Optimal Test Research Articles

Optimization of robusta green coffee fermentation by Bifidobacterium bifidum and Lactobacillus plantarum using response surface methodology

In vitro fermented robusta coffee is fermented coffee beans using lactic acid bacteria (LAB) isolated from the stomachs of civets. In vitro fermented robusta coffee was obtained by optimizing three variables, namely inoculum concentration, use of microorganisms Bifidobacterium bifidum and Lactobacillus plantarum and in vitro fermentation time using response surface methodology (RSM). This study aimed to produce fermented robusta coffee in vitro with optimal cupping test results using three variables that affect fermentation. The results of the RSM showed that the optimum cupping test results were 72.62, inoculum concentrations of 5% (w/v), in vitro fermentation time of 8 hrs and the ratio of B. bifidum/L. plantarum at a ratio of 1:0.

Utilizing municipal solid waste incineration bottom ash and volcanic tuff to produce geopolymer materials

Large volumes of municipal solid waste incineration bottom ash (MSWIBA) are produced annually in China; however, their improper disposal causes serious ecological and environmental problems. This study investigated the feasibility of using MSWIBA and volcanic tuff to produce geopolymers. The effects of the MSWIBA content, silicon–sodium molar ratio, and alkali equivalent on the uniaxial compressive strength (UCS) and bulk density of the geopolymer samples were examined using single-factor and optimization tests. The test results showed that the UCS and bulk density of the geopolymer decreased as the MSWIBA content increased, whereas they increased as the alkali equivalent increased. As the silicon–sodium molar ratio increased, the UCS first increased and then decreased, and the change in bulk density was limited. In the final geopolymer preparation, the MSWIBA content was 17.85%, the silicon–sodium molar ratio was 1.71, and the alkali equivalent was 9.49%. According to X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, energy-dispersive spectrometry, and nuclear magnetic resonance analyses, the main phases of the geopolymer under the final preparation parameters were quartz, sanidine, calcite, orthoclase, gismondine, and illite. During the geological polymerization process, a large amount of CNASH gel was generated and attached to the surface of the particles or filled the pores between the particles, resulting in a pore size of 0.01–1 μm. The heavy metals in the geopolymers exhibited a good stabilization effect owing to the CNASH gel. Except for Cd, which posed a medium risk, the remaining heavy metals posed low or no risk.

Analisis pembuatan dan penggunaan koagulan poly aluminium chloride (PAC) pada proses penjernihan air

Background: Poly Aluminum Chloride (PAC) is a complex inorganic compound between hydroxyl (OH) ions and aluminum ions which undergo gradual chlorination. PAC is able to coagulate suspended substances or colloidal dispersions to produce floc that easily settles. Methods: The raw materials used in the manufacture of PAC consist of alumina hydrate Al (OH) 3, hydrochloric acid (HCl), sulfuric acid (H2SO4), and calcium carbonate (CaCO3). The experiments were carried out by reacting Al (OH) 3 as much as 100 grams, HCl as much as 180 ml, and H2SO4 as much as 127.1 grams at 90 ° C and 100 ° C temperature variations with time variations of 30s, 60s, and 90s. The characterization was tested in the form of a jar test and FTIR test. Finding: In the Jar Test, the optimum temperature and time are obtained at 90 ° C and 60s and at 100 ° C and 60s. At the optimum jar test results, the FTIR test was performed. Conclusion: From the test results, this study meets PAC manufacturing standards.

Temporal trends and regional variability in BRAF and KRAS genetic testing in Denmark (2010-2022): Implications for precision medicine.

