Multi‐Channel Surface Acoustic Wave (SAW) Sensor Based on Artificial Back Propagation Neural (BPN) Network and Multivariate Linear Regression Analysis (MLR) for Organic Vapors

A six‐channel surface acoustic wave (SAW) detection system with a 315 MHz one‐port quartz resonator and a homemade computer interface for signal acquisition and data processing was developed to detect various organic vapors. The oscillating frequency of the SAW quartz crystal decreased due to the adsorption of organic molecules on the coating materials. Polyethylene glycol, 18 crown 6 (18C6), Cr³⁺/cryptand‐22, stearic acid, polyvinylpyrrolidene and triphenyl phosphine coated quartz crystals were used as sensors. An artificial back propagation neural (BPN) network was used to recognize various organic gases such as hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid and 1‐propylamine. It showed not only the distinction of unity of organic vapors but also mixtures of gases. The learning rate and the hidden unit of a neural network system for BPN analysis were investigated. Furthermore, the concentrations of these organic vapors were computed with about 10% error by multivariate linear regression analysis (MLR). MLR analysis with a multichannel SAW sensor was applied to determine the concentration of each component in a mixture of 1‐hexene, 1‐hexyne and propionaldehyde.     

Multi‐Channel Surface Acoustic Wave Sensors Based on Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) for Organic Vapors

A multi‐channel surface acoustic wave (SAW) detection system which is employed to detect various organic molecules in a static system was prepared using 315 MHz one‐port quartz resonators and a home‐made computer interface for signal acquisition and data process. The oscillating frequency of the quartz crystal decreases on adsorption of organic molecules on the coating materials. The principal component analysis (PCA) method with SAS software was applied to select the appropriate coating materials onto the SAW crystals for organic vapors, e.g. hexane, 1‐hexene, 1‐hexyne, 1‐propanol, propionaldehyde, propionic acid, and 1‐propylamine. A dataset for a multi‐channel sensor with 19 SAW crystals for 7 analyses was collected after comparing the correlation between the 19 coating materials and the first six principal component (PC) factor. Furthermore, linear discriminate analysis (LDA) with SPSS software and a profile discrimination map were also applied and discussed for the discrimination of these organic vapors. These organic molecules could be clearly distinguished by the six‐channel SAW static sensor. The effect of concentration for various organic vapors was investigated and discussed.     

Bi‐channel Surface Acoustic Wave Gas Sensor for Carbon Disulfide and Methanol Vapors in Polymer Plants

A C₆₀‐polyphenylacetylene (C₆₀‐PPA) and polyvinylpyrrolidone (PVP) coated two‐channel surface acoustic wave (SAW) crystal gas sensor with a homemade computer interface for data acquisition and data processing was developed and employed to detect carbon disulfide (CS₂) and methanol (CH₃OH) vapors in polymer plants. The frequency of surface acoustic wave oscillator decreases due to the adsorption of gas molecules on the coated materials of the SAW sensor. Six coating materials (C₆₀‐PPA, nafion, PPA, crytand [2,2], polyethene glycol and PVP) were used to adsorb and detect carbon disulfide and methanol gases. Adsorption of all the six coating materials to CS₂ and CH₃OH was found to be physical adsorption. The C₆₀‐PPA coated SAW detector exhibited more sensitive to CS₂ than the other coating materials. In contrast, the PVP coated SAW detector was more sensitive to CH₃OH than the other coating materials. With the two‐channel SAW sensor, the C₆₀‐PPA coated SAW showed a good detection limit of 0.4 ppm and good reproducibility with RSD of 3.37 % (n=10) for CS₂. Similarly, the PVP coated SAW also showed a good detection limit of 0.05 ppm and good reproducibility, with RSD of 0.86 % (n=10) for CH₃OH. The interference effect of other organic molecules on the SAW detection system was negligible, except for the irreversible adsorption of C₆₀‐PPA to propylamine. The frequency signals from the two‐channel SAW sensor array C₆₀‐PPA and PVP coatings were processed by a back‐propagation artificial neural network (BPN) and multiple regression analysis (MRA). Thus a two‐channel SAW sensor array with BPN and MRA has been successfully applied for the qualitative and quantitative analyses of CS₂ and CH₃OH in mixtures.     

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

Chinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.