Rapid measurement of fatty acid content during flour storage using a color-sensitive gas sensor array: Comparing the effects of swarm intelligence optimization algorithms on sensor features

Gas Graph Convolutional Transformer for Robust Generalization in Adaptive Gas Mixture Concentration Estimation

Gas concentration estimation has a tremendous research significance in various fields. However, existing methods for estimating the concentration of mixed gases generally depend on specific data-preprocessing methods and suffer from poor generalizability to diverse types of gases. This paper proposes a graph neural network-based gas graph convolutional transformer model (GGCT) incorporating the information propagation properties and the physical characteristics of temporal sensor data. GGCT accurately predicts mixed gas concentrations and enhances its generalizability by analyzing the concentration tokens. The experimental results highlight the GGCT's robust performance, achieving exceptional levels of accuracy across most tested gas components, underscoring its strong potential for practical applications in mixed gas analysis.

Determination of procymidone residues in rapeseed oil based on olfactory visualization technology

A new means about olfactory visualization technique for the quantitative analysis of procymidone residues in rapeseed oil has been proposed. First, an olfactory visualization system was set up to collect volatile odor information from rapeseed oil samples containing different concentrations of procymidone residues. Then, we utilized four intelligent optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO) and simulated annealing (SA), to optimize the characteristics of the sensors. Finally, support vector machine regression (SVR) models employing optimized features were constructed for the quantitative detection of procymidone residues in rapeseed oil. The study demonstrated that the SA-SVR model demonstrated superior prediction results, achieving a high determination coefficient of prediction (R P 2 ) at 0.9894. As indicated by the results, it is possible to successfully conduct non-destructive detection of procymidone residues in edible oil by the olfactory visualization technology.

Fusion-based strategy of CSA and mobile NIR for the quantification of free fatty acid in wheat varieties coupled with chemometrics

The use of sensor fusion, a novel method of combining artificial senses, has become increasingly popular in the assessment of food quality. This study employed a combination of the colorimetric sensor array (CSA) and mobile near-infrared (NIR) spectroscopy to predict free fatty acids in wheat flour. In conjunction with a partial least squares model, Low- and mid-level fusion strategies were used for quantification. Accordingly, performance of the built model was evaluated based on higher correlation coefficients between calibration and prediction (R_C and R_P), lower root mean square error of prediction (RMSEP), and a higher residual predictive deviation (RPD). The mid-level fusion coupled PLS model produced superior data fusion findings, with R_C = 0.8793, RMSECV = 7.91 mg/100 g, R_P = 0.8747, RMSEP = 6.99 mg/100 g, and RPD = 2.27. The findings of the study suggest that the NIR-CSA fusion approach could be effectively applied to the prediction of free fatty acids in wheat flour.

A Single Objective Crow Search for Modelling of Horizontal Flexible Plate Structure

The magnificent features of flexible plate structure, including lightweight and high-speed response, resulted in additional market demand, especially in the automotive and manufacturing industries. Nevertheless, the structure may incur structural damage and performance degradation when the system encounters excessive vibration. Therefore, a system identification approach utilising a metaheuristic algorithm via crow search to develop a horizontal flexible plate (HFP) model for vibration control is introduced in this paper. Crow search (CS) is a modern algorithm inspired by a crow’s intellectual operation to store additional food and memorise the food storage location. In this study, CS is employed to optimise the objective function, which is the mean squared error for accomplishing a precise predicted model in replicating the dynamic response of the actual structure. Hence, the preliminary action for modelling using this approach is designing and fabricating an HFP rig for experimentally gathering the real input-output vibration data. After that, the mathematical modelling utilising the CS algorithm was implemented using a parametric model structure. Finally, the best-fit model is chosen for the representation of the HFP based on the lowest mean squared error, correlation test within a 95% confidence level and stability in a pole-zero plot. The simulation result reveals that the CS algorithm with a second-order estimated model accomplished a minimum MSE of 1.1168 × 10-5, an unbiased correlation test and excellent stability for the HFP structure.

Visual Monitoring of Fatty Acid Degradation during Green Tea Storage by Hyperspectral Imaging

The reduction in freshness during green tea storage leads to a reduction in its commercial value and consumer acceptance, which is thought to be related to the oxidation of fatty acids. Here, we developed a novel and rapid method for the assessment of green tea freshness during storage. Hyperspectral images of green tea during storage were acquired, and fatty acid profiles were detected by GC-MS. Partial least squares (PLS) analysis was used to model the association of spectral data with fatty acid content. In addition, competitive adaptive reweighted sampling (CARS) was employed to select the characteristic wavelengths and thus simplify the model. The results show that the constructed CARS-PLS can achieve accurate prediction of saturated and unsaturated fatty acid content, with residual prediction deviation (RPD) values over 2. Ultimately, chemical imaging was used to visualize the distribution of fatty acids during storage, thus providing a fast and nondestructive method for green tea freshness evaluation.