This study aims to evaluate the developments in the testing of Kirsten Rat Sarcoma viral oncogene homolog (KRAS) and v-Raf murine sarcoma viral oncogene homolog B1 (BRAF) mutations across different cancer types and regions in Denmark from 2010 to 2022. Using comprehensive data from the Danish health registries, we linked molecular test results from the Danish Pathology Registry with cancer diagnoses from the Danish National Patient Registry between 2010 and 2022. We assessed the frequency and distribution of KRAS and BRAF mutations across all cancer types, years of testing, and the five Danish regions. The study included records of KRAS testing for 30 671 patients and BRAF testing for 30 860 patients. Most KRAS testing was performed in colorectal (78%) and lung cancer (18%), and BRAF testing in malignant melanoma (13%), colorectal cancer (67%), and lung cancer (12%). Testing rates and documentation mutational subtypes increased over time. Reporting of wildtype results varied between lung and colorectal cancer, with underreporting in lung cancer. Regional variations in testing and reporting were observed. Our study highlights substantial progress in KRAS and BRAF testing in Denmark from 2010 to 2022, evidenced by increased and more specific reporting of mutational test results, thereby improving the precision of cancer diagnosis and treatment. However, persistent regional variations and limited testing for cancer types beyond melanoma, colorectal, and lung cancer highlight the necessity for a nationwide assessment of the optimal testing approach.

Diabetes Disease Detection Classification Using Light Gradient Boosting (LightGBM) With Hyperparameter Tuning

Diabetes is a condition caused by an imbalance between the need for insulin in the body and insufficient insulin production by the pancreas, causing an increase in blood sugar concentration. This study aims to find the best classification performance on diabetes datasets with the LightGBM method. The dataset used consists of 768 rows and 9 columns, with target values of 0 and 1. In this study, resampling is applied to overcome data imbalance using SMOTE and perform hyperparameter optimization. Model evaluation is performed using confusion matrix and various metrics such as accuracy, recall, precision and f1-score. This research conducted several tests. In hyperparameter optimization tests using GridSearchCV and RandomSearchCV, the LightGBM method showed good performance. In tests that apply data resampling, the LightGBM method achieves the highest accuracy, namely the LightGBM method with GridSearchCV optimization with the highest accuracy reaching 84%, while LightGBM with RandomSearchCV optimization reaches 82% accuracy.

Lamax: determining the optimal test duration for maximal lactate formation rate during all-out sprint cycle ergometry.

This study aimed to ascertain the optimal test duration to elicit the highest maximal lactate formation rate ( Lamax), whilst exploring the underpinning energetics, and identifying the optimal blood lactate sampling period. Fifteen trained to well-trained males (age 27 ± 6years; peak power: 1134 ± 174W) participated in a randomised cross-over design completing three all-out sprint cycling tests of differing test durations (10, 15, and 30s). Peak and mean power output (W and W.kg-1), oxygen uptake, and blood lactate concentrations were measured. Lamax and energetic contributions (phosphagen, glycolytic, and oxidative) were determined using these parameters. The shortest test duration of 10s elicited a significantly (p = 0.003; p < 0.001) higher Lamax (0.86 ± 0.17mmol.L-1.s-1; 95% CI 0.802-0.974) compared with both 15s (0.68 ± 0.18mmol.L-1.s-1; 95% CI 0.596-0.794) and 30s (0.45 ± 0.07mmol.L-1.s-1; 95% CI 0.410-0.487). Differences in Lamax were associated with large effect sizes (d = 1.07, d = 3.15). We observed 81% of the PCr and 53% of the glycolytic work completed over the 30s sprint duration was attained after 10s. BLamaxpost were achieved at 5 ± 2min (ttest 10s), 6 ± 2min (ttest 15s), and 7 ± 2min (ttest 30s), respectively. Our findings demonstrated a 10s test duration elicited the highest Lamax. Furthermore, the 10s test duration mitigated the influence of the oxidative metabolism during all-out cycling. The optimal sample time to determine peak blood lactate concentration following 10s was 5 ± 2min.

