A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models
المؤلفون المشاركون
Feng, Quanxi
Xu, Lili
Chen, Huazhou
Xie, Hai
Cai, Ken
Lin, Bin
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-10
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The global fishmeal production is used for animal feed, and protein is the main component that provides nutrition to animals.
In order to monitor and control the nutrition supply to animal husbandry, near-infrared (NIR) technology was utilized for rapid detection of protein contents in fishmeal samples.
The aim of the NIR quantitative calibration is to enhance the model prediction ability, where the study of chemometric algorithms is inevitably on demand.
In this work, a novel optimization framework of GSMW-LPC-GA was constructed for NIR calibration.
In the framework, some informative NIR wavebands were selected by grid search moving window (GSMW) strategy, and then the variables/wavelengths in the waveband were transformed to latent principal components (LPCs) as the inputs for genetic algorithm (GA) optimization.
GA operates in iterations as implementation for the secondary optimization of NIR wavebands.
In steps of the variable’s population evolution, the parametric scaling mode was investigated for the optimal determination of the crossover probability and the mutation operator.
With the GSMW-LPC-GA framework, the NIR prediction effect on fishmeal protein was experimentally better than the effect by simply adopting the moving window calibration model.
The results demonstrate that the proposed framework is suitable for NIR quantitative determination of fishmeal protein.
GA was eventually regarded as an implementable method providing an efficient strategy for improving the performance of NIR calibration models.
The framework is expected to provide an efficient strategy for analyzing some unknown changes and influence of various fertilizers.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Feng, Quanxi& Chen, Huazhou& Xie, Hai& Cai, Ken& Lin, Bin& Xu, Lili. 2020. A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138818
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Feng, Quanxi…[et al.]. A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1138818
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Feng, Quanxi& Chen, Huazhou& Xie, Hai& Cai, Ken& Lin, Bin& Xu, Lili. A Novel Genetic Algorithm-Based Optimization Framework for the Improvement of Near-Infrared Quantitative Calibration Models. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138818
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138818
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر