administrative units (kebele) with purposive sampling techniques based on their coffee production capacity in Dale Woreda for soil testing. After coffee sample preparation in a microwave system with HNO3and H2O2 reagents, the accuracy of the optimized procedure was evaluated by analysing the digest of the spiked samples. Soil samples were abridged with a slight revision of the EPA 3050B acid digesting method. ANOVA was used to determine the significant differences in the mean concentration of metal within coffee beans from farmland at the various sampled sites at the p Na>Ca >Mn>Cu> Ni>Zn. Metal levels were found to be K>Na>Ca >Mn>Cu> Zn>Ni in coffee beans from the washed plants. Co, Cr, Pb and Cd were no detected in all coffee bean samples. Except for calcium, potassium and manganese, the levels of metals in coffee beans from farmland and washed plants were not significantly different at the 95% confidence level within a kebele. Conclusions We observed permitted levels of macro- and trace elements in coffee beans from farmlands and washed plants. Only in the soil samples are cadmium concentrations higher than those permitted for agricultural soil recommended by the WHO and FAO. Overall, there is no health danger linked with the use of coffee beans due to detrimental and trace heavy metals.

Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.

The bioactive ingredients of green coffee beans were extracted using high pressure-assisted extraction (HPE) and compared against those derived from the traditional heat reflux (HR) extraction method. The effect of extraction conditions on the extraction efficiency of functional components (chlorogenic acids, caffeine, total phenols, and flavonoids); the free radical-scavenging activity of the extract; and the inhibition of α-glucosidase, α-amylase, and food pathogenic bacteria were measured. Using water as the solvent, the high-pressure extraction rates of chlorogenic acid (CGA) and caffeine were 3.20–5.75 mg/100 g and 4.32–7.95 mg/100 g, respectively, which were significantly higher than the rates obtained using the traditional method (2.92 mg/100 g and 3.54 mg/100 g, respectively). Under optimal extraction conditions of 25 °C and 600 MPa for 2.5 min, inhibition of α-glucosidase and α-amylase by the extract reached rates of 27.8% and 26.7%, respectively, with the highest free radical-scavenging activity of 81.5%. HPE exhibited antibacterial activity against both gram-positive (Staphylococcus aureus and Listeria innocua) and gram-negative (Escherichia coli and Salmonella enterica) bacteria, and the lowest minimum inhibitory concentration (4.69 mg/mL) against S. aureus was achieved. Scanning electron microscopic observations evidenced that high pressure partially damaged the intercellular compartments of coffee beans, increased solvent permeability, and improved the extraction rate of bioactive ingredients. The outcomes of the study demonstrated that HPE can be employed as an efficient extraction technique for production of chlorogenic acids and caffeine that might have a potential application in food and related industries. [ABSTRACT FROM AUTHOR]

Background: Gingivectomy is a periodontal surgical procedure that eliminates excess gingival tissues by excision, usually to remove a gingival pocket which leads to scars and recontours the gingival to its proper anatomical form. Wound healing is a process to restore the missing functions and structures of a tissue due to injuries or inflammations. Robusta coffee bean contains many bioactive compounds that can accelerate the wound healing process. The study aimed to determinate the effectiveness of robusta coffee bean extract gel with concentration 40%,50%,60% on collagen fibers density in post-gingivectomy wound healing Method: 36 Wistar rats were divided into 9 groups: control, gingivectomy without therapy, gingivectomy +40% robusta coffee bean extract gel, gingivectomy +50% robusta coffee bean extract gel, and gingivectomy+60% robusta coffee bean extract gel. The last 4 groups were divided again into day 3 and 7 euthanasia. The results were processed by Trichrome Mallory to visualized the collagen fibers and then measured by ImageJ software. Result: Robusta coffee beans extract gel therapy with concentration 40%, 50% and 60% had higher average collagen fibers density compared to group without therapy. Conclusion: Robusta coffee bean extract gel increased the density of collagen fibers in post-gingivectomy wound healing