Minimax detection boundary and sharp optimal test for Gaussian graphical models

Abstract In this article, we derive the minimax detection boundary for testing a sub-block of variables in a precision matrix under the Gaussian distribution. Compared to the results on the minimum rate of signals for testing precision matrices in literature, our result gives the exact minimum signal strength in a precision matrix that can be detected. We propose a thresholding test that is able to achieve the minimax detection boundary under certain cases by adaptively choosing the threshold level. The asymptotic distribution of the thresholding statistic for precision matrices is derived. Power analysis is conducted to show the proposed test is powerful against sparse and weak signals, which cannot be detected by the existing Lmax and L2 tests. Simulation studies show the proposed test has an accurate size around the nominal level and is more powerful than the existing tests for detecting sparse and weak signals in precision matrices. Real data analysis on brain imaging data is carried out to illustrate the utility of the proposed test in practice, which reveals functional connectivity between brain regions for Alzheimer’s disease patients and normal healthy people.

Review of methodologies for detecting an observer effect in commercial fisheries data

Observers are deployed on commercial fishing trips to collect representative samples of discard rates. However, fishers may change their fishing habits when an observer is onboard (“observer effect”) or observer programs may over- or under-sample portions of the fleet (“deployment effect”). If the extent of these effects are substantial, observer data will not be representative of unobserved trips, potentially biasing the estimation of discards. This sampling bias can impact catch monitoring, stock assessments, and fishery management. The purpose of this study was to examine the power and error rate of several published methods for detecting an observer effect using a simulation of observer and deployment effects at varying sampling ratios (i.e., observer coverage) for several sample statistics. The simplest methods (t-test and F-test for difference of means and variances) provided an accurate although imprecise estimate of the observer effect size, but only when there were no deployment effects. A generalized linear mixed effects model (GLMM) was also not reliable for detecting small bias, but was not confounded by deployment effects and was relatively robust to changing coverage rates. The most complicated tests involved comparing differences in trip characteristics between subsequent trips for observed-unobserved and unobserved-unobserved pairs. These tests were able to detect smaller observer effects and were not confounded by deployment effects, but were unreliable at high coverage rates (>60%), producing both high false positive and false negative rates. Sensitivity tests also showed differing detection accuracy as the distribution of the metric of interest changed. Thus, the optimal test for detecting an observer effect will depend on the metric of interest, the coverage rate, and whether a deployment effect exists. An example from the New England groundfish fishery is provided to illustrate how conflicting results may be explained. Results should always be considered carefully when declaring that an observer effect is or is not occurring because of the sensitivity of the tests.

Structural Properties of the Wyner-Ziv Rate Distortion Function: Applications for Multivariate Gaussian Sources.

The main focus of this paper is the derivation of the structural properties of the test channels of Wyner's operational information rate distortion function (RDF), R¯(ΔX), for arbitrary abstract sources and, subsequently, the derivation of additional properties for a tuple of multivariate correlated, jointly independent, and identically distributed Gaussian random variables, {Xt,Yt}t=1∞, Xt:Ω→Rnx, Yt:Ω→Rny, with average mean-square error at the decoder and the side information, {Yt}t=1∞, available only at the decoder. For the tuple of multivariate correlated Gaussian sources, we construct optimal test channel realizations which achieve the informational RDF, R¯(ΔX)=▵infM(ΔX)I(X;Z|Y), where M(ΔX) is the set of auxiliary RVs Z such that PZ|X,Y=PZ|X, X^=f(Y,Z), and E{||X-X^||2}≤ΔX. We show the following fundamental structural properties: (1) Optimal test channel realizations that achieve the RDF and satisfy conditional independence, PX|X^,Y,Z=PX|X^,Y=PX|X^,EX|X^,Y,Z=EX|X^=X^. (2) Similarly, for the conditional RDF, RX|Y(ΔX), when the side information is available to both the encoder and the decoder, we show the equality R¯(ΔX)=RX|Y(ΔX). (3) We derive the water-filling solution for RX|Y(ΔX).

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.

BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases.

Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT (https://github.com/wangjr03/BayesKAT), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.

