Types Of Beans Research Articles

Chocolates Produced with Unroasted and Roasted Cocoa Beans: A Comparative Study of the Preservation of Bioactive Compounds

The aim of the present study was to evaluate the effect of roasting and cocoa mass content on the composition of chocolates. Chocolate formulations were developed (40, 50, 60 and 70% cocoa solids) using unroasted and roasted cocoa beans, and analyses were performed: physicochemical, chemical composition, antioxidant capacity, procyanidin content, total phenolic compounds, quantification of theobromine, caffeine, catechin and epicatechin by high performance liquid chromatography (HPLC) and spectroscopic profile by mid-infrared spectroscopy (MIR). The results revealed that higher values of total phenolics, epicatechin and procyanidins, were obtained in chocolates produced with unroasted cocoa beans, with a significant difference in relation to those with roasted cocoa beans. It was observed that increasing the percentage of cocoa in the formulations caused an increase in the content of bioactive compounds. The use of MIR in the differentiation of chocolate samples produced with roasted and unroasted cocoa beans obtained a positive result. This technique associated with principal component analysis (PCA) enabled the separation of chocolates according to the type of cocoa bean (roasted and unroasted) used in production, confirming its importance for a quick evaluation of the parameters.

An AI-powered e-nose system using a density-based clustering method for identifying adulteration in specialty coffees

Coffee is a valuable commodity with diverse varieties and unique specifications, particularly in Indonesia, a coffee-producing country. Ensured classification and quality of coffee throughout the supply chain are crucial for industry stakeholders, particularly in terms of flavor and aroma. This study focuses on the authenticity of coffee, particularly specialized coffee beans from microclimates that may be impacted by climate change. Advanced technologies, such as electronic nose (E-Nose) systems paired with machine learning techniques, play a significant role in achieving accurate and efficient authentication. Furthermore, the research addresses adulteration issues, such as blending different types of coffee beans, which can discourage farmers from participating in coffee cultivation. In this study, we develop an authentication method leveraging the olfactory characteristics of coffee aroma, emphasizing the unique interplay between microclimate conditions and flavor profiles. The clustering method of density-based spatial clustering of applications with noise (DBSCAN) technique is used to authenticate tropical climate specialty coffee and recognize adulterated specialty coffee beans. In our case study, adulteration in Arabica and Luwak specialty coffees was successfully detected using the DBSCAN technique combined with dimensionality reduction methods. This work has demonstrated a novel method for identifying specialty coffee authenticity and adulteration using the E-Nose system paired with machine learning techniques.

Recognizing the Types of Beans Using Artificial Intelligence

Many studies have previously addressed the recognition of plant leaf types. The process of identifying these leaf types involves a crucial feature extraction stage. Image feature extraction is pivotal for distinguishing the types of objects, thus demanding optimal feature analysis for accurate leaf type determination. Prior research, which employed the CNN method, faced challenges in effectively distinguishing between long bean and green bean leaves when identifying bean leaves. Therefore, there is a need to conduct optimal feature analysis to correctly classify bean leaves. In our research, we analyzed 69 features and explored their correlations within various image types, including RGB, L*a*b, HSV, grayscale, and binary images. The primary objective of this study is to pinpoint the features most strongly correlated with the recognition of bean leaf types, specifically green bean, soybeans, long beans, and peanuts. Our dataset, sourced from farmers' fields and verified by experienced senior farmers, consists of 456 images. The most highly correlated feature within the bean leaf image category is STD b in the L*a*b image. Furthermore, the most effective method for leaf type recognition is Neural Network Backpropagation, achieving an accuracy rate of 82.28% when applied to HSV images.

Progression of Bacterial Blight of Clusterbean (Cyamopsis tetragonoloba) in Relation to Different Sowing Dates, Varieties and Weather Parameters

At CCS Haryana Agricultural University, Hisar, Haryana, a field study was conducted to investigate the impact of varying planting timings, varieties, and weather parameters on the advancement of bacterial blight in clusterbean. The experiment was set up in three replications using a factorial randomized block design. Three types of cluster beans, namely HG 365, HG 563, and HG 2-20, were sowed at three distinct dates i.e., the 1st, 3rd, and 4th week of July. Clusterbean bacterial blight spread faster in late-planted crops (4th week of July) than in timely-planted (2nd week of July) and early-planted (1st week of July). Maximum temperature (Tmax) and maximum relative humidity (RHm) correlated positively, while minimum temperature (Tmin) and minimum relative humidity (RHe) correlated negatively, and disease development was extremely significant and negatively correlated for HG 365 at all sowing dates. Clusterbean HG 365 was shown to be more resistant to bacterial blight than HG 563 and HG 2-20.

