• 동아시아식생활학회지
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• 제21권5호
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• pp.669-676
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• 2011

This study was conducted to develop a novel salad dressing composite recipe composed of natural seasoning containing dried oak mushroom (Lentirus edodes). Dried oak mushroom (Lentirus edodes) has a better flavor and more nutrients than fresh oak mushroom since vitamins are activated during the drying process. To manufacture salad dressing with Lentirus edodes, dressing with 0%, 3%, 6%, 9%, and 12% added L. edodes were prepared and tested for quality. The pH of the dressing decreased with added L. edodes content, whereas acidity increased but decreased again in the 9% dressing. The L value decreased with added L. edodes content, whereas the a and b values increased but decreased again in the 9% dressing. Sugar content increased with added L. edodes. Rradish strength increased with added oak mushroom. Brittleness and chewiness decreased in the lower percentage dressing, increased in the 9% dressing, but decreased again in the 12% dressing. According to the sensory evaluation results, the highest overall acceptability was 3.3, in the 6% dressing compared to the control group.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• Journal of Biosystems Engineering
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• 제26권2호
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• Journal of Biosystems Engineering
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• 제32권2호
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• pp.130-135
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• 2007

An on-line real time grading and sorting system for dried oak mushrooms was developed for on-site application. Quality grades of the mushrooms were determined according to an industrial specification. Three dimensional visual quality features were used for the grading. A progressive color computer vision system with white LED illumination was implemented to develop an algorithm to extract external quality patterns of the dried oak mushrooms. Cap (top) and gil (stem) surface images were acquired sequentially and side image was obtained using mirror. Algorithms for extracting size, roundness, pattern and color of the cap, thickness, color of the gil and amount of rolled edge of the dried mushroom were developed. Utilizing those quality factors normal and abnormal ones were classified and normal mushrooms were further classified into 30 different grades. The sorting device was developed using microprocessor controlled electro-pneumatic system with stainless buckets. Grading accuracy was around 97% and processing time was 0.4 s in average.

• Journal of Biosystems Engineering
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• 제24권2호
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• pp.153-158
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• 1999

As a basic research for the development of the automatic grading and sorting system for dried oak mushrooms, the device to acquire both cap and gill side images of mushroom has been developed and neural network based side recognition and quality grading has been proposed via inputting both side images. 20 quality grades have been selected considering the requirement of grade classifications imposed by the mushroom company. Developed DC motor driven‘V’type reversing device for the image acquisition of both side images of mushroom showed more than 95% success. Most error was caused by very small size mushrooms with a radius of around 1cm. However, it required a further research to reduce the reversing time. Grading and side recognition were performed via inputting normalized size factors and average gray levels of $8{\times}8$ grids converted from the raw images of both surfaces to the multi-layer back propagation(BP) network. Accuracy of the grading showed about 88.5% and the total grading time including reversing operation was around 2 seconds.

• 한국식품조리과학회지
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• 제26권2호
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• pp.121-128
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• 2010

This study was conducted to develop an optimal composite recipe of nutritional cookies containing oak mushroom (Lentinus edodes) powder that has a high preference score. Oak mushroom(Lentinus edodes) is considered a significantly wholesome food. In addition, the dried oak mushroom(Lentinus edodes) has a better flavor and more nutrients than the fresh oak mushroom since vitamins are activated during the drying process. Wheat flour was partially substituted with Lentinus edodes powder to reduce its content. The optimal sensory composite recipe was determined by making iced cookies which have the advantage of long storage, at 3 concentrations of Lentinus edodes powder, yellow sugar and butter, using the central composite design. In addition, the mixing condition of Lentinus edodes powder cookies was optimized by subjecting the cookies to a sensory evaluation and instrumental analysis using the response surface methodology(RSM). The effects of the addition of the three variables on the quality of Lentinus edodes cookies were assessed in terms of texture, color, spread ratio and sensory evaluation. The results of the sensory evaluation produced very significant values for color, appearance, texture, overall quality(p<0.05), flavor(p<0.01) and the results of instrumental analysis showed significant values in lightness(p<0.05), spread ratio, hardness(p<0.01). As a result, the optimal sensory ratio of Lentinus edodes cookies was determined to be Lentinus edodes powder 10.83g, yellow sugar 61.89 g, and butter 120.0 g.

• 한국식품위생안전성학회지
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• 제2권1호
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• pp.29-34
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• 1987

