Coffee Fruits Research Articles

Effectiveness of Urea Molasses Multi-Nutrient Block Based on Fermented Coffee Fruit Peel on the Blood Profile of Sheep Experiencing Stress during Transportation

Transportation stress is influenced by trip duration, mileage, vehicle type, road conditions, loading and unloading, vehicle speed and environmental conditions. The provision of Urea Molasses Multi-Nutrient Block (UMMB) based on fermented coffee peel aims to overcome nutritional deficiencies and stress in livestock. This study was conducted experimentally with a non-factorial complete randomized design (RAL) with 4 treatments of A0 (without UMMB) as a control, A1 (UMMB based on fermented coffee peel 10%), A2 (UMMB based on fermented coffee peel 20%), A3 (UMMB based on fermented coffee peel 30%) and 4 replications. The parameters of the study were glucose levels, total protein and creatinine. The results showed that giving UMMB peel-based fermented coffee has a significant effect (P<0.05) on increasing glucose levels (16.03%), decreasing total protein levels (31.49%), and decreasing creatinine levels (2.71%). It can be concluded that giving UMMB based on fermented coffee peel up to 30% can overcome stress in sheep that experience transportation for 8 hours.

Addition of commercial lactic acid bacteria as starter cultures in anaerobic fermentations of Coffea arabica L., in cherry or raisin stages

The objective of this research was to verify the efficiency of adding commercial lactic acid bacteria (LAB) on anaerobic fermentation of (cherry and raisin) arabica Yellow Catuaí coffees, studying the microbiological succession and its influence in modulating sensorial characteristics on their beverages. The study took place in a rural property in Marília region, Sao Paulo State, Brasil, and was performed according to three different treatments: 1) FNC = spontaneous fermentation of cherry coffees; 2) FCBAL = cherry coffee fermentation using Sacco Lyofast SYAB1 BAL; 3) FPBAL = raisin coffee fermentation using Sacco Lyofast SYAB1 BAL. Fermentations occurred in individual (60 liters) high density polyethylene containers, properly sealed to guarantee anaerobic conditions. After a 48-hour fermentation, all the coffee fruits were washed, beans were depulped and dried on suspended terrace until a 12% humidity was observed. Physical, chemical, microbiological and sensory analysis were performed in samples collected from each fermented batch. Fermentation processes of raisin coffees occurred more slowly than fermentations using cherry coffees and resulted in a lesser consumption of soluble solids and in a slower acidification. Fermentation treatments FNC, FCBAL and FPBAL reached the necessary reduction of Enterobateriaceae and molds. The addition of BAL stimulated the growth of yeasts, which are responsible for the inclusion of the most desired flavor. The fermentations conducted in this experiment resulted in coffee beverages that reached notes of specialty coffees (> 80 points). Even though the raisin coffee achieved a lower note than cherry, the LAB inclusion raised it from 77 (non-fermented raisins) to 80 (FPBAL) points. In cherry fruits, FCBAL got 2 points on final comparation to non-fermented cherries. Thus, the inclusion of LAB presented itself beneficial to anaerobic fermentations of cherry or raisin coffees.

MORPHOLOGYOF OLFACTORIORGAN OFCOMMON PALMCIVET (PARADOXURUS HERMAPHRODITUS)

Common Palm Civet (Paradoxurus hermaphroditus) has a very sharp sense of smell that is a tool for selecting ripe and good quality coffee fruit. This research was aimed to study the morphological characteristics of the olfactory organ of Common palm civet macroscopically and microscopically. In this study, three adult Common palm civet were used. Based on macroscopic observations, Common palm civet had olfactory organs consisting of cavum nasi, nostril, septum nasi, conchae and vomeronasale organs found in the nasale. In the dextra and sinistra cavum nasi, there were conchae (superior, medius and inferior). In the medius and inferior conchae, there a respiratory epithelium in the superior conchae, there was the olfactory epithelium. Hematoxylin-Eosin (HE) staining was used for microscopic observation. Based on microscopic observations, the olfactory epithelium composed of sustentacular cells (supporting cells), receptor cells and basal cells. The respiratory epithelium composed of goblet cells, pseudostratified epithelium and basal cells. The vomeronasale epithelium consists of the sensory and non-sensory epithelium. The sensory epithelium covered the ventral two thirds of the medial side of the vomeronasale, while the non-sensory epithelium covered the lateral side and the dorsal thirded of the medial side of the vomeronasale. The epithelium sensory vomeronasale consisted of three types of cells: supporting, receptors, and basal. Non-sensoryepithelium composed of the pseudostratified epithelium and goblet cells