“RS Centenário”: New cultivar of brindle bean with high added value

To increase productivity, new types of beans that present different characteristics, such as higher grain yieldare necessary. The cultivar RS Centenário is the result of a selection carried out in 2007 using the “Iraí” cultivar, in a plot of genetic seed production. In the 2008 crop season, the seeds of the selected plants originated a segregant population. In 2009, the mass selection was carried out again. After two advancing generations, the phenotypic uniformity of the population was reached and the seeds were harvested in bulk. After several tests, the “SM 0112” lineage was denominated cultivar “RS Centenário” and the results of the assessed at a value for cultivation and use (VCU) trials were used to register the new cultivar. “RS Centenário” was registered as a new cultivar to be commercially grown in the Rio Grande do Sul state (RS), in southern Brazil. “RS Centenário” is a common bean (Phaseolus vulgaris L.) cultivar with a brindle seed coat, average weight of 1,000 seeds of 343 g, erect plants, indeterminate growth habit II/III, cycle of 88 days, high added value and average yield of 1,685 kg ha-1.

Genetic grouping of selected RILs for yield and attributing traits in determinate type of Indian bean

Genetic grouping of selected RILs for yield and attributing traits in determinate type of Indian bean

Fluoride content of common legume beans consumed in Ethiopia

ABSTRACT. The most widely used legume beans food in Ethiopia are fava beans (Vicia faba), green gram (Vigna radiata), white lupin (Lupinus albus), and three types of kidney beans (Phaseolus vulgaris). These legume crops are grown in various regions of Ethiopia and can absorb significant amounts of fluoride from air, water, and soil. This study examined the fluoride content of legume beans collected from four different regions of Ethiopia by ion selective electrode potentiometry. Fluoride concentrations in this study were found to range from 1.9 to 22.8 mg/kg. Fava bean sample from Asella was found to contain higher fluoride (22.8 mg/kg) than in the fava beans from other sites and other beans from any site. Fluoride levels (13.0 mg/kg) in kidney bean from Migira was in the middle of the range while the fluoride levels (2.5 mg/kg) in the white lupin and green gram (1.9 mg/kg) were found in the lower end of the range. This study indicates that daily consumption of 100 g of fava and kidney beans may results in health problem while that of green gram and white lupin are safe for human health (will not exceed 3 mg/day set by Food and Nutrition Board of USA).
 KEY WORDS: Fava bean, Green gram, Kidney beans, White lupin, Food, Fluoride
 Bull. Chem. Soc. Ethiop. 2023, 37(6), 1325-1336. DOI: https://dx.doi.org/10.4314/bcse.v37i6.3

Local Legumes from Dry Land of East Nusa Tenggara: Diversity, Nutritional Composition, and Their Use in Society – A Literature Study

East Nusa Tenggara (ENT) is a province in Indonesia with diverse flora, including the diversity of local legumes. It has been extensively explored and reported in various scientific publications, especially the types of legumes, their nutritional composition, and their uses in local communities. However, few literature studies still comprehensively review the types of legumes, their nutritional composition, and their utilization in the local community of ENT. So, this literature review aims to briefly review the diversity, chemical composition, and use of local legumes from the dry lands of ENT. Various studies reported that Glycine max, Arachis hypogaea, Vigna radiata (L.) R. Wilczek, Cajanus cajan, Vigna unguiculata, Phaseolus lunatus, Vigna umbellata, Lablab purpureus (L.) Sweet, Mucuna pruriens (L.) DC, Phaseolus vulgaris L, Psopocarpus tetragonolobus (L.) DC, and Pueraria montana var. lobata (Willd.) Sanjappa & Pradeep nuts are commonly found in the ENT region, either cultivated by the local community or grown wild. Generally, local legumes in ENT have a relatively complete nutritional composition, which varies depending on the type of bean. The ENT's local people use local beans as a mixture of rice/corn and function as sources of protein. Future research is needed to explore the functional characteristics of each ENT local bean. In addition, it is necessary to develop formulations and other food products to utilize ENT's local legumes to diversify legume-based local food.

Applying Artificial Intelligence to Classify the Maturity Level of Coffee Beans During Roasting

Coffee is a highly popular beverage worldwide. The quality of coffee is often judged based on its aroma and taste. Good coffee quality is influenced by various parameters during the coffee bean roasting process. Roasting is a crucial step where green coffee beans are heated at high temperatures, undergoing chemical reactions such as hydrolysis, polymerization, and pyrolysis. The color changes during the roasting process are caused by melanoidin, which results from Maillard and caramelization reactions, also impacting the flavor profile. Therefore, it is essential to accurately classify the level of coffee bean maturity. In the development of supercomputer technology, particularly with high-speed GPU microprocessors and large memory capacities, artificial intelligence algorithms have been widely implemented in various applications. Research on smart machines has been conducted to create systems resembling human intelligence. One of its applications is in recognizing the maturity level of coffee beans during roasting. In this study, image segmentation using ROI (Region Of Interest) and RGB color features are utilized to identify the characteristics of each coffee bean image. Additionally, CNN (Convolutional Neural Network) is employed for the classification stage, and this model is implemented into an Android smartphone device to detect the type of coffee bean being roasted. After the training process with 100 epochs, the model achieved a loss of 0.12 and a training accuracy of 94.79%. The model is capable of classifying images from the test data with an average accuracy of 85.83% and a loss value of 0.35.

Composição centesimal, minerais, taninos, fitatos e qualidade de cozimento de cultivares comerciais de feijão-caupi

ABSTRACT Cowpea is a socioeconomically important legume in the Northeast region of Brazil, and the most grown and consumed type of bean, representing the main source of low-cost vegetable protein for rural and urban populations in this region. The objective of this work was to characterize the proximate composition, minerals, tannins, phytic acid, and cooking quality of whole grains of cowpea from the commercial cultivars BRS Aracê, BRS Inhuma, and BRS Xiquexique. Grain samples of the cultivars were ground in a zirconium ball mill and the flour was used for analysis. The proximate composition was carried out according to the AOAC methodology. The minerals were determined by nitro-perchloric digestion and reading in a flame atomic absorption spectrophotometer. The anti-nutritional factors tannins and phytic acid were determined by extraction and reading in a spectrophotometer. Cooking quality was assessed using an electric pressure cooker and then a Mattson cooker. A completely randomized experimental design was used, with three treatments (cultivars) and three replications. The data were subjected to analysis of variance and the means were compared by the Tukey's test (p<0.05). The cowpea cultivars showed significant differences (p<0.05) for most characteristics evaluated and were similar in terms of ash, lipids, total dietary fiber, and most macrominerals. The results showed that grains of the cultivars BRS Aracê and BRS Xiquexique are excellent sources of dietary fiber, proteins, and minerals and present high cooking quality, while grains of the cultivar BRS Inhuma stood out regarding carbohydrates, soluble dietary fibers, total energetic value, and low levels of factors, anti-nutritional tannins and phytic acid.

Optimization of Defective Coffee Beans Decaffeination Using Palm Oil

Defective coffee beans amount to 15-20% of the total produced coffee beans. The defective coffee bean contains caffeine, which can negatively affect the human body, such as increased heart rate, and thus sensitive to consumption by some people. This study aims to optimize the decaffeination process of defective coffee beans. The extraction of aroma and flavor compounds was done by maceration, and the decaffeination was carried out using palm oil as a solvent. The type of beans (green and roasted beans), the decaffeination contact time, and the ratio between coffee bean extract and solvent were varied in this study. The caffeine content was quantified, and the organoleptic and color tests were done on the concentrated coffee extracts. It was found that the higher the amount of solvent volume in decaffeination, the higher the caffeine decrease. In addition, the longer the green beans’ decaffeination time, the lower the caffeine decrease. Decaffeination using green coffee beans resulted in a greater reduction of caffeine (6.515-48.241%) than roasted coffee beans (8.495-24.272%). The optimum operating condition of green coffee bean decaffeination was the coffee bean extract and solvent ratio of 1:5.82 and the decaffeination time of 26.5 minutes. The organoleptic test result shows that decaffeinated coffee flavor had the same preferability as the commercial coffee flavor and was thus able to compete in the market.

Extraction technology and influencing factors of cold brew coffee

Coffee especially cold brew coffee is widely popular around the world, so people have conducted deep research on how to make a cup of coffee. This paper studies the types of coffee beans, the chemical composition of coffee beans, the roasting degree of coffee beans and the extraction technology. Among them, there are a lot of biochemistry reactions such as Maillard and Caramel reaction. Moreover the extraction technology focuses on the cold extraction technology, which is compared with the hot extraction technology in the content of flavor substances. In addition, the flavoring substances are matched with their aroma to compare the flavor differences between hot brew and cold brew coffee. Finally, the adjustable influencing factors in the cold extraction technology were studied and summarized to provide theoretical reference and practical guidance for the flavor improvement and industrial production of cold brew coffee. Furthermore, there will emerge other problems of coffee producing which will be issues to solve in the future.

