Please use this identifier to cite or link to this item:
DC FieldValueLanguage
dc.contributorSuming Chenen_US
dc.contributorChyung Ayen_US
dc.contributorJar-Miin Luanen_US
dc.contributorChing-Chen Hsiehen_US
dc.contributorChing-Lu Hsiehen_US
dc.contributorChing-Wei Chengen_US
dc.contributorYu-I Huangen_US
dc.contributorKuang-Wen Hsiehen_US
dc.contributor.advisorChung-Teh Shengen_US
dc.contributor.authorTsai, Chao-Yinen_US
dc.identifier.citation1.李匡邦、許東明、何東英。1997。光譜化學分析。初版。台北:楊智文化事業股份有限公司。 2.林育菁。2002a。碩士論文。蓮霧及木瓜內部品質之近紅外光檢測。台北:台灣大學生物產業機電工程學研究所。 3.林佳芬。2002b。碩士論文。CCD 影像儀光學系統測試之效能分析。台南:成功大學物理研究所。 4.邵穗鵬。2004。碩士論文。光學透鏡系統實例設計與評估。中壢:中央大學機械工程研究所。 5.張文宏。1999。博士論文。水果品質檢測模式之研究。台北:台灣大學生物產業機電工程學研究所。 6.張阜權,孫榮山,唐偉國。1995。光學。初版。新竹:凡異出版社。 7.陳世銘、江昭皚、謝廣文、邱奕志、謝俊夫、蔡兆胤、洪辰雄、李經緯、陳加增、楊宜璋、蔡志成、黃政偉、莊永坤、楊昇穎、陳冠宏、陳志杰、徐子建、郭景成。2006。水果品質非破壞性線上檢測系統之開發。台灣農業機械21(1):11-12。 8.陳哲生。2003。碩士論文。適用於生醫感測器之自動化多通道訊號檢測電路及系統之設計。中壢:中原大學電子工程學系碩士班。 9.陳德請。2002。遠紅外線在生物科技之應用。機電整合雜誌 (1): 134-154。 10.曾銀彬。2002。碩士論文。電腦圖控程式應用於水果糖度分級之研究。屏東:屏東科技大學機械工程系碩士班。 11.黃永基。1988。近紅外線分析儀在加工食品製造管理上之應用。食品資訊 (48): 17-24。 12.楊文振。2000。國產蔬果品牌標準與規格介紹。高雄區農業專訊 第33期。 13.楊富強。2006。碩士論文。以路徑規劃重量分級雛型機之研製。嘉義:嘉義大學生物機電工程學系碩士班。 14.蔡兆胤、謝俊夫、盛中德。2005。砷化銦鎵多通道檢波器水果檢測系統之研製。農業機械學刊 14(1): 33-47。 15.蔡兆胤。2000。碩士論文。近紅外線檢測之應用—小番茄糖度檢測單元之研製。屏東:屏東科技大學機械工程系碩士班。 16.盧宏嘉。2001。碩士論文。機器視覺應用於蓮霧檢測單元之開發研究。屏東:屏東科技大學機械工程系碩士班。 17.蕭子健、王智昱、儲昭偉。2006。虛擬儀控程式設計LabVIEW 7X。初版三刷。台北:高立圖書有限公司。 18.Ames Photonics Inc. 2007. Temperature Compensated Dark Correction Technology. Available at: Accessed 9 July 2007. 19.Blanco, M. and I. Villarroya. 2002. NIR spectroscopy: a rapid-response analytical tool. Trends in Analytical Chemistry. 21(4): 240-250. 20.Burns, D. A. and E. W. Ciurczak. 1992. Handbook of Near-infrared Analysis Practical Spectroscopy. V.13. New York: Marcel Dekker, Inc. 21.Carlomagno, G., L. Capozzo, G. Attolico, and A. Distante. 2004. Non-destructive grading of peaches by near-infrared spectrometry. Infrared Physics & Technology. 46: 23-29. 22.Chang, W. H., S. Chen, and C. C. Tsai. 1998. Development of a universal algorithm for use of NIR in estimation of soluble solids in fruit juices. Transactions of American Society of Agricultural Engineers. 41(6): 1739-1745. 23.Chen, C. P. and J. T. Shaw. 1999. Determination of the sugar content and acidity of pears by a portable near-infrared spectrophotometer. Journal of Agricultural Machinery. 8(1): 49-57. 24.Chen, S., C. Y. Tsai, J. F. Hsieh, C. H. Hung, Y. C. Chiu, K. W. Hsieh, J. A. Jiang, R. L. C. Chen, H. C. Yang, C. T. Chen, I. C. Yang, C. W. Yang, T. H. Wu, M. T. Li, C. W. Huang, C. C. Huang, C. C. Tsai, C. K. Yang, and P. Brimmer. 2004. Growth status monitoring and quality evaluation for bio-production and products using spectral sensing techniques. In “Proceedings of the Second International Symposium on Machinery and Mechatronics for Agriculture and Bio-systems Engineering”, Keynote Speech, KN-9-23. Kobe, Japan: Kobe University. 25.Greensill, C. V. and D. S. Newman. 2001. An experimental comparison of simple NIR spectrometers for fruit grading applications. Applied Engineering in Agriculture. 17(1): 69–76. 26.He, Y., Y. Zhang, A. G. Pereira, A. H. Gomrz, and J. Wang. 2005. Nondestructive determination of tomato fruit quality characteristics using Vis/NIR spectroscopy technique. International Journal of Information Technology. 11(11): 97-108. 27.Hecht, E. 1998. Optics. 3rd ed. New York: Addison Wesley Longman Inc. 28.Herrera, J., A. Guesalaga, and E. Agosin. 2003. Shortwave–near infrared spectroscopy for non-destructive determination of maturity of wine grapes. Measurement Science and Technology. 14: 689-697. 29.Hsieh, C. and Y. Lee. 2006. Applied visible/near-infrared spectroscopy on detecting the sugar content and hardness of pearl guava. Applied Engineering in Agriculture. 21(6): 1039-1046. 30.Huck, W., W. Guggenbichler, and G. K. Bonn. 2005. Analysis of caffeine, theobromine and theophylline in coffee by near infrared spectroscopy (NIRS) compared to high-performance liquid chromatography (HPLC) coupled to mass spectrometry. Analytica Chimica Acta. 538: 195-207. 31.Kawano, S., H. Watanabe, and M. Iwamoto. 1989. Determination of sugar content in intact peach by NIR spectroscopy. Journal of Japan Society of Horticultural Science. Suppl. 2, 604-605. 32.Kawano, S., T. Fujiwara, and M. Iwamoto. 1992. Non-destructive determination of sugar content in Satsuma Mandarin using near infrared transmittance. Journal of Japan Society of Horticultural Science. 62(2): 465-470. 33.Kondo, N., U. Ahmad, M. Monta, and H. Murase. 2000. Machine vision based quality evaluation of Iyokan orange fruit using neural networks. Computers and Electronics in Agriculture. 29: 135-147. 34.Lambda Research Corporation. 2007. Software. Available at: Accessed 9 July 2007. 35.Lammertyn, J., B. Nicolai, K. Ooms, V. D. Smedt, and J. D. Baerdemaeker. 1998. Nondestructive measurement of acidity, soluble solids, firmness of Jonagold apples using NIR-spectroscopy. American Society of Agricultural Engineers. 41(4): 1089-1094. 36.Liu, Y. D., Y. B. Ying, and X. P. Fu. 2005. Study on predicting sugar content and valid acidity of apples by near infrared diffuse reflectance technique. Spectroscopy and Spectral Analysis. 25(11): 1793-1796. 37.Lu, D. L., S. Lin, and B. Chen. 2005. Rapid determination of alcohol content in beer by FT-NIR. Liquor-making Science and Technology. 4: 87-89. 38.Lu, H. S., H. R. Xu, Y. B. Ying, X. P. Fu, H. Y. Yu, and H. Q. Tian. 2006. Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits. Journal of Zhejiang University SCIENCE B. 7(10): 794-799. 39.Lu, R. 2001. Predicting firmness and sugar content of sweet cherries using near infrared diffuse reflectance spectroscopy. American Society of Agricultural Engineers. 44(5): 1265–1271. 40.Lu, R. and D. Ariana. 2002. A near-infrared sensing technique for measuring internal quality of apple fruit. Applied Engineering Agriculture. 18(5): 585-590. 41.Lu, Y. J., Y. L. Qu, Z. Q. Cao, and M. Song. 2006. Near Infrared Determination of the total sugar in Chinese Ginsengs. Spectroscopy and Spectral Analysis. 26(8): 1457-1459. 42.Malacara, D. and Z. Malacara. 1994. Handbook of lens design. E-book. New York: Marcel Dekker, Inc. 43.Marquez, A. J., A. M. Díaz, and M. I. P. Reguera. 2005. Using optical NIR sensor for on-line virgin olive oils characterization. Sensors and Actuators B. 107: 64-68. 44.Martens, H., and T. Næs. 1989. Multivariate Calibration. 4th ed. New York: Wiley. 45.Menn, N. 2004. Practical Optics. E-book. Burlington, MA: Elsevier. 46.Miller, W. M. and M. Zude-Sasse. 2004. NIR-based sensing to measure soluble solids content of Florida citrus. Applied Engineering in Agriculture. 20(3): 321-327. 