Ripeness Level Research Articles

Modifikasi Proses Pengolahan Kopi Arabika Menggunakan Metode Honey Process

The amount of mucilage can be adjusted according to the washing intensity. The level of ripeness of the coffee fruit is also directly proportional to the mucilage constituent components. Optimally ripe coffee cherries have a higher sugar, protein, lipid and water content than raw coffee cherries. So that research is needed to determine the effect of washing intensity and level of ripeness of coffee cherries on the characteristics of the resulting taste of coffee. The research design used was a complete two-factor block design. The first factor is the intensity of washing mucilage that sticks to the surface of the coffee beans; 2 times washing, 1 time washing, without washing. The second factor is the level of ripeness of the coffee cherries; raw coffee cherries, optimally ripe coffee cherries, past ripe coffee cherries. The analyzes were carried out, namely water content, ash content, total sugar content, pH value, Brix, and organoleptic test using the SCAA cupping method. The results of this study indicate that washing intensity affects the Brix value, water content, total sugar content, and pH. Whereas in the second factor, the level of ripeness of the coffee fruit affects the Brik value, water content, total sugar content, and pH. This study also showed that there was a correlation between washing intensity and the level of ripeness of the coffee cherries and the preference of the panelists. The non-washing treatment (black honey) and the optimal level of ripeness were the most preferred by the panelists, all coffees produced from this study met SNI 01-3542-2004. Keywords: arabica characteristics, honey process, maturity level, washing intensity

PENGARUH PEMBERIAN ZAT PENGATUR TUMBUH (ZPT) DAN TINGKAT KEMASAKAN BUAH TERHADAP PERKECAMBAHAN BENIH KOPI ARABIKA (Coffea arabica L.) VARIETAS Gayo 1

Olyfia Rosalina "The Effect of Growth Regulatory Substances (ZPT) and Fruit Maturity Rate on the Germination of Arabica Coffee (Coffea arabica L.) Variety Gayo 1". This research was conducted in the laboratory of the Faculty of Agriculture, Gajah Putih University, Pegasing sub-district, Central Aceh district, with an altitude of 1,200 m above sea level. This research was conducted on 22 October 2019 to 20 December 2019. This study aimed to determine the effect between giving growth agents (ZPT) with treatments of 100 cc / l (T1), 150 cc / l (T2) and 200 cc / l (T3) and to determine the effect of the ripeness level of Arabica coffee fruit. treatment of red fruit (B1), reddish yellow fruit (B2) and brown fruit (B3). On the germination of Arabica coffee seeds varieties Gayo 1. The use of growth regulators (ZPT) has a very significant effect on the potential for growth, germination, wet weight and dry weight of sprouts, the best use of growth regulators (ZPT) is the treatment of 200 cc / l (T3). The level of ripeness of the coffee cherries had a very significant effect on the potential for growth, germination, wet weight and dry weight of sprouts, the level of ripeness of the best coffee cherries with the treatment of red fruit (B1). There is a very real interaction with the treatment of growth regulators (ZPT) and the level of fruit maturity in the growth potential test. The best results were obtained at the interaction between 200 cc / l (T3) treatment and red (B1) or (T3 B1) fruit ripeness.

Addressing Enzymatic Clarification Challenges of Muscat Grape Juice

Winemakers use technical enzymes to assist with clarification, extraction, and other processes in winemaking. In some cases, enzyme mixes are found to be ineffective for a variety of reasons. This study characterizes difficult-to-clarify juices from the Muscat family, examines the effects of pasteurization, and classifies these juices based on cultivar, harvest date, geographical location, and harvesting technique. In addition to studying the chemical compositions of different Muscat juices, enzyme testing was performed by creating enzyme cocktails and evaluating their functionality. The data suggest a distinct matrix effect on juice clarification that can be influenced during juice processing. Berry proteins, polysaccharides, and native enzymes play an important role during the clarification process, influencing the efficiency of technical enzymes. On the other side, high macromolecule extraction from the grape material, through excessive shearing forces in machine-harvested and processed fruit, for example, can have a negative effect, especially in ripe and overripe grape material. Based on these findings, the winemaking strategy and use of technical enzymes need to be adapted to the incoming grapes. Besides adjusting the mechanical forces to the level of ripeness, avoiding native fermentation prior to clarification should be prioritized. The enzyme mixes developed and tested in these experiments show a high degree of efficiency in the majority of juices that were evaluated.