Assessment of the Physical, Chemical, Yield and Nutritional Quality Traits of Tropical, Temperate and Wild Dry Bean Species in Vietnam

Background: Assessing agronomic and nutritional characteristics is one of the primary goals for assisting plant scientists in selecting promising grain legume crops for future food and nutritional security and increasing the income of smallholder farmers. Methods: The experiment was conducted from September 2021 to February 2022 at the greenhouse of GRIS, Ton Duc Thang University, Vietnam. Yield components, agronomic characteristics and biochemical and nutritional parameters of two varieties of tropical beans (Velvet White Bean and Red Cowpea), two varieties of temperate beans (Dwarf Stick Bean and Soybean) and two varieties of wild beans when grown in Vietnam were evaluated in the present study. Result: The results revealed the presence of a wide range of phenotypic variations in dry bean germplasm. The biochemical and nutritional properties of the bean types investigated revealed that the Velvet White Bean had a higher phenol content (0.42 mg GAE g-1) and a lower phytic acid concentration (2.87 µg mL-1) than the other species. The Dwarf stick vean had the highest flavonoid concentration among the types (0.017 mg RE g-1) while the crude protein content of soybean (38.2 mg g-1) was the highest. These findings serve as a foundation for breeders in selecting promising legumes to breed for Vietnam.

Learning Vector Quantization for Robusta and Arabica Coffee Classification

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.

Pengaruh Konsentrasi Natrium sitrat dan Variasi Prapemasakan Terhadap Karakteristik Multigrain Rice Instan


 
 
 
 Instant multigrain rice is instant rice with a combination of more than one type of rice, beans and grains. The study aimed to determine the effect of variations in precooking and the addition of sodium citrate on the physicochemical characteristics of instant multigrain rice. The research method used a combination of two factors, namely a combination of preprocessing (roasting and steaming) and sodium citrate concentration (2% and 5%). Data analysis used ANOVA at the 0.05% level. This study included preparation of ingredients, weighing of ingredients, size reduction of edamame and kidney beans, washing, soaking (Na3C6H5O7 2% and 5%), washing, precooking (roasting and steaming), cooking, cooling, freezing, thawing, drying, and rehydration. Variations in the preprocessing method and the addition of sodium citrate concentration had a significant effect on the value of water content, fiber content, protein content, fat content, color and rehydration time. The value of water content obtained ranges from 3.47%-5.03%, fat, 3.53%-4.09%, fiber 5.07%-5.51%, protein 12.23%-12.79, rehydration time 5.07-5.51 minutes and color obtained brightness value (L*) of 53.22%-53.79%, reddish value (a*) 7.08%-6.53%, and yellowish value (b*) 14.22%-14.81%.
 
 
 

Evaluation of long-term residual activity of insecticides against Acanthoscelides obtectus (Say) on common bean in laboratory tests

There are insufficient data about long-term (longer than six-months) residual effects of insecticides against Acanthoscelides obtectus (Say) as an important economic insect pest of stored bean. Therefore, we evaluated, in laboratory conditions, the insecticide potential of recommended rates of commercial formulations: organophosphate malathion (10 mg active ingredient - AI/kg), spinosyns bioinsecticide spinosad (1 mg AI/kg) and pyrethroid deltamethrins (0,25 mg AI/kg, with and without the synergist piperonil butoxide – PBO) after 210-, 265-, 310- and 365-days of post-treatment. Testing was performed on two types of beans with kidney- and cranberry-shaped kernels to evaluate the insecticide residual effects on the survival of weevils, progeny production, and related levels of kernels damage. Survival of weevils depends mainly on adults exposure/living period in treated beans and on the recovering period. Small differencies in weevils survival in each insecticide were recorded. We found increases in progeny and thus higher damage in beans, as manifested effects of sublethal doses only in malathion treated bean. In spinosad treated bean we recorded low levels of progeny and damage, below the economic threshold. In deltamethrin treatments, we recorded weevils avoiding contact with beans, no progeny and no damages. The effect of bean type was very low or non existent. It can be concluded that bioinsecticide spinosad can provide long-term protection of common beans against A. obtectus, while organophosphate malathion has low control potential. Pyrethroid deltamethrins residues cause adults to avoid treated beans and no offspring to appear. The obtained results might have significant impact on better understanding of weevils behaviour in the presence of insecticide residues and on the creation of sustainable pest management programmes.

