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  1. NTU Theses and Dissertations Repository
  2. 生物資源暨農學院
  3. 生物機電工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59961
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor陳世銘(Su-Ming Chen)
dc.contributor.authorSheng-Yung Linen
dc.contributor.author林聖詠zh_TW
dc.date.accessioned2021-06-16T09:47:29Z-
dc.date.available2025-08-17
dc.date.copyright2020-09-17
dc.date.issued2020
dc.date.submitted2020-08-17
dc.identifier.citation林敏。2014。青江菜硝酸鹽含量檢測及栽培策略之建立。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
沈明來。2014。試驗設計學。第五版,41-308。台北:九州圖書。
陳加增、陳世銘、楊蕙綺、楊宜璋、蕭世傑。2006。蔬菜葉片氮含量之近紅外光反射光譜分析。農業機械學刊。15(4): 39-52。
陳世銘、蔡兆胤。2016。近紅外光量測。出自'生物系統量測',初版,2.32-2.43。艾群主編。台中: 滄海。
黃君席。2011。以光譜影像技術檢測葉菜類蔬菜之硝酸鹽含量。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
潘姵如。2012。葉菜類蔬菜硝酸鹽含量光譜影像檢測系統及栽培策略之建立。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
潘瑞熾。2013。植物生理學。第二版,56-62。新北市:藝軒。
劉峻銘。2016。應用小型攜帶式光譜設備進行葉菜硝酸鹽含量快速檢測之研究。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
蕭文龍。2009。多變量分析最佳入門實用書。第二版,8.1-8.34。台北:碁峰。
簡佑佳。2014。應用螢光光譜指紋技術檢測食用生菜汙染之研究。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
羅聖傑。2009。茶葉發酵度之高光譜影像檢測。碩士論文。台北:國立台灣大學生物產業機電工程學研究所。
Abdulridha, J., O. Batuman, and Y. Ampatzidis. 2019. UVA-based remote sensing technique to detect citus canker disease utilizing hyperspectral imaging and machine learning. Remote Sensing 1373: 1-22.
ADAS. 2019. Nitrate Surveillance Programme: May 2014 – April 2019. Final Report to Standards Agency. Available at: https://www.food.gov.uk/sites/default/files/media/document/nitrate-surveillance-programme-report-2014-2019.pdf. Accessed 22 May 2020.
Ariana, D. P., R. Lu, and D. E. Guyer. 2006. Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers. Computers and Electronics in Agriculture 53: 60-70.
Ariana, D. P., and R. Lu. 2008. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging: Part I. Development of a prototype. Sensing and Instrumentation for Food Quality and Safety 2: 144-151.
Anjana, S. U., and M. Iqbal. 2006. Nitrate accumulation in plants, factors affecting the process, and human health implications. A review. Agronomy for Sustainable Development 27: 45-57.
Barnes, R. J., M. S. Dhanoa, and S. J. Lister. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Society for Applied Spectroscopy 43(5): 772-777.
Blanco, M., and I. Villarroya. 2002. NIR spectroscopy: a rapid-response analytical tool. Trends in Analytical Chemistry 21(4): 240-250.
Burgess, C., and J. Hammond. 2007. Wavelength standards for the near-infrared spectral region. Spectroscopy 22(4): 40-48.
Cataldo, D. A., M. Haroon, L. E. Schrader, and V. L. Youngs. 1975. Rapid colorimetric determination of nitrate in plant tissue. Soil Science and Plant Analysis 6(1): 1-30.
Chia, A. Y. S., M. K. H. Leung, H. Eng, and S. Rahardja. 2007. Ellipse detection with Hough Transform in one dimensional parametric space. IEEE International Conference on Image Processing 5: 333-336.
Chauchard, F., J. M. Roger, and V. Bellon-Maurel. 2004. Correction of the temperature effect on near infrared calibration-application to soluble solid content prediction. Journal of Near Infrared Spectroscopy 12: 199-205.
Colla, G., H. Kim, M. C. Kyriacou, and Y. Rouphael. 2018. Nitrate in fruits and vegetables. Scientia Horticulturae 237: 221-238.
Davies, A. M. C. and A. Grant, 1987. Review: Near-infrared analysis of food. Internal Journal of Food Science and Technology 22: 191-207.
Edison Opto Corp. 2015. Edixeon A1 Series Datasheet. Taiwan: Edison Opto Corp. Available at: http://pdf.lingonet.com.tw/ab20181220-132666618.pdf. Accessed 31 July 2020.