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm−1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

Identification of Ripe Levels in Plantain Fruits Using Hue Saturation Intensity (HSI) Color Space Transformation Method

The study aimed to determine the maturity level of plantain fruit using the Hue Saturation Intensity (HSI) color space transformation method. This study uses the Hue Saturation Intensity (HSI) color space transformation method to classify the level of ripeness of plantain fruit. The results showed that the Hue Saturation Intensity (HSI) color space transformation method was quite good at identifying the ripeness level of bananas based on the high accuracy of the test. Of the 20 fruit samples where ten unripe and ten ripe plantains were calculated, the maximum and minimum average values of H and S obtained conformity accuracy of 85%.

Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches

The implementations of deep learning combined with other methods such as transfer learning and data augmentation in oil palm fresh fruit bunch (FFB) ripeness classification have been researched throughout the years. However, most of the methods require devices with high computational resources which could not be implemented in mobile applications. To overcome this problem, this research would focus on creating a mobile application to classify the ripeness levels of oil palm FFB using lightweight Convolutional Neural Network (CNN). In this research, we implemented ImageNet transfer learning on 4 lightweight CNN models with a novel data augmentation method named “9-angle crop”, which would be further optimized using post-training quantization. Transfer learning with 3 unfrozen convolution blocks and 9 angle crop successfully increased the classification accuracy on MobileNetV1, and when it was compared to other lightweight models, EfficientNetB0 performed best with 0.898 test accuracy on Keras. Float16 quantization also proved to be the most suitable post-training quantization method for this model, halving the size of EfficientNetB0 with the least increase in image classification time and an accuracy drop of only 0.005 after the model was converted to TensorFlow Lite interpreter. In conclusion, the best model created in this research is EfficientNetB0, trained with the combination of transfer learning, 9 angle crop, and float16 quantization, which enabled it to achieve an overall test accuracy of 0.893 on TensorFlow Lite with 96 ms classification time per image, far surpassing the other 3 compared models with the second-best model being MobileNetV1 with 0.811 accuracy. The model itself was able to achieve similar results when it was implemented on an Android application to classify the ripeness levels of oil palm FFB images obtained through live camera input.

Classification model of ‘Toraja’ arabica coffee fruit ripeness levels using convolution neural network approach

The purpose of this study is to design a CNN deep learning algorithm model that can classify the maturity level of Arabica coffee fruit based on image, the resulting model can be applied to a coffee bean sorting device based on artificial intelligence so that problems that exist in the process of sorting arabica coffee fruit that meets the standards can be avoided, to improve the quality of arabica Toraja coffee products. The research began from the collection of data in the form of raw Arabica coffee image Toraja as many as 4000 images of arabica coffee fruit with 4 categories, half-cooked, perfectly ripe, and mature old. CNN basic architecture is created using images with a size of 128x128 pixels, 4 convolution layers using 3x3 filters opening 32, 64, 128, and 256 with ReLU activation, followed by a poll layer with a 2x2 filter. The full connected layer uses 2 hidden layers with dropout layers. The training model was conducted with a 5-fold cross-validation method using epoch 100, 'adam' optimization algorithm with a learning rate of 0.0001, and batch size 10. The success of a model is seen based on the calculation of the confusion matrix. The test results showed that the accuracy rate of the third model using a combination of max polling and average polling performed best with an introduction accuracy of 98.75%, the first model used max polling with an accuracy of 98.25% while the lowest accuracy on the second model used average polling with an accuracy of 97.75%.