[Display omitted] • ECBR-treatment showed the significantly increase of cell viability and ATP production in HFDPCs. • Microarray analysis revealed that ECBR induce the increase of Autophagy-related biological process and KEGG pathway. • PPI network analysis showed that ECBR regulate cell cycle and apoptosis, which related hair growth cycle. • ECBR-treatment increased the expression levels of the autophagy-associated factors, VEGFA and ATG- related genes, in HFDPCs. This study aimed to evaluate the efficacy of the coffee bean residue extract (ECBR) in promoting hair growth in human hair follicle dermal papilla cells (HFDPCs). Increased viability was observed in ECBR-treated cells that was almost equivalent to that in the cells treated with minoxidil, a positive control. Moreover, significant increase in ATP production, related to cell proliferation, was observed in ECBR-treated cells, suggesting its hair growth-promoting effect. Further analysis of the mechanisms underlying the hair growth-promoting effects of ECBR using microarrays revealed a significant increase in various autophagy-related biological processes in HFDPCs. Additionally, ECBR significantly increased the expression levels of the autophagy-associated factors, vascular endothelial growth factor A, and autophagy-related (ATG)- 2 , ATG5 , ATG12 , ATG14 , and ATG16L1 , in HFDPCs by real-time PCR. Our findings revealed that ECBR promoted the proliferation of HFDPCs via the activation of autophagy, suggesting its potential for the treatment of hair growth disorders and hair loss. [ABSTRACT FROM AUTHOR]

Coffee is one of the world's most valuable, traded, and widely consumed beverages. As one of the processing steps of coffee, fermentation has gained the attention of researchers as it can be employed to improve coffee quality. The current review presents the latest knowledge regarding the application of fermentation in the coffee industry, including fermenting coffee beans to produce new materials and converting coffee by-products into value-added foods. Fermentation using various microorganisms has been employed to diversify the production of unique food materials from green coffee beans. These products are considered superior to unfermented coffee bean-derrived ones. On the other hand, by-products of coffee processing offer an opportunity to produce microbial metabolites with applications in food. Research indicates that fermentation of coffee by-products shows excellent potential for producing value-added beverages, enzymes, organic acids, and oligosaccharides. As a result, the fermentation process of coffee beans and by-products produces valuable food materials with improved health benefits.Examples of such foods include probiotic coffee, instant coffee with low toxicants, speciality coffee, and coffee wine. Consequently, fermentationhas not only improved the quality of coffee beans but has also offered a viable way to manage waste problems, aiming to create an eco-friendly environment. [Display omitted] • Fermentation improves bioactivity and bioactive compounds of coffee beans. • Probiotic brews, wine, specialty coffee and instant coffee are novel fermented coffee bean products. • Fermentation can bio-transform coffee bean by-products into useful materials. • Enzymes, ethanol, and organic acids are coffee wastes-derived products with industrial applications. • More studies are needed to optimize fermentation conditions for coffee bean. [ABSTRACT FROM AUTHOR]

The present work evaluated the effect of microwave roasting on total polyphenol content (TPC), total flavonoid content (TFC), 2,2-diphenyl-1-picrylhyrazyl (DPPH) radical scavenging activity, some selected compounds, and the mineral content of coffee beans. Coffee bean powder was roasted at three microwave power levels (450, 720, and 900 W) and treatment durations (4, 6, and 8 min). The TPC, TFC, and DPPH radical scavenging activity were increased by increasing the microwave power and roasting duration, but detrimental effects were observed at higher power levels and longer treatment durations. The highest TPC, TFC, and DPPH radical scavenging activity were detected for the sample treated at 720 W for 6 min. The mineral content was only increased in the sample treated at 450 W for 4 min; all other treatments decreased the mineral content. Microwave power levels and treatment durations showed a significant increase in the browning intensity of the coffee bean extract. The selected coffee bean compounds as analysed by GC-MS were affected in different ways by microwave treatment. The relative percentage of caffeine was increased from 40.06 to 49.12% when treated at 450 W for 4 min, while n-hexadecanoic acid content was decreased from 33.86% in untreated coffee beans to 16.31% when treated at 450 W for 4 min. There was also the formation of new compounds such as octadecanoic acid-methyl ester, vitamin E, and stigmasterol upon microwave roasting of coffee beans. Based on the above results, microwave heating can be used as a roasting method for coffee beans. [ABSTRACT FROM AUTHOR]