47.Mohsenin, N. N. 1984. Electromagnetic Radiation Properties of Food and Agricultural Products. New York: Gordon and Breach Science Publishers. 48.Pla, M., P. Hernández, B. Ariño, J. A. Ramírez, and I. Díaz. 2007. Prediction of fatty acid content in rabbit meat and discrimination between conventional and organic production systems by NIRS methodology. Food Chemistry. 100: 165-170. 49.Sadoulet, S. and T. P. Kennedy. 2007. Integration of Optical Systems. Available at: Accessed 9 July 2007. 50.Saranwong, S., J. Sornsrivichai, and S. Kawano. 2004. Prediction of ripe-stage eating quality of mango fruit from its harvest quality measured nondestructively by near infrared spectroscopy. Postharvest Biology and Technology. 31: 137-145. 51.Schaare, P. N. and D. G. Fraser. 2000. Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit. Postharvest Biology and Technology. 20: 175–184. 52.Schmilovitch, Z., A. Mizrach, A. Hoffman, H. Egozi, and Y. Fuchs. 2000. Determination of mango physiological indices by NIR spectrometry. Postharvest Biology and Technology. 19: 245–252. 53.Shahin, M. A., E. W. Tollner, R. W. McClendon, and H. R. Arabnia. 2002. Apple classification based on surface bruises using image processing and neural networks. Transactions of the ASAE, 45(5): 1619-1627. 54.Shao, Y. and Y. He. 2007. Non-destructive measurement of the internal quality of bayberry juice using Vis/NIR spectroscopy. Journal of Food Engineering. 79: 1015-1019. 55.Sirisomboon, P., M. Tanaka, S. Fujita, and T. Kojima. 2007. Evaluation of pectin constituents of Japanese pear by near infrared spectroscopy. Journal of Food Engineering. 78: 701-707. 56.Studman, J. 2001. Computers and electronics in postharvest technology - a review. Computers and Electronics in Agriculture. 30: 109-124. 57.Wang, Y., K. X. Xu, and M. Chang. 2006. Study on multi-band NIR spectroscopy for the determination of fat and protein contents in milk. Optical Instruments. 28(3): 3-7. 58.Williams, P. and K. Norris. 1990. Near infrared technology in the agricultural and food industries. Second Printing. Minnesota, USA: American Association of Cereal Chemists Inc. 59.Yuan, L., H. J. Liu, G. Y. Yu, and Y. J. Zheng. 2006. Non-destructive testing for quality parameters in orange by NIR. Chinese Journal of Spectroscopy Laboratory. 23(4): 820-822.zh_TW
dc.description.abstract近紅外線光譜技術具有精確、快速、低成本、不需使用化學藥品、不會污染環境、不需侵入及非破壞等多項優點,已廣泛應用於農畜產品之檢測,又現代消費者對水果內部品質的要求不斷提高,利用近紅外線進行水果內部品質檢測的趨勢已不可擋。為發展國內近紅外線水果線上檢測之能力,研發製作設備之關鍵技術,使用輸送帶、檢測室、照明光源、聚光鏡頭、單光儀、檢波器、陶瓷白板、電控設備及電腦等零組件研製一台雛型系統,該系統包含照明光源、檢測、白板校正及控制等四個單元。 本研究以OSLO光學設計模擬軟體來設計包含照明光源及檢測單元之光學系統。研發之白板校正機構可自動、快速及穩定地於線上進行白板校正。使用汞氬燈進行系統對焦調整、測量系統光學解析度及校正系統波長,大幅提高設備之光學貫通量,縮短檢波器之曝光時間為最低值8 ms,使系統測量速度達1.3 sec/sample。 以研發之雛型機動態檢測印度棗完整果與削皮果,及蓮霧完整果等三種水果之糖度,用數次測量之平均光譜消除測量之隨機誤差,使隨機雜訊不致超過可用信號,所建立預測糖度之模式其判定係數R2值都高於0.82,標準校正誤差SEC值都低於0.707,具有相當好的預測效能。在預測誤差為±1 ° Brix之條件下,除印度棗完整果外,其餘之兩種水果的預測正確率都高達90%以上。zh_TW