Elmasry, G., and D. W. Sun. 2010. Principles of hyperspectral imaging technology. In “Hyperspectral Imaging for Food Quality Analysis and Control”, ed. D. W. Sun, 3-43. Amsterdam: Elsevier Inc.
Elmasry, G., M. Kamruzzaman, D. W. Sun, and P. Allen. 2012. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: a review. Critical Reviews in Food Science and Nutrition 52(11): 999-1023.
Foss NIRSystems Inc. 2005. A Brief Introduction to NIR Spectroscopy. USA: NIRSystems Inc. Available at: http://www.winisi.com/NIRS_theory.htm. Accessed 16 July 2020.
Galactic. 1997. PLSplus/IQ User’s Guide. Salem, NH, U.S.A.: Galactic Industries Corporation.
Gowen, A. A., C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias. 2007. Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends in Food Science and Technology 18: 590-598.
Gowen, A. A., C. P. O’Donnell, M. Taghizadeh, E. Gaston, A. O’Gorman, P. J. Cullen, J. M. Frias, C. Esquerre, and G. Downey. 2008. Hyperspectral imaging for the investigation of quality deterioration in sliced mushrooms (Agaricus bisporus) during storage. Sensing and Instrumentation for Food Quality and Safety 2: 133-143.
Gunasekaran, S., and J. Irudayaraj. 2001. Optical methods: visible NIR and FTIR spectroscopy. In: Nondestructive Food Evaluation. Techniques to Analysis Properties and Quality. New York, Marcel Dekker Inc.
Itoh, H., S. Kanda, H. Matsuura, N. Shiraishi, K. Sakai, and A. Sasao. 2010. Measurement of nitrate concentration distribution in vegetables by near-infrared hyperspectral imaging. Environmental Control in Biology 48(2): 37-49.
Kandpal, L. M., S. Lohumi, M. S. Kim, J. Kang, and B. Cho. 2016. Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sensors and Actuators B: Chemical 229: 534-544.
Khan, K. A., Z. Yan, and D. He. 2018. Impact of light intensity and nitrogen of nutrient solution on nitrate content in three lettuce cultivars prior to harvest. Journal of Agricultural Science 10(6): 99-109.
Kim, M. S., Y. R. Chen, and P. M. Mehl. 2001. Hyperspectral reflectance and fluorescence imaging system for food quality and safety. American Society of Agricultural Engineers 44(3): 721-729.
Lawrence, K. C., B. Park, W. R. Windham, and C. Mao. 2003. Calibration of a pushbroom hyperspectral imaging system for agricultural inspection. Transactions of American Society of Agricultural Engineers 46(2): 513-521.
Lin, J. K., and J. Y. Yen. 1980. Changes in the nitrate and nitrite contents of fresh vegetables during cultivation and post-harvest storage. Food and Cosmetics Toxicology 18: 597-603.
Lo, Y., C. Chan, and D. Chang. 2012. Comparison on nitrate, vitamin C and soluble solid content of different vegetables. Seed and Nursery 14(1): 41-54. Doi: http://dx.doi.org/10.30077/SN.201203.0004
Ma, L., L. Hu, X. Feng, and S. Wang. 2018. Nitrate and nitrite in health and disease. Aging and Disease 9(5): 938-945.
McGlone, V. A., D. G. Fraser, R. B. Jordan, and R. Ku ̈nnemeyer. 2003. Internal quality assessment of mandarin fruit by vis/NIR spectroscopy. Journal of Near Infrared Spectroscopy 11: 323-332.
National Instruments Corp. 2020. USB-6210 Multifunction I/O Device. USA: National Instruments Corp. Available at: https://www.ni.com/en-us/support/model.usb-6210.html. Accessed 30 May 2020.
Norris, K. H. 1964. Design and development of a new moisture meter. Journal of Agricultural Engineering 45(7): 370-372.
Peiris, K. H. S., G. G. Dull, R. G. Leffler, and S. J. Kays. 1998. Near-infrared(NIR) spectrometric technique for nondestructive determination of soluble solids content in processing tomatoes. American Society for Horticultural Science 123(6): 1089-1093.
Prasad, S., and A. A. Chetty. 2008. Nitrate-N determination in leafy vegetables: Study of the effects of cooking and freezing. Food Chemistry 106(2008): 722-780.