Analisis Aktivitas Antioksidan, Total Senyawa Fenolik Dan Kandungan Cemaran Aflatoksin Pada Tempe Dari Berbagai Varietas Kedelai (Glycine Max L. Merill)

Tempe is an authentic Indonesian fermented product made from soybeans or other types of beans that use the fungus Rhizopus oligosporus and uses the fungus Rhizopus oryzae. Soybean (Glycine max) is one of the sources of vegetable protein, which is relatively cheap compared to animal protein sources. Soybeans contain phenolic compounds. Phenolic compounds contained in soybeans partly act as antioxidants. Soybeans also contain aflatoxin. Tempe made from fermented soybeans can be indicated aflatoxin. In this study using a Complete Random Design (CRD) with four varieties treatment and that is import soybeans "Bola" (A1), local soybean varieties Dena 1 (A2), local soybean varieties Devon 1 (A3) and local soybean varieties Anjasmara (A4).

Rancang Bangun Aplikasi Pengelola Smart Engine Untuk Deteksi Jenis Biji Kopi Dengan Menerapkan Web Service

Coffee is one of the agricultural commodities that plays a vital role as a refreshing beverage and is categorized as a perennial crop. Currently, coffee is becoming a trend and an integral part of people's lifestyles. Coffee is divided into two main varieties: robusta and arabica, each with its distinct characteristics. Differentiating between robusta and arabica coffee beans can be challenging due to their similar physical appearance. Deep learning-based smart engines can offer a solution to this problem, although their complex user interfaces often hinder accessibility. To address this issue, a Spiral development method was employed to build an application using PHP programming language and Codeigniter 4 framework. This application facilitates easy detection of coffee bean types through both a website and a RESTful API, allowing users to access it online from any device. The application underwent comprehensive black box testing, demonstrating successful functionality aligned with the initial design objectives. It is expected that this application will solve the identified problem and provide significant assistance to coffee enthusiasts and the general public in easily distinguishing between various coffee bean types. Users can effortlessly recognize and differentiate between different coffee beans while obtaining useful information about their preferred coffee beans.

Sistem Informasi Pemesanan “Cangkrukan Cak Suga”Berbasis Web

Coffee is one of the agricultural commodities that plays a vital role as a refreshing beverage and is categorized as a perennial crop. Currently, coffee is becoming a trend and an integral part of people's lifestyles. Coffee is divided into two main varieties: robusta and arabica, each with its distinct characteristics. Differentiating between robusta and arabica coffee beans can be challenging due to their similar physical appearance. Deep learning-based smart engines can offer a solution to this problem, although their complex user interfaces often hinder accessibility. To address this issue, a Spiral development method was employed to build an application using PHP programming language and Codeigniter 4 framework. This application facilitates easy detection of coffee bean types through both a website and a RESTful API, allowing users to access it online from any device. The application underwent comprehensive black box testing, demonstrating successful functionality aligned with the initial design objectives. It is expected that this application will solve the identified problem and provide significant assistance to coffee enthusiasts and the general public in easily distinguishing between various coffee bean types. Users can effortlessly recognize and differentiate between different coffee beans while obtaining useful information about their preferred coffee beans.

Optimization of Coffee Oil Extraction from Defective Beans Using a Supercritical Carbon Dioxide Technique: Its Effect on Volatile Aroma Components.

Defective green coffee beans are typically discarded due to their negative impacts on coffee qualities compared to normal beans. However, there are some types of defective beans that can cause volatile aroma compounds after roasting similar to those produced by normal beans. This study aimed to optimize conditions for coffee oil extraction by supercritical carbon dioxide using the response surface methodology (RSM). Furthermore, the investigation assessed the aroma-active compounds and sensory quality in extracted coffee oil. Thus, operational temperatures (33.2-66.8 °C), pressure (10-30 MPa) and ethanol (g) to roasted coffee (g) ratio (0.25:1-1.5:1) were optimized for coffee oil extraction. As a result, different oil yields with different key volatile aroma compounds concentrations were obtained and it was found that the optimum conditions for extraction were a temperature of 50 °C, pressure of 30 MPa, and ethanol (g) to roasted coffee (g) ratio of 1:1 to obtain 6.50% (w/w) coffee oil yield. Key volatile aroma compounds, including furfuryl alcohol, 5-methyl furfural, 2,5-dimethylpyrazine, 4-vinylguaiacol, furfuryl acetate, 3-ethyl-2,5-dimethylpyrazine, thiazole, 1-furfurylpyrrole, pyridine, 2,3-butanediol, and 3-methyl-1,2-cyclopentanedione which contributed to the most preferable burnt, sweet, bready, chocolate-like, and roasted flavors, were quantified. Overall, the results suggested that coffee oil extracted from defective beans could be potentially used as a flavoring agent.