Genetic diversity of the fungal community that contributes to the sensory quality of coffee beverage after carbonic maceration and fermentation.

Understanding the effects of microorganisms on coffee fermentation is crucial to ensure sensory quality and food security. The analysis of the dynamics of the microbial community during fermentation can contribute to a better understanding of the beneficial and harmful effects of microorganisms and help select starter cultures to improve coffee quality. Furthermore, the anaerobic environment produced by carbonic maceration of the coffee fruits inhibits aerobic respiratory processes and stimulates fermentative metabolism, modulating the microbial community during coffee fermentation. This study evaluated the effects of carbonic maceration in the fungal community dynamics during the fermentation of Coffea arabica fruits at 18, 28, and 38°C for 24, 48, 72, 96, and 120h. Fungal diversity was accompanied by high-throughput sequencing (NGS) of the Internal Transcribed Spacer (ITS) region. During the coffee fermentation, the fungal community changed over time, with the most significant changes occurring at 18 and 28°C after 72h. However, at 38°C, there were greater variations in fungal composition and fungal diversity was highest after 120h. The yeast Pichia cephalocereana was predominant in the fermentations. These results indicated that temperature and fermentation conditions influence the fungal community during coffee fermentation. Lower temperatures might favor a more stable microbial environment, while higher temperatures lead to more intense changes. Thus, our data from NGS can help in the identification, isolation, and metabolic characterization of fungi for the fermentation of coffee fruits.

Implementation of Smart Farm Systems Based on Fog Computing in Artificial Intelligence of Things Environments.

Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue. This study implements an Artificial Intelligence of Things (AIoT) system based on fog computing on a smart farm. Three experiments are conducted to evaluate the performance of the AIoT system. First, network traffic volumes between systems employing and not employing fog computing are compared. Second, the performance of the communication protocols-hypertext transport protocol (HTTP), message queuing telemetry transport protocol (MQTT), and constrained application protocol (CoAP)-commonly used in IoT applications is assessed. Finally, a convolutional neural network-based algorithm is introduced to determine the maturity level of coffee tree images. Experimental data are collected over ten days from a coffee tree farm in the Republic of Korea. Notably, the fog computing system demonstrates a 26% reduction in the cumulative data volume compared with a non-fog system. MQTT exhibits stable results in terms of the data volume and loss rate. Additionally, the maturity level determination algorithm performed on coffee fruits provides reliable results.

Integrated Metabolomics and Transcriptomics Analyses Reveal the Regulatory Mechanisms of Anthocyanin and Carotenoid Accumulation in the Peel of Coffea arabica.

The color of coffee fruits is influenced by several factors, including cultivar, ripening stage, and metabolite composition. However, the metabolic accumulation of pigments and the molecular mechanisms underlying peel coloration during the ripening process of Coffea arabica L. remain relatively understudied. In this study, UPLC-MS/MS-based metabolomics and RNA sequencing (RNA-seq)-based transcriptomics were integrated to investigate the accumulation of anthocyanins and carotenoids in the peel of Coffea arabica at different ripening stages: green peel (GP), green-yellow peel (GYRP), red peel (RP), and red-purple peel (RPP). This integration aimed at elucidating the molecular mechanisms associated with these changes. A total of ten anthocyanins, six carotenoids, and thirty-five xanthophylls were identified throughout the ripening process. The results demonstrated a gradual decrease in the total carotenoid content in the peel with fruit maturation, while anthocyanin content increased significantly. Notably, the accumulation of specific anthocyanins was closely associated with the transition of peel colors from green to red. Integrated metabolomics and transcriptomics analyses identified the GYRP stage as critical for this color transition. A weighted gene co-expression network analysis (WGCNA) revealed that enzyme-coding genes such as 3AT, BZ1, and lcyE, along with transcription factors including MYB, NAC, and bHLH, which interact with PHD and SET TR, may regulate the biosynthesis of anthocyanins and carotenoids, thereby influencing peel pigmentation. These findings provide valuable insights into the molecular mechanisms underlying the accumulation of anthocyanins and carotenoids in Coffea arabica peel during fruit maturation.