Rapid Characterization of Black Tea Taste Quality Using Miniature NIR Spectroscopy and Electronic Tongue Sensors

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K-nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension

The structure of a back propagation neural network was optimized by a particle swarm optimization (PSO) algorithm, and a back propagation neural network model based on a PSO algorithm was constructed. By comparison with a general back propagation neural network and logistic regression, the fitting performance and prediction performance of the PSO algorithm is discussed. Furthermore, based on the back propagation neural network optimized by the PSO algorithm, the risk factors related to hypertension were further explored through the mean influence value algorithm to construct a risk prediction model. In the evaluation of the fitting effect, the root mean square error and coefficient of determination of the back propagation neural network based on the PSO algorithm were 0.09 and 0.29, respectively. In the comparison of prediction performance, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the back propagation neural network based on PSO algorithm were 85.38%, 43.90%, 96.66%, and 0.86, respectively. The results showed that the backpropagation neural network optimized by PSO had the best fitting effect and prediction performance. Meanwhile, the mean impact value algorithm could screen out the risk factors related to hypertension and build a disease prediction model, which can provide clues for exploring the pathogenesis of hypertension and preventing hypertension.

Parameter Optimization of Support Vector Machine to Improve the Predictive Performance for Determination of Aflatoxin B1 in Peanuts by Olfactory Visualization Technique

This study proposes a novel method for detection of aflatoxin B₁ (AFB₁) in peanuts using olfactory visualization technique. First, 12 kinds of chemical dyes were selected to prepare a colorimetric sensor to assemble olfactory visualization system, which was used to collect the odor characteristic information of peanut samples. Then, genetic algorithm (GA) with back propagation neural network (BPNN) as the regressor was used to optimize the color component of the preprocessed sensor feature image. Support vector regression (SVR) quantitative analysis model was constructed by using the optimized combination of characteristic color components to achieve determination of the AFB₁ in peanuts. In this process, the optimization performance of grid search (GS) algorithm and sparrow search algorithm (SSA) on SVR parameter was compared. Compared with GS-SVR model, the model performance of SSA-SVR was better. The results showed that the SSA-SVR model with the combination of seven characteristic color components obtained the best prediction effect. Its correlation coefficients of prediction (R_P) reached 0.91. The root mean square error of prediction (RMSEP) was 5.7 μg·kg^-1, and ratio performance deviation (RPD) value was 2.4. The results indicate that it is reliable to use the colorimetric sensor array with strong specificity for the determination of the AFB₁ in peanuts. In addition, it is necessary to properly optimize the parameters of the prediction model, which can obviously improve the generalization performance of the multivariable model.

Effects of packaging and ripeness on plantain flour characteristics during storage

The increasing implication of plantain flour in various food formulations calls for the need to evaluate the effects of ripening stage, packaging materials, and storage duration on its proximal composition and functional properties. For this study, plantain flours were produced from the cultivar Alloga at unripe and semiripe stage 3. They were stored both in transparent polyethylene bags and in an opaque aluminum foil. Physicochemical analyses and functional characterization of the plantain flour were performed on samples taken prior to storage and on monthly basis for 6 months during storage. Ash and carbohydrate contents decreased while the yellowness and redness increased with ripening. Pasting viscosity drastically decreased with ripening. During storage, significant differences in color and among most functional characteristics were observed as a consequence of both storage duration and packaging materials. Based on this research, flour from semiripe plantain could be recommended for use in formulations requiring low viscosity. Besides, it is suggested to store plantain flours in opaque containers to reduce the variability in its properties, thus maintaining its original quality.

An efficient differential sensing strategy for phenolic pollutants based on the nanozyme with polyphenol oxidase activity

To realize the efficient differential sensing of phenolic pollutants in sewage, a novel sensing strategy was successfully developed based on one nanozyme (GMP-Cu) with polyphenol oxidase activity. Phenolic pollutants can be oxidized by GMP-Cu, and the oxidation products reacts subsequently with 4-aminoantipyrine to produce a quinone-imine compound. The absorption spectra of final quinone-imine products resulted from different phenolic pollutants showed obvious differences, which were due to the interaction difference between GMP-Cu and phenolic pollutants, as well as the different molecular structures of the quinone-imine products from different phenolic pollutants. Based on the difference of absorption spectra, a novel differential sensing strategy was developed. The genetic algorithm was used to select the characteristic wavelengths at different enzymatic reaction times, HCA and PLS-DA algorithms were utilized for the discriminant sensing of seven representative phenolic pollutants, including hydroquinone, resorcinol, catechol, resorcinol, phenol, p-chlorophenol, and 2,4-dichlorophenol. Scientific wavelength selection algorithm and recognition algorithm resulted in the successful identification of phenolic pollutants in sewage with a discriminant accuracy of 100%, and differentiation of the phenolic pollutants regardless of their concentration. These results indicate that sensing strategy can be used as an effective tool for the efficient identification and differentiation of phenolic pollutants in sewage.

A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification

BACKGROUND

As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What's more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease.

METHODS

In this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension.

RESULTS

Wavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model.

CONCLUSION

In summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.