Palm oil extraction rate prediction based on the fruit ripeness levels using C4.5 algorithm

Oil palm plantations are one of the main keys in supporting Indonesia’s economic growth. The rising consumption needs for palm oil products make it necessary to carry out data mining activities to increase CPO production. The maturity factor of palm fruit dramatically affects the quality of the oil extraction content (CPO yield) produced. This study aims to investigate the effect of fruit ripeness on the yield of CPO by using a data mining classification method with a decision tree. The algorithm used to generate decision tree classification is the C4.5 algorithm. The implementation of the C4.5 algorithm in the study was carried out using the Rapid Miner Studio 5.2 tools. The results shows that the yield of CPO is influenced by the attributes of the condition of the long and ripe fruit, the condition of the long and overripe fruit, the normal condition of the fruit and the age of 3-6 years and the condition of the fruit of normal and age of 7-10 years. Decision tree C4.5 algorithm generates 8 rules with 4 rules showing a high production value, which means that the four rules affect the yield of CPO.

Papaya ripeness and post-harvest storage conditions affect growth, survival and death kinetics of Salmonella and spoilage organisms

The purpose of this study was to determine the effect of papaya ripeness level (0, 25, 50, 75 and/or 100 %) and post-harvest storage conditions (i.e., Temperature: 4, 12 and 21 °C, and RH: 55 and 90 %) on the survival kinetics of Salmonella spp., and spoilage organisms. In addition, the effect of test conditions on the physico-chemical properties of fresh-cut papayas was also determined. Maradol papayas of different commercial ripeness levels were cut into 3 cm2 cubes either with or without a peel. The samples were spot inoculated with 25 μL of nalidixic acid adopted Salmonella spp (4-strain) to achieve 4–5 log CFU g−1. The inoculated samples and uninoculated controls were stored in an environmental chamber for up to 14 d. Papaya ripeness level in combination with storage temperature and RH have shown to affect Salmonella survival on both fresh-cut papaya and papaya peel. Increasing the fruit ripeness level from 0 to 100 %, storage temperature from 4 to 21 °C and RH from 55 to 90 % increased the log survival. Samples at low ripeness levels showed slower growth of yeast and molds compared to samples at higher ripeness levels. Ripeness levels also showed an effect on the total soluble solids content of fresh-cut papaya during the storage. Based on these findings, papaya ripeness level in combination with post-harvest storage conditions need to be considered to maximize the microbiological safety while maintaining the quality.

Detection of Banana and Its Ripeness Using Residual Neural Network

Automatic fruit detection utilizing computer vision techniques has been carried out to help the agriculture and plantation industries. This study researches smart systems to detect bananas and ripeness classification utilizing residual neural networks. The method used to detect bananas is transfer learning from pretraned Model VGG-19. Whereas, in the bananas ripeness classification process, residual neural networks, which are trained from the start, are used. Sliding Windows is used to detect the position of bananas followed by Non-Max Suppression to summarize the results of several detected bananas. Previous studies were limited to the level of ripeness, but in this study, bananas are detected and followed by the level of bananas ripeness (raw, ripe, and overripe). This study’s data uses bananas which were mixed with other kinds of fruit. There two kinds of bananas detection architecture used in this study, VGG-19 and Restnet. After they were used to detect bananas, it was found that VGG-19 was more suitable. The results of this study are very satisfying as it is seen from the bananas detection testing percentage using VGG-19 architecture which shows 100% ripe bananas, 99 % raw bananas, and 100% overripe bananas.Keywords: Detection of banana, banana ripeness, Non-Max suppression, residual block.

From green to ripe: Dynamics of peacemaking in Colombia (1998–2016)

Armed conflict in Colombia with the Revolutionary Armed Forces of Colombia (FARC-EP) was only settled after 50 years and several attempts at negotiations. This sequence of events fits the pattern of “conflict ripeness” first proposed by William Zartman. But using a successful settlement as a way to determine ripeness can be tautological. To address this issue, we develop a formal model to identify the level of ripeness at which a conflict is settled. In an overripe conflict both parties end up spending resources in a military build-up that is out of proportion with what they obtain in the final settlement. We show that such overripeness is exacerbated by the access to resources and by the factional heterogeneity within the two sides. We illustrate these dynamics by looking in detail at the attempts at negotiation between Colombia’s government and the FARC-EP. To that end, we combine statistical data, some previously undisclosed, and interviews with some key participants.

HIDUNG ELEKTRONIK BERBASIS SENSOR GAS MOS UNTUK KARAKTERISASI KEMATANGAN BUAH KELAPA SAWIT

The ripeness of oil palm fruits is one of the key factors for crude palm oil qualities. Recently, electronic nose systems have been developed intensively for fruit quality assessment which relates odors to ripeness levels. This study developed an electronic nose system to characterize the ripeness levels of oil palm fruits using output voltage of each sensor and fruit hardness. The system consisted of a sensor chamber and a sample chamber. The sensor chamber consisted of eight MOS gas sensor modules of MQ series. Samples were oil palm fruits taken from oil palm fresh fruit bunches (FFB) which were previously categorized traditionally into unripe, ripe, over ripe, peeled and put into the sample chamber. Some of the fruits were also used for hardness measurement. To quantify the output voltages for each sensor, integrated trapezoid areas were calculated and related to the fruit hardness values. The results showed a significant voltage difference of each sensor for the three ripeness levels. Only four out of eight sensors showed significantly higher voltages. Three sensors which can significantly differentiate the ripeness levels are MQ3, MQ5, and MQ135 which MQ135 is the best. This shows that the electronic nose is potential for oil palm fruits. Keywords: electronic nose, fruit hardness, MOS gas sensor, oil palm fruit, ripeness

Potential Contribution of Climate Change to the Protein Haze of White Wines from the French Southwest Region.

The aim of this study was to evaluate the role played by climatic conditions during grape ripening in the protein instability of white wines produced in the French southwest region. For this purpose, basic wine analyses were carried out on 268 musts and the corresponding wines, all produced during the 2016, 2017, 2018, and 2019 vintages, with distinctive climatic conditions. Qualitative and quantitative variables were correlated with levels of protein haze determined by heat test (80 °C/2 h) in the wines using analysis of covariance (ANCOVA), principal component analysis (PCA), and classification and regression trees (CART). Our results show that the climatic change, with the increase in temperatures, and the decrease in precipitation during the grape ripening phase, tends to enhance the risk of protein instability in wines. Indeed, the values of pH, titratable acidity, and malic acid concentrations of the musts, which are good indicators of the conditions in which the grapes ripened and of the level of ripeness of the grapes, were also the variables that correlated best with the protein haze. By measuring these parameters at harvest before alcoholic fermentation, it may be possible to predict the risk of protein haze, and thus early and precisely adapt the stabilization treatment to be applied.

AI-based soft-sensor for shelf life prediction of \u2018Kesar\u2019 mango

This paper presents prediction of shelf-life of ‘Kesar’ cultivar of mangoes stored under specified conditions based on their respiration rate and ripeness levels. A deep-CNN was fine-tuned on 1524 image data of mangoes stored under different conditions to classify the ripeness levels of mangoes as ‘unripe’, ‘early-ripe’, ‘partially-ripe’ and ‘ideally-ripe’. CO2 respiration rate (RRCO2) was further calculated using principle of enzyme kinetics to establish a correlation between RRCO2 and ripeness levels. A Support Vector Regression model was employed to predict the shelf life and ripeness levels of mangoes under different storage conditions, thereby creating an AI based soft-sensor. The developed methodology can be used for other climacteric fruits besides mangoes. This solution can be used by producers and distributors for post-harvest handling of climacteric fruits like mango. It will also aid retailers in taking dynamic decisions with respect to pricing, logistics and storage conditions to be maintained to get the desired ripening rate, thus, contributing to reduction of wastage of fruits and subsequent economic losses.Article highlightsVariation in CO2 respiration rate of ‘Kesar’ mangoes over different maturity stages were observed under different supply chain scenarios simulated in lab environmentAI models were developed based on respiration rate and ripeness levels for prediction of shelf life of mangoes under different supply chain scenarios.These models once deployed helps all stake holders in fruit supply chain to take dynamic decisions such as repricing, recycling and repurposing based on the predicted shelf life thus minimizing wastage and maximizing profit.

Effect of processing conditions on quality of juice extracted from hog plum fruit

The effect of processing conditions such as machine shaft speed, loading and level of ripeness of the Spondias mombin fruit on quality (i.e moisture, ash, fibre, fat and protein contents) of juice extracted were investigated in this study using a newly designed juice extractor for Spondias mombin fruit. The moisture content of the extracted juice was observed to initially decrease as the shaft speed increased from 120 to 130 rpm and then increased with increase in shaft speed from 130 to150 rpm. Increase in loading from 5 to 15 kg per time increased the moisture content of the juice at different shaft speeds. As the shaft speed and rate of loading per time increases, the ash content of the juice also increases. Increase in shaft speed also increased the fibre, fat and protein contents of the juice. The effect of the processing conditions considered indicates that separate and interactive effects of the three factors on the qaulity parameters of the juice were significant (p<0.05).
 Keywords: Hog plum, Ripeness, Machine, Processing, Juice, Quality

Inhibition of orange (Citrus reticulata ) green mold with anti-fungal yeast Debaryomyces hansenii and Aureobasidium pullulans

Green mold rot is an important disease that generally affects post-harvest and storage oranges. The attack of this disease quickly causes a decrease in the quality and shelf life of citrus fruits during storage. Post-harvest handling of citrus fruit from green mold attack is generally by coating the fruit using synthetic fungicides so that the fungicide residue often sticks to the orange peel when marketed. Fruit protection with a protective material can be used to increase shelf life and maintain the quality or quality of fruit stored in a room without refrigeration. The use of yeast as a protective material for postharvest fruit is still a little developed. The yeast fructoplan D. hansenii and A. pullulans have anti-fungal properties, so they have the potential to be developed as fruit protective agents. The experiment objective to find out the yeast ability of fructoplan D. hansenii and A. pullulans to protect postharvest citrus fruits with different ripeness levels against pathogens of green mold, storage capacity and quality of citrus fruits. The study was conducted using a two-factor randomized block design with four replications. The first factor, the type of antifungal yeast treatment, consisted of 4 levels; namely treatment without yeast and pathogens as negative control; pathogen inoculation alone, without application of yeast as positive control; A. pullulans application treatment, and D. hansenii application treatment. The second factor was the level of maturity of the citrus fruit, consisting of 3 levels, namely 25% yellow, 75% yellow; and 100% yellow. Measurement variables included disease severity, intensity of green mold rot, fruit storage time, weight loss, fruit hardness, vitamin C content, and total dissolved solids. The results of the experiment proved that the coating treatment of citrus fruits with yeast D. hansenii and A. pullulans was able to maintain the quality of oranges, increase shelf life, and increase the resistance of oranges to post-harvest diseases without reducing the quality of citrus.

Implementation of Hygiene and Environmental Sanitation in Under Five Years Old Diarrhea Patients at Surabaya Primary Health Center

Surabaya has the most cases of diarrhea in East Java, with the highest detection targets in Gading Health Center (2346 cases) and Mojo Health Center, with diarrhea cases handled by 1322 cases. This study aimed to describe hygiene and environmental sanitation in under-five diarrhea patients in the working area of Surabaya Health Center. This study used a survey method, descriptive approach, interviews, and questionnaires in October 2019-March 2020. The results showed 62,2% of under-fives ‘drinking water sources were ineligible, 86,5% of mothers’ handwashing behavior were ineligible, 67,6% of the use of serving hoods were ineligible, 100% of food ripeness level were eligible, 59,5% of the distance between well and septic tank were eligible, 100% of latrines were eligible, 89,2% of sewerages were ineligible, and 83,8% of waste disposal facilities were ineligible. This research concluded that drinking water sources, handwashing behavior, serving hoods, sewerages, and waste disposal facilities were ineligible. The problems can be solved by consuming branded gallons of water / well water or refillable water that is boiled before consumption, improving how to wash hands with soap and running water, covering food using a serving hood, use the cover to close the sewerage, and throwing the trash into the temporary shelter (TPS) as much as two times a week or less than 3x24 hours.