Qiao, J., M. O. Ngadi, N. Wang, C. Garie ́py, and S. O. Prasher. 2007. Pork quality and marbling level assessment using a hyperspectral imaging system. Journal of Food Engineering 83: 10-16.
Qin, J. 2010. Hyperspectral imaging instruments. In “Hyperspectral Imaging for Food Quality Analysis and Control”, ed. D. W. Sun, 129-172. Amsterdam: Elsevier Inc.
Qin, J., M. S. Kim, K. Chao, D. E. Chan, S. R. Delwiche, and B. Cho. 2017. Line-scan hyperspectral imaging techniques for food safety and quality applications. Applied Sciences 7(125): 1-22.
Resonon Inc. 2020. Pika NIR-320. USA: Resonon Inc. Available at: http://docs.resonon.com/spectronon/pika_manual/html. Accessed 4 May 2020.
Resonon Inc. 2020. SpectrononPro Manual 5.4. USA: Resonon Inc. Available at: http://docs.resonon.com/spectronon/pika_manual/html. Accessed 4 May 2020.
Samuoliene, G., A. Urbonaviciute, P. Duchovskis, Z. Bliznikas, P. Vitta, and A. Zukauskas. 2009. Decrease in nitrate concentration in leafy vegetables under a solid-state illuminator. HortScience 44(7): 1857-1860.
Santamaria, P., A. Elia, A. Parente, and F. Serio. 1998. Fertilization strategies for lowering nitrate content in leafy vegetables: chicory and rocket salad cases. Journal of Plant Nutrition 21(9): 1791-1803.
Santamaria, P., A. Elia, F. Serio, and E. Todaro. 1999. A survey of nitrate and oxalate content in fresh vegetables. Journal of the Science of Food and Agriculture 79: 1882-1888.
Santamaria, P. 2006. Nitrate in vegetables: toxicity, content, intake and EC regulation. Journal of the Science of Food and Agriculture 86: 10-17.
Slaughter, D. C., D. Barrett, and M. Boersig. 1996. Nondestructive determination of soluble solids in tomatoes using near infrared spectroscopy. Journal of Food Science 61(4): 695-697.
Yao, H., and D. Lewis. 2010. Spectral preprocessing and calibration techniques. In “Hyperspectral Imaging for Food Quality Analysis and Control”, ed. D. W. Sun, 45-78. Amsterdam: Elsevier Inc.
Yang, H., T. Inagaki, T. Ma, and S. Tsuchikawa. 2017. High-resolution and non-destructive evaluation of the spatial distribution of nitrate and its dynamics in spinach (spinacia oleracea l.) leaves by near-infrared hyperspectral imaging. Frontiers in Plant Science 8(1937): 1-9.
Zhang, X., Y. Li, W. Wei, and Y. Peng. 2019. Detection of adulteration with duck meat in minced lamb meat by using visible near-infrared hyperspectral imaging. Meat Science 149(2019): 55-62.
Zhou, W. L., W. K. Liu, and Q. C. Yang. 2012. Quality changes in hydroponic lettuce grown under pre-harvest short short-duration continuous light of different intensities. The Journal of Horticultural Science and Biotechnology 87(5): 429-434.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59961-
dc.description.abstract硝酸鹽為葉菜生長必須之氮肥的主要來源,生長過程中過量施肥使作物產量提升,也增加了植物體內的硝酸鹽濃度,人體若食用過量的硝酸鹽導致血液疾病和罹癌。目前標準的硝酸鹽檢測方法雖然精準但耗時長,故僅能以抽樣的方式來進行檢測。快速準確且非破壞性的檢測對於農作物等農產品及生物材料之檢測非常重要,而光譜檢測技術符合了這些要求。生物材料變異性大,能結合光譜與空間資訊的高光譜影像技術逐漸被廣泛應用。相較於其他拍攝方法,線掃描的方式擁有低雜訊干擾以及高影像掃描速度,因此特別適合即時產品檢測。
為了能快速且精準的拍攝農產品高光譜影像,本研究整合了線高光譜相機、滾珠螺桿滑台、步進馬達、鹵素光源、控制器等硬體元件,製作了一可感測950 ~ 1700 nm波段之線掃描式高光譜影像系統並使用LabVIEW與MATLAB進行系統軟體建置。相機幀數最高可達344 FPS,拍攝一次範圍300 mm × 260 mm之空間的高光譜影像只需花費15秒。系統相當穩定,系統光雜訊之平均雜訊偏移(Bias)小於2.36 × 10^-3 Abs、均方根值(RMS)小於8.49 × 10^-5 Abs。而系統軟體功能包含光場調整人機介面、自動化送樣速度校正、高光譜影像自動化流程拍攝、光譜資訊互動式讀取人機介面與成分可視化影像顯示等功能,可提供光譜分析研究快速且自動化光譜資料取得。
本研究選擇硝酸鹽含量較高、十字花科葉菜類的青江菜做為實驗對象,以光譜吸收度來預測葉片之硝酸鹽濃度,模型建立分別使用MLR與MPLSR來進行迴歸。最好之預測結果以8次重複取樣之光譜使用MLR方法建立之模型,校正組之rc2可達0.85、SEC為684 mg/kg,而交叉驗證組之rcv2可達0.83、SECV為713 mg/kg。此結果也證明增加重複取樣次數來降低光雜訊之影響可以提高預測結果。從青江菜硝酸鹽濃度可視化影像中可以確定葉菜各組織與位置的濃度分布並不均勻,濃度由高到低分別為葉脈 > 中心葉肉 > 外緣葉肉。此外,本研究成功使用可視化影像進行後續試驗,確認在葉菜採收後置於冷藏狀態下,仍可以透過4小時的短期光處理使硝酸鹽快速下降約20%。
