The quality of soybeans from different growing areas is different. It is common for low-quality soybeans to fake high-quality soybeans. This paper proposes a lightweight residual convolutional neural network (LRCNN) combined with an electronic nose (e-nose) to realize soybean quality traceability. Firstly, obtain soybean gas information from different growing areas through the e-nose. Then, according to the characteristics of e-nose detection data, the grouped heterogeneous kernel-based convolution (GHConv) is proposed, which effectively reduces the number of parameters through the combination of grouping and heterogeneous convolution. Finally, the LRCNN is proposed, which reduces the number of network parameters and avoids feature degradation, realizing the high-precision identification of soybean quality differences. In the multi-model comparison, the classification accuracy of the network is 98.37%, recall is 98.20%, and precision is 98.49%. The results show that the LRCNN combined with the e-nose can effectively identify the gas information of soybeans from different growing areas, providing a new method for soybean quality traceability.

Cortisol is commonly used as a significant biomarker of psychological or physical stress. With the accelerated pace of life, non-invasive cortisol detection at the point of care (POC) is in high demand for personal health monitoring. In this paper, an ultrasensitive immunosensor using gold nanoparticles/molybdenum disulfide/gold nanoparticles (AuNPs/MoS₂/AuNPs) as transducer was explored for non-invasive salivary cortisol monitoring at POC with the miniaturized differential pulse voltammetry (DPV) system based on a smartphone. Covalent binding of cortisol antibody (CORT-Ab) onto the AuNPs/MoS₂/AuNPs transducer was achieved through the self-assembled monolayer of specially designed polyethylene glycol (PEG, SH-PEG-COOH). Non-specific binding was avoided by passivating the surface with ethanolamine. The miniaturized portable DPV system was utilized for human salivary cortisol detection. A series current response of different cortisol concentrations decreased and exhibited a linear range of 0.5–200 nM, the detection limit of 0.11 nM, and high sensitivity of 30 μA M⁻¹ with a regression coefficient of 0.9947. Cortisol was also distinguished successfully from the other substances in saliva. The recovery ratio of spiked human salivary cortisol and the variation of salivary cortisol level during one day indicated the practicability of the immunosensor based on the portable system. The results demonstrated the excellent performance of the smartphone-based immunosensor system and its great potential application for non-invasive human salivary cortisol detection at POC.

The effective feature mining method is one of the key problems in the field of pattern recognition. Moreover, the lack of efficient feature extraction and selection methods has limited the application and development of electronic nose (e-nose) technology. In this study, a synergetic strategy for e-nose sensors data feature mining was proposed in combination with Support Vector Machine (SVM) to determine the beer olfactory information. First, twenty time-domain features and twenty frequency-domain features of e-nose sensors data were extracted to represent the olfactory characteristics of beer. Second, forty features were sorted with variable importance in projection (VIP) scores and forty subsets of multi-features with the best VIP score were generated. Finally, the classification models were established based on SVM, and the best parameter c and g of SVM models was calculated by Genetic Algorithm (GA). Furthermore, the classification performance of each class was evaluated by efficiency value (EFF) in different feature sets. The result indicates that GA-SVM model achieves good classification performance based on the #27 feature set with 81.67% and 96.67% in calibration set and testing set, respectively, and the EFF value is also the highest compared with other feature sets. In conclusion, it indicated that the analytical method can be used as a reliable tool for accurate identification of beer olfactory information.

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.