• Korean Journal of Microbiology
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• v.53 no.3
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• pp.163-169
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• 2017

Strains D4 and D5 were isolated from peach-rotten soil during the peach harvest season. The isolates were identified based on morphological and biochemical characterization, and identification was determined by 16S rRNA gene sequencing. Results showed that D4 has high similarity to Lactobacillus plantarum ATCC $14917^T$ and Lactobacillus pentosus ATCC $8041^T$ at 99.05% and 98.98%, respectively. D5 was also similar to Lactobacillus pentosus ATCC $8041^T$ and Lactobacillus plantarum ATCC $14917^T$ at 98.71% and 98.64%, respectively. In contrast, isolates showed differences in carbohydrate utilization in comparison to Lactobacillus plantarum ATCC $14917^T$ and Lactobacillus pentosus ATCC $8041^T$. In view of this we performed VITEK MS matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis, multiplex PCR fingerprinting, and random amplified polymorphic DNA (RAPD)-PCR to further confirm the identification of D4 and D5. The results of these analyses showed that both strains were most similar to Lactobacillus plantarum.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.286-296
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• 2019

Land suitability assessments have been a crucial issue for enhancing productivity in agriculture and conserving agricultural lands. Based on soil and climate information, land suitability assessment for peaches (Prunus persica) and grapes (Vitis vinifera L.) were conducted using the maximum limiting characteristic method (MLCM) in South Korea. In peaches, S1 (highly suitable) exists on 2.21% of the land, S2 (moderately suitable) on 19.20%, N1 (currently not suitable) on 12.07%, and N2 (permanently not suitable) on the remaining 66.52%. In grapes, 3.65% of the land is classified as S1, 17.98% as S2, 11.85% as N1 and 66.52% as N2. In both fruit trees, the results acquired from soil and climatic information were similar to those from soil information alone. The data also suggest that the grades by soil information were relatively low over the land. With the assumption that the more suitable area a province has, the more will be cultivated for the fruit trees, we compared the percentages of area for peach and grape farming per province with the results by MLCM, and suggested that some provinces with a small percentage of farm can be encouraged to plant more in suitable areas as dictated by MLCM for the species. In the near future, we plan to use an advanced method such as analytic hierarchy process (AHP) to conduct similar tests, in which having reference data of yields or benefits per farm can efficiently increase the accuracy of the measurements.