本研究線掃描式高光譜影像系統與儀器級分光光度計在光譜分析青江菜硝酸鹽濃度上之預測能力相當,並且能將高光譜影像經檢量線轉換後顯示成分可視化影像。可視化影像包含空間與成分之資訊,非常適合應用於需觀察成分動態變化之研究上。未來,此系統將繼續用於建立光譜成份檢量線以及農產品成份影像可視化之研究。
zh_TW
dc.description.abstractNitrate, as the main source of nitrogen, is essential for plant growth. Excessive application of nitrogen fertilizer could increase plant yield, but also lead to elevated nitrate concentration in leafy vegetables. Consuming high doses of nitrate may be associated with blood diseases and cancer. The current standard approaches used for nitrate detection, although accurate, are destructive, time-consuming and limited to the small size of samples. The characteristics of fast, accurate and non-destructive are the important requirement for the quality assessment of agricultural products and biomaterials, and the spectral detection technology meets these requirements. Hyperspectral imaging technology, combining both the spatial and spectral information of a sample, has been widely applied in biomaterial detection. Compared to other scanning methods, the line-scan method has the advantages of low noise and high scanning speed, particularly suited for real-time detection.
To acquire the hyperspectral images of the agricultural products in a fast and accurate manner, this study developed a hyperspectral imaging system (HIS) using a hyperspectral camera with 950 - 1700 nm detection wavelength, and integrated with hardware of stepper motor, ball screw slide, halogen light source and controller. A control program was written by LabVIEW and MATLAB software. The system’s frame rate can reach up to 344 frames per second, with only 15 seconds are required to scan an area of 300 mm × 260 mm. The stability of the system is determined by measuring the photometric noise. The bias of noise is less than 2.36 × 10^-3 Abs and the root mean square of noise is less than 8.49 × 10^-5 Abs. The functionality of the developed system software including the light field adjustment, automatic correction for sample delivery speed, automatic hyperspectral image acquisition, interactive interface of spectral information and visual image display of ingredients which can provide fast and automated hyperspectral data acquisition for spectral analysis research.
In this study, bok-choy (Brassica chinensis Linn), commonly with high nitrate content, was used as the study material. The nitrate concentration in bok-choy leaves was predicted by absorption spectrum. The models, specifically, multiple linear regression (MLR) and modify partial least squares regression (MPLSR), were used to establish the prediction model. The best prediction model was given by MLR with 8 repetitions of sample scanning. The rc2 and SEC of calibration samples were 0.85 and 684 mg/kg, respectively, while the rcv2 and SECV of the cross-validation samples were 0.83 and 713 mg/kg, respectively. This result also indicated that increasing the number of sample scanning to reduce the influence of optical noise can improve the prediction result. From the visualization of nitrate concentration in bok-choy, it can be observed that the nitrate concentration was not uniformly distributed in the leaf tissues, from high to low: leaf veins > center of leaf > edge of the leaf. Also, this study showed that the nitrate concentration of post-harvested leafy vegetables can be significantly reduced by about 20% through the short-term light treatment of 4 hours under cold storage.
