2,4-dimethyl-3,3-dioxo-3λ⁶-thia-glutaric acid | 16680-82-3

• 分子结构分类: 有机化合物 - 有机酸及其衍生物 - 羧酸及其衍生物
• 英文名称: 2,4-dimethyl-3,3-dioxo-3λ⁶-thia-glutaric acid
• 英文别名: 2,4-Dimethyl-3,3-dioxo-3λ⁶-thia-glutarsaeure；2-(1-Carboxyethylsulfonyl)propanoic acid
• CAS: 16680-82-3
• 化学式: C₆H₁₀O₆S
• 分子量: 210.208
• InChiKey: BEWZANMONGBSQQ-UHFFFAOYSA-N

物化性质

• 沸点：
  525.6±35.0 °C(Predicted)
• 密度：
  1.516±0.06 g/cm3(Predicted)

计算性质

• 辛醇/水分配系数(LogP)：
  -0.4
• 重原子数：
  13
• 可旋转键数：
  4
• 环数：
  0.0
• sp3杂化的碳原子比例：
  0.67
• 拓扑面积：
  117
• 氢给体数：
  2
• 氢受体数：
  6

反应信息

• 作为反应物：
  描述：

  2,4-dimethyl-3,3-dioxo-3λ⁶-thia-glutaric acid 生成 二乙基砜
  参考文献：
  名称：

  Modeling of Fried Potato Chips Color Classification using Image Analysis and Artificial Neural Network

  摘要：

  ABSTRACT: Quality of potatoes in chips industry is estimated from the intensity of darkening during frying. This is measured by a human jury, subject to numerous factors of variation. Gray level intensities were obtained for the apex, the center, and the basal parts of each chip using image analysis of frying assays. We then tested a feed forward artificial neural network designed to associate these data with color categories. It behaved in good agreement with human estimations, obtaining correlation coefficients of 0.972 for training data and of 0.899 for validation data. A systematic study of the response of the model allowed understanding the criteria of evaluation used by the human operators.

  DOI：

  10.1111/j.1365-2621.2003.tb05757.x
• 作为产物：
  描述：

  alkaline earth salt of/the/ methylsulfuric acid 在 sodium ethanolate 、 碘甲烷 作用下， 生成 2,4-dimethyl-3,3-dioxo-3λ⁶-thia-glutaric acid
  参考文献：
  名称：

  Modeling of Fried Potato Chips Color Classification using Image Analysis and Artificial Neural Network

  摘要：

  ABSTRACT: Quality of potatoes in chips industry is estimated from the intensity of darkening during frying. This is measured by a human jury, subject to numerous factors of variation. Gray level intensities were obtained for the apex, the center, and the basal parts of each chip using image analysis of frying assays. We then tested a feed forward artificial neural network designed to associate these data with color categories. It behaved in good agreement with human estimations, obtaining correlation coefficients of 0.972 for training data and of 0.899 for validation data. A systematic study of the response of the model allowed understanding the criteria of evaluation used by the human operators.

  DOI：

  10.1111/j.1365-2621.2003.tb05757.x

文献信息

• Poly(meth)acrylate membranes for separation of hydrocarbon mixtures
  申请人：Yeager William Gary

  公开号：US20060289352A1

  公开（公告）日：2006-12-28

  The separation of hydrocarbon mixtures comprising a polymerizable vinyl containing polyester of the formula I: Each R and R 2 is independently a C 6 -C 20 aromatic residue or a C 1 -C 20 aliphatic residue. Each R 1 is independently hydrogen or C 1 -C 5 alkyl; “j” is an integer in the range of 1-1000. Z is a heteroatom or two hydrogen atoms. The invention is also directed to the method of preparing the separation membrane.

  本发明涉及一种聚合物化的含有公式I的聚酯的烃混合物分离方法：其中每个R和R2独立地是C6-C20芳香基残基或C1-C20脂肪基残基。每个R1独立地是氢或C1-C5烷基;“j”是1-1000范围内的整数。Z是一个杂原子或两个氢原子。本发明还涉及制备分离膜的方法。
• Backer; Meijer, Recueil des Travaux Chimiques des Pays-Bas, 1927, vol. 46, p. 213
  作者：Backer、Meijer

  DOI：——

  日期：——
• Loven; Ahlberg, Chemische Berichte, 1921, vol. 54, p. 228
  作者：Loven、Ahlberg

  DOI：——

  日期：——
• US7638053B2
  申请人：——

  公开号：US7638053B2

  公开（公告）日：2009-12-29
• Modeling of Fried Potato Chips Color Classification using Image Analysis and Artificial Neural Network
  作者：T. Marique、A. Kharoubi、P. Bauffe、C. Ducattillon

  DOI：10.1111/j.1365-2621.2003.tb05757.x

  日期：2003.9

  ABSTRACT: Quality of potatoes in chips industry is estimated from the intensity of darkening during frying. This is measured by a human jury, subject to numerous factors of variation. Gray level intensities were obtained for the apex, the center, and the basal parts of each chip using image analysis of frying assays. We then tested a feed forward artificial neural network designed to associate these data with color categories. It behaved in good agreement with human estimations, obtaining correlation coefficients of 0.972 for training data and of 0.899 for validation data. A systematic study of the response of the model allowed understanding the criteria of evaluation used by the human operators.