14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance
-
Aarya Vilas Karanjawane
Abstract
In the field of homeopathy, maintaining the quality and consistency of medicinal plants is fundamental to ensuring effective treatment outcomes. However, the inherent variability among these plants poses a significant challenge to quality assurance efforts. Chemometric standardization offers a promising solution, using statistical and mathematical methods to enhance data accuracy and refine analytical processes. This comprehensive approach includes preprocessing techniques, calibration methods, and validation procedures, all directed towards improving product quality and safety. By adopting chemometric standardization, homeopathic practitioners can effectively address issues such as ensuring batch-to-batch consistency, authenticating botanicals, and detecting potential adulterants. Consequently, this strategy strengthens the reliability of medicinal products and gives patients greater confidence regarding the efficacy of homeopathic treatments. Moreover, the use of chemometric standardization facilitates compliance with regulatory requirements, further enhancing patient safety and trust. The establishment of standardized protocols and guidelines requires collaborative effort between academia, industry, and regulatory bodies. By working together, stakeholders can ensure consistent implementation of chemometric standardization practices, ultimately enhancing the credibility and efficacy of homeopathic medicinal products. In essence, the adoption of chemometric standardization represents a pivotal step toward improving product quality, regulatory compliance, and overall patient well-being within the field of homeopathy.
Abstract
In the field of homeopathy, maintaining the quality and consistency of medicinal plants is fundamental to ensuring effective treatment outcomes. However, the inherent variability among these plants poses a significant challenge to quality assurance efforts. Chemometric standardization offers a promising solution, using statistical and mathematical methods to enhance data accuracy and refine analytical processes. This comprehensive approach includes preprocessing techniques, calibration methods, and validation procedures, all directed towards improving product quality and safety. By adopting chemometric standardization, homeopathic practitioners can effectively address issues such as ensuring batch-to-batch consistency, authenticating botanicals, and detecting potential adulterants. Consequently, this strategy strengthens the reliability of medicinal products and gives patients greater confidence regarding the efficacy of homeopathic treatments. Moreover, the use of chemometric standardization facilitates compliance with regulatory requirements, further enhancing patient safety and trust. The establishment of standardized protocols and guidelines requires collaborative effort between academia, industry, and regulatory bodies. By working together, stakeholders can ensure consistent implementation of chemometric standardization practices, ultimately enhancing the credibility and efficacy of homeopathic medicinal products. In essence, the adoption of chemometric standardization represents a pivotal step toward improving product quality, regulatory compliance, and overall patient well-being within the field of homeopathy.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