Segmentation of Mango Fruit Image Using Fuzzy C-Means

Mango contains about 20 vitamins and minerals such as iron, copper, potassium, phosphorus, zinc, and calcium. The freshness of the ripe mango will taste sweet. The level of ripeness of the mango fruit can be seen from the texture of the skin and skin color. Ripe mangoes have a bright, fragrant color and a smooth skin texture. The problem found in mango segmentation is that the image of the mango fruit is influenced by several factors, such as noise and environmental objects. In measuring the maturity of mangoes traditionally, it can be seen from image analysis based on skin color. The mango peel segmentation process is needed so that the classification or pattern recognition process can be carried out better. The segmented mango image will read the feature extraction value of an object that has been separated from the background. The procedure on the image that has been analyzed will analyze the pattern recognition process. In this process, the segmented image is divided into several parts according to the desired object acquisition. Clustering is a technique for segmenting images by grouping data according to class and partitioning the data into mango datasets. This study uses the Fuzzy C Means method to produce optimal results in determining the clustering-based image segmentation. The final result of Fuzzy C-based mango segmentation processing means that the available feature extraction value or equal to the maximum number of iterations (MaxIter) is 31 iterations, error (x) = 0.00000001, and the image computation testing time is 2444.913636

Ripeness Level Classification of Banana Fruit Based on Hue Saturate Value (HSV) Color Space Using K-Nearest Neighbor Algorithm

Many types of bananas are cultivated locally in Indonesia, including the Muli Banana or Musa Acuminata Linn. During the post-harvest period of banana fruit, there is a problem in the sorting process of bananas based on their level of maturity. The fruit sorting process manually uses the human eye, but it is ineffective due to decreased vision and the large quantity of fruit. Therefore, we need a system that can quickly classify the ripeness of the banana fruit. This study aims to create a system that can organize the maturity level of the banana fruit. The classification system designed using the HSV color feature extraction method and the K-Nearest Neighbor classification algorithm. After going through the testing phase, the system can classify bananas into three classes: unripe, ripe, and rotten. System testing used 30 test data images, and the results show 2 test images whose classification results are wrong and 28 other test images whose classification results are correct. Based on calculations, the accuracy achieved by the system is 93.333%.

Durian (Durio zibethinus) ripeness detection using thermal imaging with multivariate analysis

The detection of durian ripeness using thermal imaging is an essential study geared towards improving the current analytical methods which rely heavily on routine analysis and human labour skills. Thermal imaging was investigated in this study in order to evaluate the ripeness of durian based on the relationship of physicochemical properties and thermal image parameters. Thermal images of durians were acquired at three different ripening stages (unripe, ripe, and overripe) and the physicochemical properties of the soluble solids content, pH, firmness, moisture content, and colour changes were determined. Partial least squares (PLS) regression was used to develop quantitative prediction models with R2 values greater than 0.94 for all the physicochemical properties of durians. Principal component analysis (PCA) showed successful clustering ability of three different ripeness levels of durians. Linear discriminant analysis (LDA), k-nearest neighbour (kNN), and support vector machine (SVM) were applied for the establishment of the optimal classification modelling algorithms. The SVM classifier gave the overall best performance for the discrimination of durian ripeness with a classification accuracy of 97 %. The feasibility of thermal imaging coupled with multivariate methods demonstrated huge potential for non-destructive evaluation of durian ripeness levels.