Copy number variation sequencing for the products of conception: What is the optimal testing strategy

BackgroundCopy number variation sequencing (CNV-seq) is crucial in prenatal diagnosis, but its limitations in detecting polyploidy, maternal cell contamination (MCC), and uniparental disomy (UPD) restrict its application in the analysis of products of conception (POCs). This study aimed to investigate an optimal genetic testing strategy for POCs in the era of CNV-seq. MethodsCNV-seq and quantitative fluorescent polymerase chain reaction (QF-PCR) were performed in all 4,211 spontaneous miscarriage cases. Different testing strategies were compared and the optimal testing strategies were proposed. ResultsOf the 4,211 cases, 2561 (60.82%) exhibited clinically significant chromosomal abnormalities. CNV-seq alone, without QF-PCR, might misdiagnose 311 (7.39%) cases, including 278 polyploidy, 13 UPD, and 20 MCC. In 20 MCC cases identified by QF-PCR, CNV-seq successfully pinpointed the cause of miscarriage in 13 cases. Furthermore, in cases where QF-PCR suggested polyploidy, CNV-seq improved the diagnostic accuracy in 54 (1.28%) hypo/hypertriploidy cases. After comparing four different strategies, the sequential approach (initiating with CNV-seq followed by QF-PCR if necessary) emerged as advantageous, reducing approximately 70% of the cost associated with QF-PCR while maintaining result accuracy. ConclusionsWe propose an initial CNV-seq followed by QF-PCR if needed—an efficient and cost-effective strategy for the genetic analysis of POCs.

Accessibility, availability and common practices regarding genetic testing for epilepsy across Europe: A survey of the European Reference Network EpiCARE.

The increasingly rapid pace of advancement in genetic testing may lead to inequalities in technical and human resources with a negative impact on optimal epilepsy clinical practice. In this view, the European Reference Network (ERN) for Rare and Complex Epilepsies EpiCARE conducted a survey addressing several aspects of accessibility, availability, costs, and standard practices on genetic testing across ERN EpiCARE centers. An online Google form was sent to 70 representatives of ERN EpiCARE centers. Descriptive statistics and qualitative analysis were used for data presentation. We received 45 responses (1/center) representing 23 European countries with a better representation of Western Europe. Forty-five percent of the centers did not have access to all available types of genetic testing, mainly reflecting the limited availability of whole-genome sequencing (WGS). Thirty-five percent of centers report cost coverage only for some of the available tests, while costs per test varied significantly (interquartile range IQR ranging from 150 to 1173 euros per test across centers). Urgent genetic testing is available in 71.7% of countries (time-to-urgent result: 2 day to 2 months). The average time-to-result of specific tests in case of non-urgent prescription has a significant variance across centers, with the biggest range observed for whole-exome sequencing (6-128 weeks, IQR: 27 weeks). The percentage of agreement among the experts regarding the choice of genetic test at first intention in specific clinical situations was in all cases less than 50 percent (34.9% to 47% according to the proposed scenarios). Costs, time to deliver the results to the clinician, and type of first-line genetic testing vary widely across Europe, even in countries where ERN EpiCARE centers are present. Increased availability of genetic tests and guidance for optimal test choices in epilepsy remains essential to avoid diagnostic delays and excess health costs. The survey of the ERN EpiCARE highlights disparities in genetic testing for epilepsy across 45 ERN EpiCARE centers in 23 European countries. The findings reveal variable access to certain genetic tests, with lowest access to WGS. Costs and time-to-results vary widely. Urgent genetic testing is available in 71.7% of countries. Agreement among experts on first-line genetic tests for specific patient scenarios is below 50%. The study emphasizes the need for improved test availability and guidance to avoid diagnostic delays and unnecessary costs. EpiCARE has the mission to contribute in homogenizing best practices across Europe.

