ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

綠茶游離氨基酸總量近紅外光譜定量分析模型優(yōu)化
CSTR:
作者:
作者單位:

作者簡介:

通訊作者:

中圖分類號:

基金項目:


Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 圖/表
  • |
  • 訪問統(tǒng)計
  • |
  • 參考文獻
  • |
  • 相似文獻
  • |
  • 引證文獻
  • |
  • 資源附件
  • |
  • 文章評論
    摘要:

    采集158份綠茶樣品并對其進行光譜掃描,,分別采用主成分回歸,、偏最小二乘、改進偏最小二乘3種定量校正方法,,對原始光譜分別進行4種不同的預處理,,運用正交試驗設計,建立了綠茶中游離氨基酸總量優(yōu)化定標模型,,利用目標函數(shù)法對模型進行評定,,并對模型的適用性進行了驗證。試驗結果為:SNV+Detrend,1,1,4,4的光譜預處理下,,采用改進偏最小二乘法進行定標建立的模型最優(yōu),,其目標函數(shù)值f為94.25%,驗證相對標準差eRSD為6.39%(<10%),。結果表明:采用正交試驗設計能夠綜合考查不同的校正方法和光譜預處理對模型的影響,,利用近紅外光譜分析法能夠實現(xiàn)綠茶中游離氨基酸總量的定量,、快速檢測。

    Abstract:

    158 samples of comminuted green tea were scanned by the near infrared spectroscopy analyzers. Using orthogonal experimental design, the calibration models of free amino acids content in green tea were gained with the correction methods of PCR, PLS and MPLS under different spectral data preprocessing methods separately. The models were evaluated by the objective function method. The optimal correction methods and spectral data preprocessing methods were determined, and the applicability of the models was validated. The experimental result is that using MPLS could get the optimal model. And its spectral data preprocessing method is SNV+Detrend, 1, 1, 4, 4. Its objective function value is 94.25%; the relative standard deviation of validation is 6.39% (less than 10%). The results showed that using orthogonal experiment design could comprehensively evaluate the different correction methods and spectral data preprocessing methods on the models, and NIRS could achieve quantitative and fast detection of free amino acids content in green tea. 

    參考文獻
    相似文獻
    引證文獻
引用本文

林新,牛智有,馬愛麗,劉輝.綠茶游離氨基酸總量近紅外光譜定量分析模型優(yōu)化[J].農(nóng)業(yè)機械學報,2008,39(10):144-147.[J]. Transactions of the Chinese Society for Agricultural Machinery,2008,39(10):144-147.

復制
分享
文章指標
  • 點擊次數(shù):
  • 下載次數(shù):
  • HTML閱讀次數(shù):
  • 引用次數(shù):
歷史
  • 收稿日期:
  • 最后修改日期:
  • 錄用日期:
  • 在線發(fā)布日期:
  • 出版日期:
文章二維碼