Correction: Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).
出版信息
PLoS One. 2015 Jul 28;10(7):e0134313. doi: 10.1371/journal.pone.0134313. eCollection 2015.
Abstract
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Correction: Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).更正：通过与自动交互检测相关联的通用线性模型（GLM-AID）和人工神经网络模型（ANN）鉴定用于番茄表征和认证的相关植物化学成分。
PLoS One. 2015 Jul 28;10(7):e0134313. doi: 10.1371/journal.pone.0134313. eCollection 2015.
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Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).通过与自动交互检测（GLM-AID）和人工神经网络模型（ANNs）相关联的一般线性模型来鉴定番茄特征和真伪的相关植物化学成分。
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本文引用的文献
1
Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).通过与自动交互检测（GLM-AID）和人工神经网络模型（ANNs）相关联的一般线性模型来鉴定番茄特征和真伪的相关植物化学成分。
PLoS One. 2015 Jun 15;10(6):e0128566. doi: 10.1371/journal.pone.0128566. eCollection 2015.
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