Thermocatalytic decomposition of methane to low-carbon hydrogen using LaNi1-xCuxO3 perovskite catalysts

The thermocatalytic decomposition (TCD) of methane is an attractive alternative to produce low-carbon hydrogen and solid carbonaceous materials. In this study, substituted LaNi1-xCuxO3 perovskite catalysts prepared via self-combustion method were investigated for methane TCD. The effect of Ni partial substitution with Cu, varying gas hourly space velocity, temperature and stability on methane conversions were evaluated. PXRD, H2-TPR, SEM-EDS, TGA, and XPS were used to characterize the catalysts. An increase in Cu loading to 50%, increase in temperature and decrease in GHSV resulted in an improvement in methane conversions to 92%. At a GHSV of 2400 ml/gcat.h, no significant deactivation was observed as the stability of the LaNi0.5Cu0.5O3 catalyst increased from 6 to 20 hours’ TOS with methane conversions maintained at 92% where carbon nanofibers were observed on the surface of the spent catalysts. The study demonstrates the potential to prepare LaNi1-xCuxO3 catalysts and identify optimal testing conditions for the novel production of low-carbon hydrogen with minimal catalyst deactivation.

A novel layer-based optimization method for stabilizing the early printing stage of LPBF process

ABSTRACT The laser power bed fusion (LPBF) forming process introduces heat accumulation and variations in powder layer thickness, which can destabilize the melt track and reduce surface quality. This phenomenon is especially more serious in the early printing stage. To tackle this stability problem, we proposed a novel approach optimizing process parameters on a layer-specific basis. At first, a numerical database was constructed through a set of numerical simulations. Then, a neural network prediction model was trained based on the database. Finally, this prediction model was embedded into a genetic algorithm for layer-based prediction. To verify the prediction results on processing parameters and calibrate the prediction model, physical experiments were prepared. The developed model consistently exhibited relative errors mostly within 6%. It is noteworthy that the relative errors between the numerical simulation results and the expected values were only 0.77% in width and 1.11% in depth. Printing optimization test was applied for an LPBF machine with a Invar alloy powder. The proposed method yielded positive results in both numerical simulations and the printing test. It can be further adopted for new material printing parameter optimization due to an efficient printing stability in the early-stage for LPBF process.

Analysis and Structural Optimization Test on the Collision Mechanical Model of Blade Jun-Cao Grinding Hammer

Aiming at the problems found in grinding Jun-Cao, such as poor grinding effect and high grinding power of mill, this study proposes a blade Jun-Cao grinding hammer based on the traditional hammer mill. With dynamics model analysis, it had better performance than a traditional hammer. By simulating the operation process in the DEM, forces on Jun-Cao and their motions were analyzed. By optimizing the structural parameters of the hammer blade based on multiobjective optimization using the genetic algorithm, an optimal solution set was obtained as a reference for practical production. Meanwhile, a bench test was designed to compare the traditional rectangular hammer with the new blade hammer regarding the operation effect. The result proved the following: (1) cutting edge length, cutting edge thickness and hammer thickness had a significant influence on the grinding effect and grinding power; (2) a total of 22 optimal solution sets were obtained, based on which the blade hammer with a cutting edge length of 45 mm, a cutting edge thickness of 3 mm and a hammer thickness of 7 mm was finally selected in the bench test; (3) the bench test proved that the blade hammer was generally superior to the traditional rectangular hammer with the output per kilowatt-hour having been improved by 13.55% on average.

Dynamic Exploration–Exploitation Pareto Approach for high-dimensional expensive black-box optimization

Surrogate Optimization (SO) plays a vital role in optimizing performance parameters for computationally expensive simulations. However, SO encounters significant challenges in high-dimensional spaces due to the curse of dimensionality, hampering effective point sampling around global optima. In this paper, we introduce “Dynamic Exploration–Exploitation Pareto Approach (DEEPA),” a novel SO method that combines Pareto sampling with a dynamic discretization schema to optimize high-dimensional black-box functions. Unlike traditional SO methods that heavily rely on specific surrogate models, Pareto sampling offers a more adaptable approach. Improvement-based acquisition functions, frequently employed in black-box optimization, are sensitive to model accuracy and tend to prioritize exploitation, potentially missing valuable regions of interest in complex landscapes. Furthermore, they can encounter challenges when dealing with high-dimensional problems due to the curse of dimensionality. DEEPA leverages dynamic coordinate importance to generate samples effectively, providing a solution for addressing high-dimensionality and complex functions. We employ feature selection strategies to assign importance probabilities to perturb each coordinate, demonstrating the impact of importance-based perturbation on convergence to a near-optimal region. We showcase DEEPA’s versatility in fixed-batch evaluation environments using complex global optimization test problems with various topological properties. We compare DEEPA’s performance with state-of-the-art black-box optimization algorithms, and our experimental results demonstrate DEEPA’s superior performance, particularly in complex problems with multiple local minima.

