This chapter introduces the Artificial Neural Networks (ANNs) in chemometrics and also pattern recognition which is highlighted as a vital tool in chemometrics, particularly in analyzing the complex chemical datasets involved into nonlinear relationships. As described in this chapter, the authors choose to use ANNs because ANNs are applicable to manage high-dimensional, noisy and can use nonlinear data more efficiently than traditional methods. The implementation also explains by using gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC–MS) for classification. Artificial Neural Networks (ANNs) are a very helpful tool in chemometrics for analyzing complex chemical data, addressing problems with regression, classification, and pattern recognition (PR). Their ability to handle large datasets of oil spills and nonlinear interactions makes them particularly well-suited for chemometric applications. As is common in spectroscopy, chromatography, and other analytical techniques, ANNs play a key role in pattern recognition and data modeling in chemometrics. They are adapting at identifying underlying patterns in multi-dimensional and noisy chemical data. ANNs have become an important tool in the field of oil spill fingerprinting because of their ability to handle complex, nonlinear data and extract relevant patterns. This ability is essential in environmental forensics, where identifying the origin of oil spills is critical to reducing environmental damage and identifying the culprit. The Artificial Neural Networks (ANNs) are machine learning techniques and cognitive science that solve time-series forecasting issues, recognize patterns, and produce state-of-the-art outcomes for a wide range of tasks that are difficult to complete with rule-based programming, such as the voice recognition and computer vision. The ANNs are commonly defined as a biologically adaptive technique using the three sorts of parameters listed below: 1. The pattern of connections among the distinct layers of neurons. 2. The interconnectivity weight's learning procedure for updating. 3. The activation function, which is the method by which the weighted input of a neuron is transformed into the activation of its output. Complex hydrocarbon mixtures that differ based on the source (crude oil, refined products, etc.) are found in oil spills. To differentiate between various oil sources, ANNs can evaluate chemical composition data, such as gas chromatography-mass spectrometry (GC–MS) results. The ANN is trained using features including weathering effects, hydrocarbon ratios, and biomarker profiles to categorize samples according to their source. Anyhow, in this book oil spill datasets from GC-FID and GC–MS are used to run the ANNs.

Solving Pattern Recognition Challenges Using Artificial Neural Networks (ANNs)

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发表日期：

2026年

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Azimah Ismail,
Hafizan Juahir

摘要

This chapter introduces the Artificial Neural Networks (ANNs) in chemometrics and also pattern recognition which is highlighted as a vital tool in chemometrics, particularly in analyzing the complex chemical datasets involved into nonlinear relationships. As described in this chapter, the authors choose to use ANNs because ANNs are applicable to manage high-dimensional, noisy and can use nonlinear data more efficiently than traditional methods. The implementation also explains by using gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC–MS) for classification. Artificial Neural Networks (ANNs) are a very helpful tool in chemometrics for analyzing complex chemical data, addressing problems with regression, classification, and pattern recognition (PR). Their ability to handle large datasets of oil spills and nonlinear interactions makes them particularly well-suited for chemometric applications. As is common in spectroscopy, chromatography, and other analytical techniques, ANNs play a key role in pattern recognition and data modeling in chemometrics. They are adapting at identifying underlying patterns in multi-dimensional and noisy chemical data. ANNs have become an important tool in the field of oil spill fingerprinting because of their ability to handle complex, nonlinear data and extract relevant patterns. This ability is essential in environmental forensics, where identifying the origin of oil spills is critical to reducing environmental damage and identifying the culprit. The Artificial Neural Networks (ANNs) are machine learning techniques and cognitive science that solve time-series forecasting issues, recognize patterns, and produce state-of-the-art outcomes for a wide range of tasks that are difficult to complete with rule-based programming, such as the voice recognition and computer vision. The ANNs are commonly defined as a biologically adaptive technique using the three sorts of parameters listed below: 1. The pattern of connections among the distinct layers of neurons. 2. The interconnectivity weight's learning procedure for updating. 3. The activation function, which is the method by which the weighted input of a neuron is transformed into the activation of its output. Complex hydrocarbon mixtures that differ based on the source (crude oil, refined products, etc.) are found in oil spills. To differentiate between various oil sources, ANNs can evaluate chemical composition data, such as gas chromatography-mass spectrometry (GC–MS) results. The ANN is trained using features including weathering effects, hydrocarbon ratios, and biomarker profiles to categorize samples according to their source. Anyhow, in this book oil spill datasets from GC-FID and GC–MS are used to run the ANNs.