Isolation and Identification of Acid-Forming Bacteria in Arabica Coffee Fruit (Coffea arabica) During Wet Fermentation

The distinctive flavor and aroma possessed by Arabica coffee are an attraction for coffee enthusiasts. Post-harvest processing of coffee such as coffee fruit fermentation also contributes to the formation of the flavor and aroma of Arabica coffee. In this study, spontaneous fermentation was conducted by adding distilled water, with treatments on fermentation days 4 and 7 aimed at detecting acid-forming bacteria that played a role in the fermentation process. The bacterial isolates were characterized through macroscopic and microscopic observations, physiological, and biochemical tests. Identification of bacterial isolates was performed using the profile matching method based on Bergey’s Manual of Systematic Bacteriology (Holt et al., 1994) and Axellson (2004) until the genus level. The identified bacterial genera were Asaia, Gluconobacter, Neoasaia, Saccharibacter, and Lactobacillus.

Microbiota of arabica coffee: insights from soil to fruit.

Studies have shown that a diverse and metabolically active microbiota exists throughout different stages of coffee processing, from pre- to post-harvest. This microbiota originates from both the cultivation and processing environments. Additionally, microorganisms from the soil can be found on the fruit due to the transfer between them. This study reviews the microbiota present in Arabica coffee fruits and the soils where the plants are grown. It examines how microbial profiles are related to coffee variety, altitude, cultivation region, and processing method, and establishes a connection between the microbiota in soil and fruit. A diverse microbiota was observed in both coffee fruits and soils, with similar microorganisms identified across different growing regions, processing methods, and coffee varieties. However, exclusive detections of some microorganisms were also observed. These differences highlight the influence of terroir on coffee's microbial composition, confirming that environmental conditions, genetic factors, and processing methods shape coffee microbiota. Since microbial development during coffee fermentation can affect the beverage's quality, the data presented in this review offer valuable insights for researchers and producers. Understanding the influence of processing methods, coffee varieties, and cultivation regions on coffee microbiota enables the selection of specific fermentation conditions or starter cultures to enhance terroir characteristics or adjust microbial populations to favor or introduce microorganisms beneficial for coffee quality.

Neem-Extract Formulation onHypothenemus hampeiPreference and Performance in Arabica Coffee Fruits and Artificial Diet.

Coffee berry borer (CBB) Hypothenemus hampei is a major biotic threat to coffee production worldwide. Studies have reported negative effects on CBB by oil-based formulations of neem (Azadirachta indica), but little information is available for other neem-extract formulations. This study evaluated CBB preference and performance in arabica coffee fruits and artificial diet treated with a neem-extract formulation (Openeem Plus®) in the field and laboratory conditions. Field experiments were performed using CBB females artificially infested in cherry or green coffee fruits confined in voile-fabric cages tied to branches of neem-treated and control plants, recording the adult mortality and offspring production. Dual-choice and no-choice bioassays assessed CBB preference and development in fruits and artificial diet treated with the neem extract compared to controls in the laboratory, respectively. As main results obtained in the field and laboratory experiments, the neem extract significantly reduced CBB oviposition in both cherry and green fruits, as well as in artificial diet compared to controls. However, the botanical product did not affect CBB adult survival and preference in the laboratory bioassays. The neem extract is promising for use in pest management strategies in sustainable arabica coffee crops by reducing CBB oviposition and offspring. These effects can contribute to lowering the pest population buildup along the crop cycle and damage potential to coffee production.

Bioconversion of Coffee Fruit Bark Waste into Commercially Valued, Healthy, and High-Quality Compost Fertilizer as a Solution for Community Economic Independence and Environmental Sustainability

Bioconversion of Coffee Fruit Bark Waste into Commercially Valued, Healthy, and High-Quality Compost Fertilizer as a Solution for Community Economic Independence and Environmental Sustainability

Machine learning models for classifying coffee fruits detachment force

AbstractThe maturation process of coffee (Coffea arabica) trees exhibits inherent variability, producing fruits at various physiological maturity stages. This variability affects the resistance between the fruit and its peduncle, posing a challenge in mechanized harvesting: non‐selective harvesting. A precise classification of coffee fruit detachment force is essential to address this challenge, ensuring coffee's quality and producer's profitability. This study assesses the efficacy of machine learning (ML) models in determining the detachment force across various coffee cultivars under drip‐irrigated and rainfed conditions. The dataset included detachment force measurements from 24 cultivars—13 drip‐irrigated and 11 rainfed—yielding 1152 data points. Variance analysis compared irrigation methods and three maturity stages: green, cherry, and dry. Detachment force was categorized into four classes based on the dataset's quartile distribution. The ML models utilized were random forest (RF), support vector machine (SVM), K‐nearest neighbors, and artificial neural networks. The SVM model was notably effective in classifying detachment force for rainfed cultivars, with a Matthews correlation coefficient (MCC) of 0.78. In contrast, the RF model was particularly adept for drip‐irrigated cultivars, with an MCC of 0.75. The highest classification accuracies were recorded for the extreme force classes I and IV, with precision values of 0.93 and 0.8, respectively, while classes II and III had lower precision at 0.57 and 0.69. Implementing these ML models for detachment force classification has been beneficial, improving decision‐making in mechanized harvesting systems.

Variation in Soluble Sugars in Arabica Coffee Cherry Fruits.

The maturation of Arabica coffee fruits is influenced by both endogenous and external factors. The stage of fruit maturation affects the chemical composition of the beans, which in turn impacts the quality of the coffee beverage. During maturation, the fruit peel changes colour from green to red (cherry), signalling the optimal harvest time and suggesting high fruit quality. However, the degree of redness can vary, indicating different levels of maturity. This study aimed to explore the variation in soluble sugar accumulation in relation to the redness of coffee fruit tissues. We classified ripe fruits into six ripeness categories based on the intensity of the red colour of the epicarp, measured using a colourimeter. We analysed total soluble sugar, sucrose, and starch in three parts: coat (exocarp + mesocarp), coat juice (obtained by squeezing the coat), and beans. Our findings reveal that the variation in sugar in the endosperm does not correspond to changes in the coat, suggesting separate regulation of sugar accumulation, particularly sucrose, which is crucial for coffee quality. Our data indicate that there is no transfer of sucrose and reducing sugars from the red coat to the bean.

Extension of Beauveria bassiana the Effectiveness As a Coffee Fruit Borer Pest Control in Ngrayudan Village, Ngawi Regency

The development of coffee cultivation in Ngrayudan Village, Ngawi Regency has experienced a decline in quality due to the attack of the Coffee Fruit Borer pest. The pest causes damage to the coffee fruit. Based on the results of the identification of the area potential, there is a problem regarding the handling of Coffee Fruit Borer pests that had not been maximized by farmers in Ngrayudan Village. Farmers do not have the knowledge and skills to use biological agents as solutions. This study aims to design extension and behavior changes related to farmers' knowledge, attitudes, and skills. This research was conducted in February - March 2024 using a qualitative approach by using the level III action research method which has four stages in extension activities including planning, action, observation, and reflection. Based on observations and interviews with respondents after counseling showed significant changes in behavior. Supported by quantitative data from the extension evaluation results which showed an increase in knowledge of 34%, while the attitude level showed results of 89% including in the category of responsibility in deciding a matter, and a skill level of 80% including in the level of problem-solving farmers can describe problems to find the best solution. The conclusion of this study shows the existence of effective extension in improving behavior change in Farmer Groups in making biological agents such as Coffee Fruit Borer pest control.

Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method

The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.

Coffee Fruit Rot: The Previously Unrecognized Role of Fusarium and Its Interactions with the Coffee Berry Borer (Hypothenemus hampei).

Coffee fruit rot (CFR) is a well-known disease worldwide, mainly caused by Colletotrichum spp., the most important species being C. kahawae subsp. kahawae. In Puerto Rico, Colletotrichum spp. were identified as pathogens of coffee fruits. The coffee berry borer (CBB) was shown to be a dispersal agent of these fungi, and interaction of Fusarium with Colletotrichum affecting coffee fruits was suggested. In this study, we demonstrated that Fusarium spp. also cause CFR in Puerto Rico. Fusarium spp. are part of the CBB mycobiota, and this insect is responsible for spreading the pathogens in coffee fields. We identified nine Fusarium spp. (F. nirenbergiae, F. bostrycoides, F. crassum, F. hengyangense, F.solani-melongenae, F.pseudocircinatum, F. meridionale, F. concolor, and F. lateritium) belonging to six Fusarium species complexes isolated from CBBs and from rotten coffee fruits. Pathogenicity tests showed that F.bostrycoides, F. lateritium, F. nirenbergiae, F. solani-melongenae, and F.pseudocircinatum were pathogens causing CFR on green coffee fruits. F.bostrycoides was the predominant species isolated from the CBB mycobiota and coffee fruits with symptoms of CFR, suggesting a close relationship between F. bostrycoides and the CBB. To our knowledge, this is the first report of F. bostrycoides, F. solani-melongenae, F. pseudocircinatum, and F.nirenbergiae causing CFR worldwide and the first report of F. lateritium causing CFR in Puerto Rico. Understanding the CFR disease complex and how the CBB contributes to dispersing different Fusarium spp. on coffee farms is important to implement disease management practices in Puerto Rico and in other coffee-producing countries.

Rancang Bangun Alat Pemilah Biji Kopi Berdasarkan Kualitas Buah Menggunakan Sensor TCS3200

Coffee fruit is a trade commodity that has been known for centuries. Coffee fruit can be processed into a delicious drink, but when coffee farmers harvest at the same time, farmers combine immature and ripe coffee fruit in one container, this results in a decrease in the quality of the taste. significant. In this research, the fuzzy Sugeno method was used to determine the classification of ripe, semi-ripe and ripe coffee fruit. This tool uses an Arduino Uno Atmega328 microcontroller to control the TCS3200 color sensor as a reader for the color of the coffee fruit and uses a servo to sort the coffee fruit according to quality. In testing the automatic control system for separating coffee beans based on color using the TCS3200 Color Sensor, it was successful in directing the coffee beans to a predetermined container. Keywords— TCS3200 Sensor, Arduino ATmega328P,Coffee,Color,Quality,Fuzzy sugeno.

DETERMINATION OF COFFEE FRUIT MATURITY LEVEL USING IMAGE HISTOGRAM AND K-NEAREST NEIGHBOR

Coffee has a very important role in the Indonesian economy, as one of the country's foreign exchange contributors in the plantation sector. Therefore, coffee processing is very important in determining the quality of coffee. The procedure for choosing and evaluating the coffee fruit's physical quality is one of the most crucial steps. The step of determining the maturity level of coffee fruit is carried out using the image histogram and K-Nearest Neighbor (KNN) method. This research uses the KNN algorithm with classification stages that will show the level of accuracy value according to the value of k = 5 used when processing the classification of coffee fruit image data. In order to complete this step, the features of the coffee fruit are identified using its color. The qualities of quality coffee fruit, which is flawlessly red in color. Twenty images total—ten of which are of ripe coffee fruit and ten of which are of raw coffee fruit—were used in this study. The test results were carried out using rapidminer tools using 40% training data and 60% testing data from the total data set. Based on the test results, it gives an accuracy value of 100%, meaning that the data set can be used in the next stage as valid data to be used.

Optimizing Harvesting Efficiency: Development and Assessment of a Pneumatic Air Jet Excitation Nozzle for Delicate Biostructures in Food Processing.

