盧福明臺灣大學：生物產業機電工程學研究所林連雄Lin, Lian-HsiungLian-HsiungLin2007-11-262018-07-102007-11-262018-07-102007http://ntur.lib.ntu.edu.tw//handle/246246/52892於加工線上進行白米內部品質之即時檢測，可精確篩選稻米，並針對各級產品進行包裝及加工貯藏處理，為重要之產業技術。本研究目的為發展近紅外線影像系統，於稻米加工過程中，以非破壞性方式進行即時之團粒米成分分析。並進一步研製發光二極體（LED）檢測系統，針對單粒白米進行含水率量測，以探究進行單粒白米精確分級之可能性。 近紅外線影像系統之構造包括影像攝影裝置、濾鏡自動更換裝置及程式控制介面。影像攝影裝置使用近紅外線CCD攝影機，連接攝影機控制器。攝影機控制器可透過串列介面連接電腦，由電腦控制攝影機控制器之增益等參數，以控制拍攝影像之品質。將影像訊號藉由影像擷取卡數位化成640×480像素之影像送至個人電腦。由四盞鹵素燈組成光源，以具電壓控制器之直流光源穩定光源亮度。為擷取分光後之光譜影像及提高作業效率，設計可自動更換濾鏡之裝置，其構造包括濾鏡、濾鏡盤、步進馬達、步進馬達控制器及傳動機構。該裝置使用帶通濾鏡加裝於鏡頭下方，15組濾鏡之中心波長範圍為870 nm至1,014 nm。以多重線性迴歸（MLR）、部分最小平方迴歸（PLSR）及類神經網路（ANN）探討近紅外線分光光度計及近紅外線影像系統對白米含水率及蛋白質之檢測結果。為減少重複與多餘輸入值，以獲得較正確之神經網路，選用於MLR分析模式中，對白米水分及蛋白質含量具高度相關之波長，作為類神經網路之輸入值。 以近紅外線影像系統偵測白米含水率，檢測效果與近紅外線分光光度計相近。綜合比較光譜影像之驗證組採用三個模式所得之rval2、SEP、及RPD值，其分別介於0.942-0.952、0.435-0.479%及4.2-4.6。進一步探討近紅外線影像系統量測白米蛋白質之效能，結果rval2及SEP分別為0.769-0.806及0.266-0.294%。試驗結果顯示，近紅外線影像系統使用MLR、PLSR及ANN校正模式，對檢測白米水分及蛋白質含量具高預測能力，且其預測能力與紅外線分光光度計之檢測結果相近，可應用於白米含水率及蛋白質之非破壞性線上即時檢測作業。 為提昇白米分級之精確性，本研究並發展近紅外線LED檢測系統，以進行即時之線上單粒白米之含水率量測。系統之主要構造包括進料裝置、檢測裝置及訊號處理介面。檢測裝置以近紅外線LED為光源，矽偵測器為感測元件，用以量測單粒白米通過檢測裝置時之近紅外線穿透光譜。試驗用白米樣本品種為梗稻，校正用之78顆單粒白米樣本含水率範圍為10.34-22.37%，驗證用之60顆單粒白米樣本的含水率分佈範圍於10.50- 21.65%之間。本研究選用940、1,050及850 nm LED波長組合，所獲含水率校正模式之判定係數為0.706，預測模式之判定係數為0.624。此結果顯示，應用以近紅外線LED檢測系統進行線上即時量測白米含水率之作業具可行性。One of the objectives of this research was to develop a near-infrared (NIR) imaging system that would detect rice intrinsic qualities nondestructively in real time rice processing lines. The sorting of a single rice kernel based on intrinsic qualities will precisely influence the classification and packaging process of rice. Therefore, the other objective of the research was to develop a rice moisture detecting system for single rice kernel. The developed NIR imaging system consists mainly of a NIR CCD camera which is coupled to a camera controller. A frame grabber board was used to receive the video signal from the camera. A filter exchange device consisted of a filter adapter, a filter holder, and a stepper motor module that was combined with the CCD camera. The filters were installed in a filter holder. The filter exchange device was controlled by a stepper motor in order to rotate automatically such that the NIR imaging system can effectively acquire multi-spectral images. In this work, calibration methods including multiple linear regression (MLR), partial least squares regression (PLSR), and artificial neural network (ANN) were used in both of near-infrared spectrometer (NIRS) and NIR imaging system to determine the moisture and protein contents of rice. Comprehensive performance comparisons among MLR, PLSR, and ANN approaches were conducted. To reduce repetition and redundancy in the input data and obtain a more accurate network, six and five significant wavelengths selected by the MLR model, which had high correlation with the moisture and protein contents of rice, were used as the input data of the ANN. The performance of the developed system was evaluated via a series of experimental tests for rice moisture and protein contents. Utilizing three models of MLR, PLSR, and ANN, the rice moisture analysis results of rval2, SEP, and RPD for the validation set were within 0.942-0.952, 0.435-0.479%, and 4.2-4.6, respectively. The prediction of protein content with the NIR imaging system by employing the same three models achieved rval2 of 0.769-0.806, and SEP of 0.266-0.294%, respectively. While compared with a commercial NIRS, experimental results showed that the performance of the NIR imaging system was almost the same as that of NIRS. Using the developed NIR imaging system, all of the three different calibration methods (MLR, PLSR, and ANN) provided satisfactory