건조 표고버섯의 위생적이고 안전한 품질 보존법 개발을 위하여 현행 방법인 ethylene oxide 훈증처리와 감마선조사가 저장 상대습도에 따른 시료의 미생물 및 조리시 재수화성과 관능적 특성에 미치는 영향을 비교 검토하였다. 일반 곰팡이의 생육한계 수분 환성도인 0.80과 내건성 곰팡이의 생육한계 최저 수분활성도인 0.64에 해당하는 건조 표고버섯의 평형 수분함량은 $25^{\circ}C$에서 17%, 27%였다. 건조 표고버섯의 미생물 오염은 전세균이 $1.27{\times}10^5/g,\;곰팡이가\;4.80{\times}10^3/g이며,\;대장균군은\;1.20{\times}10^2/g$이 검출되었는데 3 kGy의 감마선조사로서 2-3 log cycles 정도 격감되었고, 대장균은 완전 살균되었으며, 5 kGy 조사로서는 미생물 검출 한계 이하로 살균되어 실온에서 3개월 저장 후에도 이들의 생육은 거의 없었는데 반해 ethylene oxide 처리구는 곰팡이의 살균이 다소 불충분하였다. ethylene oxide 처리와 감마선조사된 건조 표고버섯의 수화도는 각 시험구 모두 첨지온도와 조사선량이 높을수록 흡수속도가 빨랐으며, ethylene oxide 처리구가 충분한 수화에 도달하기 위해서는 조사구에 비해 더 많은 시간이 필요하였다. 또한 조리적성 검토를 위한 관능 실험 결과에서 시료의 풍미는 처리구간에 유의적인 차이가 나타나지 않았으며, 조직감에서는 감마선조사구가 타 처리구에 비해 우수함을 보였다.

• Agricultural and Biosystems Engineering
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• 제4권1호
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• pp.8-15
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• 2003

In Korea, quality evaluation of dried oak mushrooms are done first by classifying them into more than 10 different categories based on the state of opening of the cap, surface pattern, and colors. And mushrooms of each category are further classified into 3 or 4 groups based on its shape and size, resulting into total 30 to 40 different grades. Quality evaluation and sorting based on the external visual features are usually done manually. Since visual features of mushroom affecting quality grades are distributed over the entire surface of the mushroom, both front (cap) and back (stem and gill) surfaces should be inspected thoroughly. In fact, it is almost impossible for human to inspect every mushroom, especially when they are fed continuously via conveyor. In this paper, considering real time on-line system implementation, image processing algorithms utilizing artificial neural network have been developed for the quality grading of a mushroom. The neural network based image processing utilized the raw gray value image of fed mushrooms captured by the camera without any complex image processing such as feature enhancement and extraction to identify the feeding state and to grade the quality of a mushroom. Developed algorithms were implemented to the prototype on-line grading and sorting system. The prototype was developed to simplify the system requirement and the overall mechanism. The system was composed of automatic devices for mushroom feeding and handling, a set of computer vision system with lighting chamber, one chip microprocessor based controller, and pneumatic actuators. The proposed grading scheme was tested using the prototype. Network training for the feeding state recognition and grading was done using static images. 200 samples (20 grade levels and 10 per each grade) were used for training. 300 samples (20 grade levels and 15 per each grade) were used to validate the trained network. By changing orientation of each sample, 600 data sets were made for the test and the trained network showed around 91 % of the grading accuracy. Though image processing itself required approximately less than 0.3 second depending on a mushroom, because of the actuating device and control response, average 0.6 to 0.7 second was required for grading and sorting of a mushroom resulting into the processing capability of 5,000/hr to 6,000/hr.

• 한국버섯학회지
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• 제13권4호
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• pp.301-304
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• 2015

본 연구는 잎새버섯 병 재배시 적합한 배지조성비율을 구명하고자 수행되었다. 배지조성은 참나무톱밥: 옥수수피: 건비지를 건물중량 비율로 T1처리(67:11:22), T2처리(68:15:17), T3처리(74:14:12)의 3처리를 두고 시험한 결과, 발이율은 T1처리에서 72.6%, T2처리에서 72.1%로 두 처리 간 유의적 차이가 없었으나 T3처리에서는 65.8%로서 다른 두 처리에 비하여 낮았다. 이병률은 T2처리에서 4.1%, T3처리에서 3.7%인데 비하여 T1처리에서는 9.8%로 가장 높았다. 수확률은 T1처리에서 64.1%, T3처리에서 63.6%인데 비하여 T2처리에서는 70.5%로 가장 높았다. 병당 자실체 중량은 T1처리에서 85.5 g, T2처리에서 83.3 g으로 두 처리 간 유의적 차이가 없었으나 T3처리에서는 72.4 g으로 다른 두 처리에 비하여 낮았다. 입병 수 10,000병 기준으로 재배 시 수량은 T2처리에서 587 kg으로 T1처리 및 T3처리에 비하여 각각 7% 및 28%가 높았다. 따라서 잎새버섯 병재배 시 적합한 배지조성비율은 건조중량비율로 참나무톱밥: 옥수수피: 건비지가 68:15:17인 것으로 나타났다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.607-614
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• 1996

A computer vision based automatic intelligent sorting system for dried oak mushrooms has been developed. The developed system was composed of automatic devices for mushroom feeding and handling, two sets of computer vision system for grading , and computer with digital I/O board for PLC interface, and pneumatic actuators for the system control. Considering the efficiency of grading process and the real time on-line system implementation, grading was done sequentially at two consecutive independent stages using the captured image of either side. At the first stage, four grades of high quality categories were determined from the cap surface images and at the second stage 8 grades of medium and low quality categories were determined from the gill side images. The previously developed neuro-net based mushroom grading algorithm which allowed real time on-line processing was implemented and tested. Developed system revealed successful performance of sorting capability of approximate y 5, 000 mushrooms/hr per each line i.e. average 0.75 sec/mushroom with the grading accuracy of more than 88%.