This study presents a new pneumatic air jet excitation nozzle, specifically designed for food processing applications. The device, which uses compressed air equipment and a precision solenoid valve, controls air discharge through a parametric air jet nozzle. Tests showed that the device could achieve shooting frequencies in the 40-45 Hz range, with operational pressures between 5 and 7 bar. A sensor system was used to measure the force generated by the device at different frequencies and pressures. Using the Design of Experiments (DOE) methodology, we identified optimal cavity designs for 5 and 6 bar pressures. These designs outperformed others in generating uniform force and maintaining consistent vibration voltage behavior. This highlights the efficacy of our approach in enhancing device performance under different conditions. The device's practical application in food processing was demonstrated, particularly in delicate tasks such as the selective harvesting of sensitive crops like coffee fruits. The precise vibrations generated by the device could potentially enhance harvesting efficiency while significantly reducing mechanical damage to plants. The results position the device as a compelling proof of concept, offering an alternative method for exciting biostructures in food processing. This device opens up new possibilities in agricultural and biological fields, providing a non-intrusive and practical approach to manipulating and interacting with delicate, contactless structures, with a specific focus on improving food processing efficiency and quality.

Preference of hypothenemus hampei for coffee fruits according to state of maturation and infestation

The coffee berry borer Hypothenemus hampei (Ferrari) remains the most important coffee pest worldwide. Coffee fruits emit a mixture of volatiles, which function as insect attractants. Y-tube olfactometry tests allowed us to determine insect response to coffee fruits in different developmental stages: green, underripe, mature-ripe and overripe. The ripe fruits showed the highest levels of insect attraction, up to 30% more than the other stages. Additionally, insect attraction to fruits previously infested with H. hampei for 3 and 24 h vs. noninfested fruits was tested. The infested and noninfested ripe fruits showed the same levels of attraction. The overripe and green fruits infested for 3 h showed 12% greater insect attraction. The green fruits infested for 24 h showed 20% more attraction. When evaluating insect attraction to the methanol:ethanol alcohol mixture vs. the fruits, the alcohols showed greater insect attraction than all developmental stages except the ripe fruits. The understanding of H. hampei behavioral characteristics and preferences for coffee fruits in different developmental and infestation stages will allow the design of better insect control strategies.

Impact of immature coffee fruits and water addition during spontaneous fermentation process: Chemical composition and sensory profile

BackgroundCoffee fermentation process influences the final coffee composition and the sensory aspects which define the quality of the beverage. In this study, coffee fruits underwent spontaneous self-induced anaerobic fermentation using samples with two percentages of immature fruits in submerged and solid-state processing. The effects on the physicochemical composition and sensory quality of coffees were evaluated. ResultsThe two percentages of immature fruits corresponded to 11.0 and 0.3% of unripe fruits. The percentage of immature fruits significantly altered the initial content of sugars (sucrose, glucose, and fructose), ash, and titratable acidity. The water addition during the fermentative process did not significantly influence final moisture, proteins, citric acid, and propionic acid concentrations. Compared to the solid-state, the submerged process gave rise to coffees with lower concentrations of ethanol, glycerol, ash, lipids, succinic, and acetic acids. Coffee fermented with 0.3% of immature fruits showed higher lactic acid production in submerged fermentation (67.44 mg/g), and higher concentrations of ethanol (42.84 mg/g) and glycerol (1.68 mg/g) in solid-state fermentation. All coffees produced were classified as specialty coffees with a score above 84 points. However, the submerged fermented coffee with 11% immature fruit stood out with notes of caramel, brown sugar, honey, orange, lemon, floral, nut, yellow and red fruits. ConclusionsThis study confirmed that spontaneous fermentation can be used to produce specialty coffees. Differentiation in sensory attributes can be achieved through the addition of water and varying the percentage of green fruits during the fermentation process. Up to 11% of immature fruits did not compromise coffee quality.How to cite: Ferreira LJC, Casé IN, Bertarini PLL, et al. Impact of immature coffee fruits and water addition during spontaneous fermentation process: Chemical composition and sensory profile. Electron J Biotechnol 2024;69. https://doi.org/10.1016/j.ejbt.2024.04.001.