Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network

Abstract

pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectroscopy. Two groups of Vis-NIR spectral analysis experiments of water pH detection were employed to evaluate the performance of 1D-CNN. Two conventional multivariate regression calibration methods, including partial least squares (PLS) and least squares support vector machine (LS-SVM), were introduced for comparative analysis with 1D-CNN. The successive projections algorithm (SPA) was adopted to select the feature variables. In addition, the learning mechanism of 1D-CNN was interpreted through visual feature maps by convolutional layers. The results showed that the 1D-CNN models obtained the highest prediction accuracy based on full spectra for the two experiments. For the spectrophotometer experiment, the root mean square error of prediction (RMSEP) was 0.7925, and the determination coefficient of prediction () was 0.8515. For the grating spectrograph experiment, the RMSEP was 0.5128 and the was 0.9273. The convolutional layers could automatically preprocess the spectra and effectively extract the spectra features. Compared with the traditional regression methods, 1D-CNN does not need complex spectra pretreatment and variable selection. Therefore, 1D-CNN is a promising regression approach, with higher prediction accuracy and better modeling convenience for rapid water pH detection using Vis-NIR spectroscopy.

Identification of Air-Writing Tamil Alphabetical Vowel Characters

“…There are multiple benefits associated with the utilization of one-dimensional convolutional neural networks (1D CNNs) [28]. These advantages include notable performance on datasets with limited amounts of data, reduced computational complexity in comparison to two-dimensional CNNs and other deep learning architectures, expedited training processes, and a strong capability to extract pertinent features from sequential data and time series, such as signal data [26], [36].…”

“…Furthermore, the use of 1D CNNs has been explored in other domains as well. applied a 1D CNN algorithm for the detection of water pH using visible near-infrared spectroscopy [28]. They interpreted the learning mechanism of the 1D CNN through visual feature maps generated by the convolutional layers.…”

“…Examples of such sensor data include electrocardiogram readings, temperature measurements, environment-control variables, motion data, and power consumption data. One potential approach for modeling this information is the utilization of one-dimensional convolutional neural networks (1D CNN) [28], [29], [36].…”

“…Researchers have found that using CNNs to classify time series is better than other methods in a number of important ways. This is because CNNs are very good at ignoring noise and can pull out very useful, deep features that are not affected by time [28]. In the context of CNN, one-dimensional convolutional neural networks (1D CNNs) excel at extracting useful features from smaller (fixed-length) subsets of a larger data set [29] and at analyzing audio signals, cyber security, NLP, and time series data from sensors [18], [30].…”

Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning

“…Due to fewer parameters, they are often less prone to overfitting than 2D‐CNN. 1D‐CNN have shown excellent results in measuring pH levels by utilizing sequential data 19 . The feasibility of establishing a moisture content prediction model for the Tencha drying process using computer vision to extract image features as sequential data in combination with 1D‐CNN remains to be explored.…”