The developed line-scan HIS in this study is comparable to the instrument-level spectrophotometer for the spectral analysis of nitrate concentration in bok-choy. The nitrate content obtained by the hyperspectral image can be visualized as a distribution map using the calibration line. The visualized image contains information about space and content concentration, which is very suitable for research that needs to observe the dynamic changes of concentration. For the future, this system will continually research on establishing the spectral prediction models and visualization of the spectral images.
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dc.description.tableofcontents論文口試委員審定書 i
誌 謝 ii
摘 要 iii
Abstract v
圖目錄 x
表目錄 xiii
第一章 前言 1
1.1 研究動機 1
1.2 研究目的 2
第二章 文獻探討 3
2.1 光譜檢測技術 3
2.1.1 光譜檢測原理 3
2.1.2 光譜儀設備 5
2.1.3 光譜分析方法 7
2.2 高光譜影像技術 10
2.2.1 高光譜影像系統 10
2.2.2 高光譜影像系統校正 13
2.2.2.1 波長校正 13
2.2.2.2 平場校正 14
2.2.2.3 空間校正 15
2.2.3 高光譜影像檢測之應用 16
2.3 蔬菜硝酸鹽 17
2.3.1 蔬菜硝酸鹽含量影響因子 18
2.3.2 硝酸鹽對植物與人體之影響 19
2.3.3 降低蔬菜硝酸鹽之策略與相關研究 20
2.3.4 蔬菜硝酸鹽檢測方法 24
2.3.5 蔬菜硝酸鹽光譜檢測 25
第三章 材料與方法 30
3.1 高光譜影像系統之開發 30
3.1.1 線掃描式高光譜影像系統架構 30
3.1.2 高光譜相機 31
3.1.3 光源 33
3.1.4 樣本移動平台 33
3.1.5 馬達與驅動板 34
3.1.6 控制器 35
3.1.7 系統軟體 36
3.1.8 實驗室型分光光度計 37
3.2 系統校正與測試 38
3.2.1 線掃描式高光譜影像系統校正 38
3.2.2 高光譜影像之重複性 39
3.2.3 平台移動之穩定性 39
3.2.4 系統拍攝流程 40
3.3 葉菜硝酸鹽光譜預測模型建立 41
3.3.1 葉菜樣本備製 41
3.3.2 葉菜硝酸鹽含量檢測 42
3.3.3 光譜分析 44
3.3.3.1 多重線性迴歸 45
3.3.3.2 修正部分最小平方迴歸 46
3.3.3.3 預測模型評估 47
3.4 採收後葉菜硝酸鹽降低試驗 48
3.4.1 試驗設計 48
3.4.2 統計分析 50
第四章 結果與討論 53
4.1 線掃描式高光譜影像系統 53
4.1.1 硬體整合 53
4.1.2 系統功能 55
4.2 高光譜影像系統校正 59
4.2.1 高光譜相機波長校正結果 59
4.2.2 光譜影像平場校正結果 61
4.2.3 空間校正 65
4.3 青江菜硝酸鹽光譜檢測 68
4.3.1 硝酸鹽標準檢量線 68
4.3.2 青江菜硝酸鹽光譜分析 69
4.4 青江菜硝酸鹽可視化影像 76
4.4.1 青江菜硝酸鹽濃度分布 76
4.4.2 光照對採收後冷藏葉菜硝酸鹽之影響 77
第五章 結論 82
參考文獻 84
dc.language.isozh-TW
dc.title線掃描式高光譜影像系統之開發-以葉菜硝酸鹽分布分析之應用為例
zh_TW
dc.titleDevelopment of a Line-scan Hyperspectral Imaging System - An Example for Distribution Analysis of Nitrate Content in Leafy Vegetablesen
dc.typeThesis
dc.date.schoolyear108-2
dc.description.degree碩士
dc.contributor.oralexamcommittee楊宜璋(I-Chang Yang),洪滉祐(Huaang-Youh Hurng),邱奕志(Yi-Chich Chiu),謝禮丞(Li-Cheng Hsieh)
dc.subject.keyword近紅外光,高光譜影像系統,葉菜,硝酸鹽,成分可視化,zh_TW
dc.subject.keywordNear-infrared,Hyperspectral Imaging System,Leafy vegetable,Nitrate,Content visualization,en
dc.relation.page89
dc.identifier.doi10.6342/NTU202003268
dc.rights.note有償授權
dc.date.accepted2020-08-18
dc.contributor.author-college生物資源暨農學院zh_TW
dc.contributor.author-dept生物機電工程學系zh_TW
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