Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/35718
標題: 近紅外線光譜技術應用於蓮霧糖度檢測之研究
Study of Applying Near Infrared Spectroscopy for Determinating the Sugar Content of Wax Apple Fruits
作者: 鍾昭台
關鍵字: NIRS
近紅外線光譜技術
Sugar content
Wax apple
Non-destructive measurement
糖度
蓮霧
非破壞性檢測
出版社: 生物產業機電工作學系
摘要: In this study, the near infrared spectroscopy (NIRS) was used to determinate the sugar content of wax apple fruits in the wavelength range from 500nm to 1000nm. In the experiment A, the wax apples in Pingtung Fungliao area were chosen as the fruits to be detected. By using non-destructive measurement technique, the total calibration curves were established. And in the experiment B, it was to find one method of amending calibration curves, which was used for different time, region or variety, and then to modify the determinative mode quickly and conveniently. By applying multiple linear regression (MLR) analysis, the correlation of sugar content and second derivative spectra could be obtained. From the results of experiment A, it showed that the total calibration equation which consisted of 5 wavelengths (i.e. 952nm, 642nm, 884nm, 906nm and 858nm) had the calibration group with =0.931, SEC=0.388, and the first prediction set with =0.937, SEP=0.262, Bias=0.309 and RPD=4.454. For the second prediction set, the statistic data were =0.964, SEP =0.207, Bias =0.251, RPD=5.551. Finally, the total prediction set had the results as =0.918, SEP=0.322, Bias =0.287, RPD=3.649. The results of experiment B showed that the calibration equation of the first group could have good ability to predict the sugar content of samples in the second group. The statistic data were =0.974, SEC=0.229 and =0.96, SEP=1.099, Bias=1.06, RPD=1.119. However, the values of measurement had 1 average deviation different with values of content from the prediction set. After amending the deviation, the results were SEP=0.267, Bias=0.267 and RPD=4.607. Obviously, the internal qualities of fruits would be different with different cultivating environment and sections. In order to reduce predicting error, it should be collect more samples to modify the calibration curve. To expand the measuring range, in the future, it has to establish a number of databases from different sections.
本研究利用近紅外線光譜技術在500nm∼1000nm波長範圍內偵測蓮霧之糖度,針對屏東枋寮地區蓮霧作非破壞性檢測,並依照實驗A 步驟製作各批蓮霧之檢量線,以及建立總檢量線去對各批蓮霧樣本作糖度預測。同時,在實驗B中建立一套檢量線修正方法,對於預測不同時間、地區或品種蓮霧樣本時,能夠達到快速且方便之檢量線修正模式。 本研究應用多重線性迴歸(MLR)模式分析求得糖度與二次微分光譜之間的相關性。從實驗A可以得知,總校正組建立之校正方程式在選取五個波長組合(952nm、642nm、884nm、906nm、858nm)時,結果顯示Rc^2=0.931,SEC =0.388;對於預測第一批蓮霧所得結果Rp^2=0.937,SEP=0.262,Bias=0.309,RPD值為4.454;預測第二批蓮霧所得結果Rp^2=0.964,SEP=0.207,Bias=0.251,RPD值5.551;而總預測樣本組所得結果Rp^2=0.918,SEP=0.322,Bias=0.287,RPD值亦有3.649之預測水準。 由實驗B結果可知,以第一批蓮霧所建立之檢量線,預測不同母群取樣之第二批蓮霧糖度值時,所得結果顯示Rc^2=0.974,SEC=0.229; Rp^2=0.96,SEP=1.099,Bias=1.06,RPD值為1.119。可以看出,對於整體的預測趨勢是不錯的,但是所預測的測量値結果與實測糖度值均會相差一度。但是經過糖度偏差値(Bias)修正後,其SEP值降為0.267,Bias為0.267,RPD值達到4.607的預測水準。由此可知,不同栽培環境下的作物會呈現不同的內部組織特性,為了避免在往後的糖度檢測有所差異,必須先採集當地蓮霧之部份樣本,作其檢量線校正工作。依此類推,在往後的檢量線建立上,便可利用不同地區的蓮霧樣本來擴增整個量測範圍,得以建立各種不同蓮霧特性之龐大資料庫。
URI: http://hdl.handle.net/11455/35718
Appears in Collections:生物產業機電工程學系

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