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표제지 2

목차 8

Ⅰ. 딥러닝이 통합된 모바일 소프트웨어를 이용한 potassium permanganate 기반 에틸렌 지시계를 통한 바나나와 키위의 신선도 판독 16

1. 서론 17

2. 재료 및 방법 20

2.1. 바나나 및 키위 준비 20

2.2. 에틸렌 지시계 제작 20

2.3. Potassium permanganate 농도 결정 23

2.4. 과일의 부패 시점 결정 23

2.5. 저장 중 과일 품질 결정 24

2.6. 주성분 분석을 이용한 부패 시점에서의 과일 품질 특성의 다변량 분석 25

2.7. 에틸렌 지시계 신선/부패 이미지를 이용한 ResNet50 모델 학습과 모델 작동 평가 26

2.8. ResNet50 모델 학습을 적용한 모바일 어플리케이션 개발 27

2.9. 통계 분석 27

3. 결과 및 고찰 28

3.1. Potassium permanganate 농도 결정 28

3.2. 과일의 부패 시점 결정 34

3.3. 주성분 분석을 이용한 부패 시점에서의 과일 품질 특성의 다변량 분석 37

3.4. 에틸렌 지시계 신선/부패 이미지를 이용한 ResNet50 모델 학습과 모델 평가 41

3.5. ResNet50 모델 학습을 적용한 모바일 어플리케이션 개발 44

4. 결론 46

Ⅱ. 딥러닝이 통합된 모바일 소프트웨어를 이용한 dual system 에틸렌-이산화탄소 지시계를 통한 바나나와 키위의 신선도 판독 47

1. 서론 48

2. 재료 및 방법 51

2.1. 에틸렌 지시계 개발 51

2.2. 이산화탄소 지시계 제작 54

2.3. 지시계 적용 포장 재료 결정 55

2.4. Dual system 신선도 지시계 적용 과일 포장재 준비 56

2.5. 과일의 품질 평가 60

2.6. 지시계 이미지를 이용한 ResNet50 모델 학습 61

2.7. ResNet50 모델 학습을 통한 소프트웨어 개발 62

2.8. 통계 분석 62

3. 결과 및 고찰 63

3.1. 에틸렌 지시계 개발 63

3.2. 이산화탄소 지시계 제작 73

3.3. 지시계 적용 포장 재료 결정 76

3.4. Dual system 신선도 지시계의 적용성 확인 81

3.5. 지시계 이미지를 이용한 ResNet50 모델 학습 85

3.6. ResNet50 모델 학습을 통한 모바일 소프트웨어 개발 88

4. 결론 90

참고 문헌 92

ABSTRACT 105

표목차 11

Table 1. Color changes in the ethylene indicators made of KMnO₄ (the solution of KMnO₄ at 0.1 (A), 0.5 (B), and... 29

