Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/35708
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dc.contributor.advisor萬一怒zh_TW
dc.contributor.advisorYenu Wanen_US
dc.contributor.author顏名賢zh_TW
dc.contributor.authorYen, Minghsienen_US
dc.date2004zh_TW
dc.date.accessioned2014-06-06T07:52:45Z-
dc.date.available2014-06-06T07:52:45Z-
dc.identifier.urihttp://hdl.handle.net/11455/35708-
dc.description.abstractThis study develops a nondestructive inspection method and quality index construction model to evaluate the quality of agricultural products using digital signal processing and statistical discriminate analysis. A pendulum device is designed to impact and measure the response signal of products. The impact parameters which correspond to the change in the quality of agricultural products are obtained from the amplitude spectrum, real-part spectrum, imaginary-part spectrum and the slope, curvature and micro-fluctuation signal of the impact force-time curve. The analysis of variance (95% confidence interval) is used to determine the effective frequency and amplitude of the spectra of amplitude, real-part and imaginary-part as the inspection parameters for the quality of agricultural products. Analyses indicate that a three-order lowpass digital filter can smoothen the raw impact force-time curves to calculate their exact slope and curvature using finite difference. The maximum and minimum slopes, maximum and minimum curvatures and time of inflection point are valid impact parameters from the curve. Additionally, the power spectral density of the micro-fluctuation signal obviously reflects the variation in the texture of soft products using the Wiener-Khintchine theorem. The accuracies are lower than 70% using an impact parameter to classify the quality of guavas, mangos and tomatoes, as well as to egg variety. However, the classification accuracies can be improved by more than 10% when using high accuracy indices with parameters selected from the time and frequency domains, as well as their combinations, which are established using statistical discriminate analysis. Test results demonstrate that the accuracy reaches 82.7%, 81.0%, 85.7%, 80.0% and 82.5% in quality classification of guava maturity, mango maturity, mango acidity and tomato maturity and recognition classification of egg variety, respectively.en_US
dc.description.abstract本研究使用數位信號處理技術與統計鑑別分析,以發展農產品品質非破壞撞擊檢測方法及品質檢測指標之建立模式。設計ㄧ單擺撞擊裝置,撞擊與量測農產品之反應信號,藉由檢測信號之振幅頻譜、實部頻譜、虛部頻譜、撞擊力-時間曲線之斜率與曲率及曲線上之微小震盪信號,推導出與農產品品質變化有關的撞擊檢測參數。經變異數分析檢定(信賴區間95%),獲得振幅頻譜之有效頻率,及實部頻譜與虛部頻譜中振幅等於零之頻率與振幅最大值、最小值及其相對應之頻率等與品質變化之關係。同時原始撞擊信號經3階數位低通濾波器平滑化處理與有限差分計算,得到撞擊力曲線之最大斜率、最小斜率、最大曲率、最小曲率、第一轉折點時間與第二轉折點時間等之品質量測參數。進ㄧ步研究發現質地柔軟之農產品可應用Wiener-Khintchine定理,計算撞擊力曲線上之微小震盪信號的功率頻譜密度(PSD),探討其品質之變化。許多參考研究使用單一撞擊檢測參數分類番石榴、芒果、牛心番茄的品質等級及雞蛋種類之辨識,其準確率不超過70%。然而結合時域與頻域等各類型撞擊檢測參數,經統計之鑑別分析做部分參數的最佳化組合,形成農產品品質檢測指標,可獲得較高準確度的檢測結果。研究顯示,對於番石榴成熟度、芒果成熟度、芒果酸度、牛心番茄成熟度的品質分類及雞蛋種類的辨識,分別可達到82.7%、81.0%、85.7%、80.0%、82.5%的準確率。zh_TW