prediction of rice moisture and protein content. These results indicated that the NIR imaging system developed in this research can be used as a device for the measurement of rice moisture and protein content. A NIR light-emitting diode (LED) individual rice kernel moisture content measurement system contains NIR LED, rice moving chute, detecting units, and signal processing unit was also developed in this research. A calibration set which contained 78 rice kernels with moisture content ranged from 10.34-22.37% was used to calibrate the system and to develop a prediction equation. Another set of rice which containing 60 kernels with moisture content range of 10.50-21.65% was used for validation. The coefficients of determination for the calibration and validation sets based on 940, 1,050 and 850 nm LED were 0.706 and 0.624, respectively. The results indicated that the developed NIR LED measurement system can be utilized on the rice processing line.誌謝……………………………………………………………… i 摘要……………………………………………………………… ii Abstract………………………………………………………… iv 目錄……………………………………………………………… vi 圖目錄…………………………………………………………… x 表目錄…………………………………………………………… xiii 符號說明…………………………………………………………… xv 第壹篇 緒論及文獻探討………………………………… 1 第一章 緒論……………………………………………… 1 第二章 文獻探討…………………………………………… 3 2.1 稻米的產量及成分……………………………… 3 2.1.1稻米的產地分佈與產量…………………… 3 2.1.2稻米結構與化學成分.......................4 2.1.3影響米飯食味的主要成分……………………. 6 2.2 穀物成分檢測…………………………………….. 7 2.2.1化學分析法檢測穀物成分…………………. 7 2.2.2近紅外線分光光度計分析穀物成分……. 10 2.2.3以機械視覺分析農產品品質之應用…………. 12 2.2.4近紅外線影像技術於農產品品質分析之研究… 13 2.3 近紅外線原理及分析方法………………………….. 14 2.3.1光譜分析的基本原理………………………….. 14 2.3.2近紅外線光譜之定量分析……………………. 17 2.3.3校正方程式之建立…………………………... 20 2.3.4校正方程式之性能評估的相關統計定義……. 35 第貳篇 發展近紅外線影像系統量測白米品質……………... 37 第三章 近紅外線影像系統之研製……………………….. 37 3.1前言………………….........................37 3.2材料與方法……………………….......….. 38 3.2.1近紅外線影像系統…………………………... 38 3.2.2影像校準和近紅外線影像資料分析………….. 46 3.2.3近紅外線分光光度計光譜資料之取得……….. 49 3.2.4模式建立－多重線性迴歸（MLR）…………… 49 3.3結果與討論……………………………………….. 50 3.3.1近紅外線光譜分析－波長選擇………………… 51 3.3.2近紅外線影像分析結果……………………….. 56 3.3.3 影像系統白米含水率自動偵測介面………… 58 3.4結論………………………………………………… 59 第四章 不同校正模式對近紅外線影像系統檢測白米含水率性能之 影響..........................................61 4.1前言………………………………………….…… 61 4.2材料與方法………………………………….. 64 4.2.1白米樣本………………………....... 64 4.2.2水分含量分析……………………………… 64 4.2.3近紅外線分光光度計…………………….. 64 4.2.4近紅外線影像系統之設計…………………. 65 4.2.5影像擷取及處理…………………………… 69 4.2.6模式建立……………………………….. 70 4.3 結果與討論……………………………... 72 4.3.1多重線性迴歸（MLR）分析白米含水率……… 74 4.3.2部份最小平方迴歸（PLSR）分析白米含水率… 77 4.3.3類神經網路（ANN）分析白米含水率………… 79 4.4結論.......................................85 第五章 近紅外線影像系統檢測白米蛋白質含量之研究…… 86 5.1前言……………………………….....…… 86 5.2材料與方法…………...........…….. 88 5.2.1白米樣本……....……………………. 88 5.2.2蛋白質分析……….…………………. 88 5.2.3近紅外線分光光度計…….…………….. 88 5.2.4近紅外線影像系統之設計……………………. 88 5.2.5影像資料處理與模式建立……………………. 91 5.3 結果與討論…………………....…….. 94 5.3.1多重線性迴歸（MLR）分析白米蛋白質……… 97 5.3.2部份最小平方迴歸（PLSR）分析白米蛋白質… 100 5.3.3類神經網路（ANN）分析白米蛋白質……….. 103 5.4 結論……………………................ 109 第參篇 線上白米內部品質自動偵測系統之研製……...... 110 第六章 近紅外線發光二極體（LED）檢測系統量測單粒白米含水 率之研究......................................110 6.1前言……...........……............ 110 6.2材料與方法……………………………….. 112 6.2.1單粒白米內部品質檢測系統…………………… 112 6.2.2訊號處理………………………………………… 118 6.2.3單粒米樣本挑選及含水率量測………………… 122 6.3結果與討論……………………………………….. 122 6.3.1近紅外線光譜分析－LED波長選擇…………... 124 6.3.2近紅外線LED偵測白米含水率之分析結果..... 125 6.3.3近紅外線 LED偵測白米含水率訊號處理介面.. 127 6.4結論…………………………………………… 129 第肆篇 結論.................................... 130 第七章 結論與建議………………………………………….. 130 7.1結論………………………………………… 130 7.2建議………………………………………………… 131 參考文獻…………………………………………………………. 1322058092 bytesapplication/pdfen-US近紅外線影像含水率蛋白質發光二極體稻米Near-infraredImagingMoisture contentProtein contentLight- emitting diodeRicethesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/52892/1/ntu-96-D90631003-1.pdf