dc.description.abstractNIR spectroscopy has the advantages of precision, rapid-response, low cost, chemical-free, pollution-free, non-invasive and non-destructive. Hence, it has been widely adopted in agriculture and the food industry. The present consumers are more concerned about the internal quality of fruit than the old comsumers. And the internal quality can be precisely determined with the NIR spectroscopy technology. The essential elements of establishing a NIR online detecting system have been studied solidly in this research. The assembled prototype system consists of conveyor, chamber, light source, lens, monochromator, detector, white ceramic board, automation control elements and computer. The whole system includes the following four units which are the light source, the detecting, the reference calibration and the controlling. OSLO software has adopted in this research to design the optical system including the light source and detecting units. The online reference calibration can be carried out automatically, fast and stably with the developed unit. Focusing, measuring the optical resolution, and calibrating the wavelength of the system are done with an Hg-Ar calibration lamp. Through the developed focusing work, the optical throughput has been significantly increased, so the exposure time can be reduced to the lowest limit of 8ms. And the detecting speed of system is further reduced to 1.3s per fruit. The developed prototype system is used to measure the sugar contents of intact and peeled jujubes, and intact bell fruit online. The system will take the average of several spectroscopy measurements to eliminate the random noise of measurement to prevent the level of noise to be higher than the useful signal level. The developed model, which can be used to predict the sugar content, shows good prediction capability. The value of R2 is higher than 0.82 and the value of SEC is lower than 0.707, which can be used to demonstrate the system performance. The prediction percentage is over 90% and the prediction deviation is 1 Brix error in most cases except the intact jujube.en_US
dc.description.tableofcontents摘要 I Abstract II 目錄 III 圖目錄 VI 表目錄 IX 符號表 X 第一章 緒論 1 1-1 研究背景及動機 1 1-2 研究目的 2 第二章 文獻探討 4 2-1 光學 4 2-1-1 光學系統設計程序 4 2-1-2 光線覓跡與光矩陣 5 2-1-3 玻璃折射率 7 2-1-4 光學系統設計模擬軟體 7 2-2 近紅外線光譜技術 9 2-2-1 電磁波之種類及其應用 9 2-2-2 光與物質之作用 10 2-2-3 光柵之基本作用原理 11 2-2-4 比爾定律 12 2-2-5 近紅外線光譜技術之相關研究 12 2-2-6 建立預測模式 14 2-3 測量及控制 17 2-3-1 LabVIEW圖形語言程式軟體 17 2-3-2 LabVIEW之相關研究 17 第三章 實驗設備材料與方法 19 3-1 系統基本架構 19 3-2 照明光源單元設計 20 3-2-1 背景條件說明 20 3-2-2 設計構想與方法 21 3-2-3 儀器與主要零件 22 3-3 檢測單元設計 25 3-3-1 背景條件說明 25 3-3-2 設計構想與方法 26 3-3-3 主要零件 26 3-4 白板校正單元設計 29 3-4-1 背景條件說明 29 3-4-2 設計構想與方法 30 3-4-3 主要零件 30 3-5 控制單元設計 32 3-5-1 背景條件說明 32 3-5-2 設計構想與方法 32 3-5-3 主要零件 33 3-6 系統調整與校正 35 3-6-1 儀器設備 35 3-6-2 光學系統對焦調整 36 3-6-3 系統解析度測量 36 3-6-4 系統波長校正 37 3-7 水果動態測量實驗 37 3-7-1 儀器設備 37 3-7-2 實驗材料 38 3-7-3 實驗方法 39 3-8 水果靜態測量實驗 43 3-8-1 儀器設備 43 3-8-2 實驗材料 43 3-8-3 實驗方法 43 第四章 結果與討論 45 4-1 照明光源單元設計 45 4-1-1 光學系統設計 45 4-1-2 機構設計 49 4-2 檢測單元設計 51 4-2-1 光學系統設計 51 4-2-2 高通濾光鏡的選用 54 4-2-3 平衡濾光鏡的選用 55 4-3 白板校正單元設計 57 4-3-1 機構設計 57 4-3-2 穩定性實驗 57 4-4 控制單元設計 61 4-4-1 硬體設計 61 4-4-2 軟體設計 62 4-5 系統調整與校正 73 4-5-1 光學系統對焦調整 73 4-5-2 系統解析度的測量 77 4-5-3 系統波長校正 78 4-6 水果動態測量實驗 81 4-6-1 輸送速度設定 81 4-6-2 印度棗之檢測 87 4-6-3 蓮霧之檢測 94 4-7 水果靜態測量實驗 100 4-7-1 結果 100 4-7-2 討論 101 第五章 結論與建議 103 5-1 結論 103 5-2 建議 104 第六章 參考文獻 105 第七章 附錄 111zh_TW
dc.subjectonline detectingen_US
dc.subjectCCD detectoren_US
dc.titleDevelopment of a Continuous Online Detecting System for Fruits Using Near Infrared Technologyen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
Appears in Collections:生物產業機電工程學系
Show simple item record
TAIR Related Article

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.