Eigenvalue-based quantum state verification of three-qubit W class states

In quantum many-body systems, W class states are typical examples of states with genuine multipartite entanglement. They have been found to be valuable resources in many quantum information processing tasks. However, the characterization and verification of W class states still remains an intractable problem. Here we first propose an eigenvalue-based verification protocol consisting of four practical adaptive local projective measurement tests, and obtain the optimal test probabilities with particle swarm optimization algorithm. The efficiency achieved by this protocol is far from satisfactory when compared with the theoretical upper bound. Then by utilizing the symmetry of the target states and a class of specific unitaries, we optimize the original verification strategy based on group theory. In this case, our new protocol can realize the efficient verification of three-qubit W class states.

Dynamic constitutive identification of concrete based on improved dung beetle algorithm to optimize long short-term memory model

In order to improve the accuracy of concrete dynamic principal identification, a concrete dynamic principal identification model based on Improved Dung Beetle Algorithm (IDBO) optimized Long Short-Term Memory (LSTM) network is proposed. Firstly, the apparent stress–strain curves of concrete containing damage evolution were measured by Split Hopkinson Pressure Bar (SHPB) test to decouple and separate the damage and rheology, and this system was modeled by using LSTM network. Secondly, for the problem of low convergence accuracy and easy to fall into local optimum of Dung Beetle Algorithm (DBO), the greedy lens imaging reverse learning initialization population strategy, the embedded curve adaptive weighting factor and the PID control optimal solution perturbation strategy are introduced, and the superiority of IDBO algorithm is proved through the comparison of optimization test with DBO, Harris Hawk Optimization Algorithm, Gray Wolf Algorithm, and Fruit Fly Algorithm and the combination of LSTM is built to construct the IDBO-LSTM dynamic homeostasis identification model. The final results show that the IDBO-LSTM model can recognize the concrete material damage without considering the damage; in the case of considering the damage, the IDBO-LSTM prediction curves basically match the SHPB test curves, which proves the feasibility and excellence of the proposed method.

OPTIMIZATION OF GEDONG GINCU MANGO DISTRIBUTION COSTS IN MAJALENGKA DISTRICT

Facing free trade, Indonesia is required to increase the performance of mango production in order to compete with mangoes from other countries, but the performance of gedong gincu mango agribusiness still faces a number of obstacles, both on farm and off farm. One of them is to optimize distribution costs by designing a transportation model to determine the amount of goods that must be sent from each source to each destination in such a way that the total transportation cost is minimum. The transportation model used is Vogel's Approximation Method. The purpose of this research was to find out the description of the distribution of gedong gincu mangoes in Majalengka District, the distribution costs incurred from source to destination and find out the optimization of distribution costs. The method used was quantitative with survey research techniques. The results of this research were an overview of the distribution of Gedong Gincu mangoes involving 5 parties, namely farmers, collectors, dealers, retailers and consumers which can be done with 4 marketing channels. The distribution costs incurred were different, on the four channels the lowest distribution costs are Rp 29,000 and the highest fee is Rp 74,000/kg. In channel I, it did not carry out cost optimization calculations because it did not make shipments out of town, for optimization in channel II it is Rp 13,950,000, channel III is Rp 249,100,000 and channel IV is Rp 250,980,000. Thus, it can be concluded that the lowest cost is on ordinary channel II. Further research is recommended to perform optimal testing by means of a stepping stone.