dc.description.tableofcontents摘要……………………………………………………………………… I Abstract…..……………………………………………………………… II 目錄…………………………………………………………………….. III 表目錄…………………………………………………………………... V 圖目錄………………………………………………………………… VII 參數符號表……………………………………………………………... X 第一章 緒論…………………………………………………………….. 1 1-1前言…………………………………………………………… 1 1-2研究目的……………………………………………………… 5 第二章 文獻探討……………………………………………………….. 6 2-1農產品堅實度與質地性狀量測……………………………… 6 2-2應用撞擊力技術量測農產品堅實度與質地性狀…………… 8 第三章 實驗材料與設備……………………………………………… 12 3-1測試材料……………………………………………………. 12 3-2儀器設備……………………………………………………. 14 第四章 研究方法……………………………………………………… 18 4-1數位信號處理方法…………………………………………. 18 4-1-1時域參數………………………………………………. 18 4-1-2頻譜分析………………………………………………. 19 4-1-3撞擊力-時間曲線之斜率與曲率曲線參數…………... 21 4-1-4撞擊曲線之微小震盪信號分析………………………. 34 4-1-5水果黏彈撞擊模型之模擬……………………………. 39 4-2統計分析……………………………………………………. 42 4-2-1變異數分析……………………………………………. 42 4-2-2鑑別分析………………………………………………. 45 4-2-3集群分析………………………………………………. 47 第五章 結果與討論…………………………………………………… 49 5-1番石榴撞擊檢測參數………………………………………. 49 5-1-1番石榴撞擊檢測之時域參數………………………… 49 5-1-2番石榴撞擊力與時間曲線之斜率與曲率…………… 50 5-1-3番石榴撞擊檢測之頻譜分析參數…………………… 58 5-1-4番石榴撞擊檢測曲線之微小震盪信號分析………… 63 5-2番石榴成熟度品質指標之建立模式分析…………………. 70 5-3相關農產品品質撞擊檢測之應用…………………………. 80 5-3-1芒果品質檢測之應用……………………………….. 80 5-3-2牛心番茄成熟度檢測之應用……………………….. 92 5-3-3台灣種雞蛋與來亨雞白蛋辨識之應用…………….. 97 5-4水果黏彈撞擊模型之建立………………………………… 103 5-5 討論……………………………………………………….. 108 第六章 結論………………………………………………………….. 111 第七章 建議………………………………………………………….. 114 參考文獻……………………………………………………………… 115 附錄…………………………………………………………………… 121 A1. 撞擊力-時間曲線之濾波處理與斜率、曲率之計算程式... 121 A2. 撞擊力-時間曲線上之微小震盪信號處理與檢測參數之計算程式…………………………………………………… 123 A3. 鑑別分析之SAS統計程式………………………………... 126 A4. 番石榴之撞擊檢測參數值………………………………… 129 A5. 愛文芒果之撞擊檢測參數值……………………………… 149 A6. 牛心番茄之撞擊檢測參數值……………………………… 164 A7. 水煮雞蛋之撞擊檢測參數值……………………………… 177zh_TW
dc.language.isoen_USzh_TW
dc.publisher生物產業機電工程學系zh_TW
dc.subjectNondestructive inspectionen_US
dc.subject非破壞檢測zh_TW
dc.subjectImpact forceen_US
dc.subjectAgricultural producten_US
dc.subjectQuality indexen_US
dc.subjectSpectrum analysisen_US
dc.subjectDiscriminate analysisen_US
dc.subject撞擊力zh_TW
dc.subject農產品zh_TW
dc.subject品質指標zh_TW
dc.subject頻譜分析zh_TW
dc.subject鑑別分析zh_TW
dc.title農產品品質撞擊檢測指標建立模式之研究zh_TW
dc.titleThe Study of Agricultural Product Quality Index Construction Model Using Impact Forceen_US
dc.typeThesis and Dissertationzh_TW
item.grantfulltextnone-
item.openairetypeThesis and Dissertation-
item.languageiso639-1en_US-
item.fulltextno fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:生物產業機電工程學系
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