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  1. 0 資料タイプ別
  2. 02 学位論文
  1. 230 大学院自然科学研究科
  2. 60 博士学位論文
  3. 10 博士学位論文

可視・近赤外分光法による農産物内部障害の非破壊評価に関する研究

http://hdl.handle.net/10191/24086
http://hdl.handle.net/10191/24086
c584ea1c-4819-4f86-810f-95672639c042
名前 / ファイル ライセンス アクション
D_S_N_K131.pdf 本文 (1.6 MB)
Item type 学位論文 / Thesis or Dissertation(1)
公開日 2013-11-20
タイトル
タイトル 可視・近赤外分光法による農産物内部障害の非破壊評価に関する研究
タイトル
言語 en
タイトル 可視・近赤外分光法による農産物内部障害の非破壊評価に関する研究
言語
言語 jpn
資源タイプ
資源 http://purl.org/coar/resource_type/c_46ec
タイプ thesis
著者 滝沢, 憲一

× 滝沢, 憲一

WEKO 50124

滝沢, 憲一

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抄録
内容記述タイプ Abstract
内容記述 1.Development of nondestructive technique for detecting internal defects in Japanese radish Internal defects always can be found in many produces such as Japanese radish. It is impossible to be detected by human eye. Nondestructive measurement is suitable technique for detecting internal defects like black heart, and air cavity after harvest time, which makes the radish root unmarketable in Japan. This study developed the nondestructive detection algorithm for internal defects of Japanese radish by Vis/NIR spectroscopy. Using the first derivative, selected wavelengths were calculated by stepwise forward selection method. The selected wavelengths were used as classifying parameters in multiple discriminant analysis and neural network. Multiple discriminant analysis and neural network were used to build the detection algorithm based on leave-one-out cross validation. Using the multiple discriminant analysis for the prediction set (removed samples), 128 of the 130 normal radishes were correctly discriminated, giving a discriminant rate of 98.5%. The internal defect radishes were correctly discriminated for 45 of 62 samples, giving a discriminant rate of 72.6%, the overall discriminant rate was 90.1%. When the error goal was 0.05 and the number of hidden neurons was 13, the discriminant rate of the normal radish, the internal defects radish and the total sample were 97.0%, 82.9% and 92.4% respectively. These results show the potential of the proposed techniques for detecting and predicting radish with internal quality. 2.Determination of Astringent Fruit in 'Le Lectier' Pears Using Visible and Near-infrared Spectroscopy and Neural Network It is impossible to distinguish the astringent fruit in 'Le Lectier' pears by visual inspection. This study aimed to develop nondestructive determination of the astringent fruit and quality assurance of the intact fruit using neural network classification, visible and near-infrared spectroscopy. For this study, 51 pears harvested in Sanjo City and 46 pears harvested in the Tsukigata area of Niigata City, 97 pears in all were collected. The recognition ratio was established by neural network learning and validation repeatedly using leave-one-out cross validation. The average recognition ratio was 81.1% when the neural network was discussed using 15 hidden layer units and set an error goal to 0.11 and calculated by 10 times cross validation.
内容記述
内容記述タイプ Other
内容記述 学位の種類: 博士(農学). 報告番号: 甲第3800号. 学位記番号: 新大院博(農)甲第131号. 学位授与年月日: 平成25年3月25日
書誌情報 p. 1-88, 発行日 2013-03-25
出版者
出版者 新潟大学
著者版フラグ
値 ETD
学位名
学位名 博士(農学)
学位授与機関
学位授与機関名 新潟大学
学位授与年月日
学位授与年月日 2013-03-25
学位授与番号
学位授与番号 13101A3800
学位記番号
内容記述タイプ Other
内容記述 新大院博(農)甲第131号
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