Introduction. Coffee plantations are one of the growing agriculture sub-sectors and a high coffee production should be based on proper processing and marketing. Objective. To analyze the updates of products, processes, functions, and upgrading of the robusta coffee value chain in East Java. Materials and methods. This study was conducted in the Robusta coffee plantations in the Bondowoso, Jember, Lumajang, Pasuruan, Malang, and Ngawi districts in East Java, Indonesia, between October 2019 and March 2020. The quota sampling technique was used, followed by surveys and interviews. The number of respondents was 120 people who were actors in the coffee value chain, such as farmers, coffee cherry collectors, coffee cherry grinders, green bean grinders, and consumers. Results. The results showed that product, function, and channel upgrading had a significant effect on the coffee value chain, but the upgrading process did not significantly affect it in several regions on East Java. Conclusions. There is a need for innovation to enhance the coffee value chain in order to deliver future benefits to all stakeholders in this market.

Peaberries are a special type of coffee bean with an oval shape. Peaberries are not considered defective, but separating peaberries is important to make the shapes of the remaining beans uniform for roasting evenly. The separation of peaberries and normal coffee beans increases the value of both peaberries and normal coffee beans in the market. However, it is difficult to sort peaberries from normal beans using existing commercial sorting machines because of their similarities. In previous studies, we have shown the availability of image processing and machine learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest-neighbors (KNNs), for the classification of peaberries and normal beans using a powerful desktop PC. As the next step, assuming the use of our system in the least developed countries, this study was performed to examine their implementation in and the limitations of Raspberry Pi 3. To improve the performance, we modified the CNN architecture from our previous studies. As a result, we found that the CNN model outperformed both linear SVM and KNN on the use of Raspberry Pi 3. For instance, the trained CNN could classify approximately 13.77 coffee bean images per second with 98.19% accuracy of the classification with 64×64 pixel color images on Raspberry Pi 3. There were limitations of Raspberry Pi 3 for linear SVM and KNN on the use of large image sizes because of the system's small RAM size. Generally, the linear SVM and KNN were faster than the CNN with small image sizes, but we could not obtain better results with both the linear SVM and KNN than the CNN in terms of the classification accuracy. Our results suggest that the combination of the CNN and Raspberry Pi 3 holds the promise of inexpensive peaberries and a normal bean sorting system for the least developed countries. [ABSTRACT FROM AUTHOR]

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740-1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The detection of coffee bean defects is the most crucial step prior to bean roasting. Existing defect detection methods used in the specialty coffee bean industry entail manual screening and sorting, require substantial human resources, and are not standardized. To solve these problems, this study developed a deep learning algorithm to detect defects in coffee beans. The results reveal that when the pooling layer was used to enhance features and reduce neural dimensionality, some of the coffee been features were lost or misclassified. Therefore, a novel dimensionality reduction method was adopted to increase the ability of feature extraction. The developed model also overcame the drawbacks of padding causing blurred image boundaries and the dead neurons causing impeding feature propagation. Images of eight types of coffee beans were used to train and test the proposed detection model. The proposed method was verified to reduce the bias when classifying defects in coffee beans. The detection accuracy rate of the proposed model was 95.2%. When the model was only used to detect the presence of defects, the accuracy rate increased to 100%. Thus, the proposed model is highly accurate in coffee bean defect detection in the classification of eight types of coffee beans. [ABSTRACT FROM AUTHOR]

This study aimed to inactivate Aspergillus flavus on green coffee beans using organic acid vapor treatment. Green coffee beans inoculated with A. flavus spores (approximately 5.2–5.9 log cfu/5 beans) were treated with vapor (100 mg/L) generated from acetic acid, formic acid, or propionic acid at 25 °C or 50 °C and relative humidity (RH) of 43 % or 100 % for up to 6 h. Exposure to acetic acid or propionic acid vapor for 6 h decreased the number of A. flavus on the beans by 1.2–3.1 or 0.5–3.3 log cfu/5 beans, respectively. Treating green coffee beans with formic acid vapor for 6 h reduced number of A. flavus spores to below the detection limit (1.7 log cfu/5 beans), regardless of temperature and RH. Formic acid treatment did not alter the color values (L *, a* , and b* values) of the beans; however, it significantly decreased the pH. Mold did not grow on beans treated with formic acid vapor during storage, whereas untreated beans and those treated with acetic acid or propionic acid showed visible mold after 5 days. The treatment of organic acid vapor significantly reduced A. flavus populations on the beans and high temperatures or RH synergistically enhanced mold inactivation effect of organic acid vapor. • Organic acid vapor prevented growth of A. flavus at 12 °C or 25 °C. • Mold did not grow on coffee beans treated with organic acid vapor during storage. • Organic acid vapor has a minor impact on the color and pH of green coffee beans. • Organic acid vapor successfully enhanced the microbial safety of coffee beans. [ABSTRACT FROM AUTHOR]

The coffee phytochemical trigonelline and its roasting products nicotinic acid and N -methylpyridinium represent an important class of bioactives that mediate the health benefits of coffee consumption. However, there is a paucity of methods for their simultaneous analysis in coffee beans, especially those affordable and reliable for widespread routine use. To improve this situation, an easy-to-use HPLC−UV method was developed in this study based on a reversed-phase/cation-exchange mixed-mode column showing unique chromatographic selectivity toward the polar and ionic analytes over the matrix components of coffee. The selectivity was achieved by using a decreasing acetonitrile gradient, which tuned the column to a cation-exchange/hydrophilic interaction (CEX/HILIC) mixed separation mode whilst minimizing its hydrophobic interaction capability. The method was assessed for figures of merit, giving limits of detection in coffee beans at around the 0.1 mg/100 g wet matter level, and verified for its applicability to a heterogeneous set of coffee bean samples differing in roasting degree and origin. [Display omitted] • Retention mechanism on the column is selectable between CEX/HILIC and CEX/RP. • CEX/HILIC mixed mode offers selectivity for polar ionic analytes over coffee matrix. • The method proved its applicability to green and roasted coffee with low LOD. • Strong negative correlation existed between trigonelline and the two roast products. [ABSTRACT FROM AUTHOR]

Coffee is the most traded commodity after petroleum. The Vietnamese coffee bean industry has raised concerns lately over an inefficient coffee value chain; bets on coffee price uncertainty are increasing worldwide in the current. Accurate optimization of coffee bean prices helps manufacturers to control an unpredictable market and upgrade cooperativeness in sustainable agriculture. The authors proposed a forecasting method to deal with demand volatility and uncertainty in volumes and coffee bean prices. In this paper, we applied the forecasting nonlinear grey Bernoulli model (NGBM) (1,1). NGBM (1,1), which is based on the parameter optimization algorithm, can increase the precision of predictions. NGBM (1,1) was integrated with Fourier residual modification model to forecast coffee bean price, which was a crucial factor in the Vietnamese coffee bean supply chain. The price of coffee beans was calculated using a differential equation in an uncertain system, along with actual data collected over the past six years. The results of this study demonstrate that an integrated forecasting model is an effective forecasting method. This research can help companies to control risks that come with uncertain coffee prices and reduce risks in the sustainable agriculture supply chain. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the defects, moisture content, flavor, and overall acceptance of Robusta coffee from 20 coffee producers in Tanggamus Regency. Taste testing involved some expert panelists. The data were analyzed descriptively. The results showed that as much as 70% of coffee quality was following SNI 01-2907-2008. The defect value of coffee was at the quality level of 5 until the inferior original quality (“kopi asalan”). The main percentages of the defects were broken seeds, brown beans, black seeds and then followed by hollow seeds. The water content of the coffee was varied between 5.07-7.27. The taste of coffee was dominated by green/grassy, the average taste was slightly bitter, the colors ranged from brown, black and cinnamon-like color, while, the overall acceptance was somewhat like. This results indicated that standard operating procedure is required to be implemented by coffee producers (IKM) in Tanggamus Regency to improve the quality of the coffee.

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