Table 2. The effect of KMnO₄ concentration (the solution of KMnO₄ at... 32

Table 3. Performance of ResNet50 in differentiating the labelled... 43

Table 4. Color changes in the carbon dioxide indicators at different... 74

Table 5. Changes in the concentration of carbon dioxide in banana... 80

Table 6. Performance of ResNet50 in differentiating the labelled dual... 87

그림목차 12

Figure 1. Images of the banana and kiwi samples with the potassium... 22

Figure 2. Nonlinear regression (NR) analysis of color changes (ΔE) in... 33

Figure 3. Changes in the total aerobic bacterial counts in bananas (A)... 35

Figure 4. Changes in the yeast and mold counts of kiwi (A) and colors... 36

Figure 5. Principal component analysis (PCA) of fresh and spoil stages of... 40

Figure 6. Changes in the accuracy (A) and loss (B) rates for training and... 42

Figure 7. User interface of the developed mobile software that indicates... 45

Figure 8. Structures of the dual system freshness indicator 58

Figure 9. Images of the polypropylene pouches installed with the dual... 59

Figure 10. Absorbance in the PdSO₄-ammonium molybdate (AM) solution... 65

Figure 11. Changes in the colors of ethylene indicators at different... 67

Figure 12. Fourier transform infrared spectroscopy spectra of the... 70

Figure 13. X-ray diffraction patterns of the agarose, indicator prepared... 71

Figure 14. X-ray photoelectron spectroscopy spectrum, Mo3d fitting of... 72

Figure 15. Changes in the colors of carbon dioxide indicators at different... 75

Figure 16. Changes in carbon dioxide concentration and the ΔE values... 79

Figure 17. Changes in total aerobic bacterial counts of banana (A), the... 83

Figure 18. Changes in yeast and mold counts of kiwi (A), the ethylene... 84

Figure 19. Changes in the accuracy (A) and loss (B) rates of the... 86

Figure 20. User interface of mobile application that indicates the... 89

초록보기

Ⅰ. Assessing the freshness of bananas and kiwis with a potassium permanganate-based ethylene indicator using deep learning-based mobile software

This study investigated the applicability of a potassium permanganate(KMnO₄)-containing ethylene indicator in sensing the freshness of bananas and kiwis and explored the feasibility of the use of deep learning to interpret the indicator outcomes. This research also developed a mobile software that adapted the trained deep learning model. The concentrations of KMnO₄ impregnated in the indicator base material (filter paper) were 0.1, 0.5, and 1.0% (w/v). Bananas or kiwis were placed in a polypropylene pouch attached to the ethylene indicator and stored at 25 ℃ for 10 days. The color of the indicator, total aerobic bacterial count, yeast and mold count, ethylene gas concentration in the pouch, total soluble solid content, titratable acidity, firmness, and weight loss of the fruits were analyzed during storage. The indicator image dataset demonstrating freshness and spoilage was labeled and trained using ResNet50. High correlations were observed between the ethylene gas concentration and color change in the "0.5% KMnO₄ indicator." The color of the indicator changed from purple to brown during the spoilage of bananas and kiwis from day 7 to 10. The spoilage time points determined by microbial counts were validated by principal component analysis. The ResNet50 model demonstrated 100% accuracy in predicting the freshness of both fruits. The mobile software developed using the trained model quickly identified the indicator images on fruit packages. Overall, the ethylene indicator was found suitable for indicating the freshness of bananas and kiwis, and the mobile software rapidly and accurately assessed the fruit freshness.

Ⅱ. Establishment of a freshness identification system for climacteric fruits using a dual system ethylene-carbon dioxide indicator with deep learning-based mobile software in the food supply chain

This research developed a dual system freshness indicator, consisting of an ammonium molybdate (AM)-based ethylene indicator and a methyl red-based carbon dioxide indicator, which changed its color reacting with ethylene and carbon dioxide gases. Banana or kiwi was placed in a PP attached with the dual system freshness indicator, stored at 25 ℃ for 10 days or 12 days, respectively, and analyzed for changes in the color of dual system freshness indicator, as well as the total aerobic bacterial counts and yeast and mold counts in the fruits and ethylene gas concentration, and carbon dioxide gas concentration inside the pouch. Indicator images representing freshness and spoilage were labelled as a dataset and trained using a deep learning model (ResNet50). The trained model was installed in mobile software that identifies the freshness of the fruits. Among the concentrations of AM in the PdSO₄-AM solution (1, 2, and 3%), the 1% solution resulted in the highest distinctive color change responding to different concentrations of ethylene gases. The PP pouch was more suitable for the carbon dioxide indicator application with appropriate carbon dioxide permeation than nylon PE pouch. The color of the ethylene indicator prepared with the PdSO₄-AM solution containing AM at 1%. For bananas, spoilage occurred on the 9th day of storage based on the total bacterial count criterion. At this point, the ethylene indicator changed from yellow to blue, while the carbon dioxide indicator changed from red to yellow. while the color of the carbon dioxide indicator shifted from red to yellow and then to orange. In the case of kiwis, spoilage was observed on the 12th day of storage based on the yeast and mold count threshold. At this stage, the ethylene indicator exhibited a color change, while the carbon dioxide indicator showed no color change. The ResNet50 model demonstrated 100% accuracy in predicting freshness of both fruits. Moreover, the mobile software integrated with the trained ResNet50 model was rapidly judging the freshness of fruit. The results demonstrated that the dual system freshness indicator effectively assessed the freshness of bananas and kiwis and the mobile software incorporating the trained deep learning model enabled rapid evaluation of freshness of the fruits.

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