Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model.

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Salivary Cortisol Determination on Smartphone-Based Differential Pulse Voltammetry System.

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.

Evaluation of Food Fineness by the Bionic Tongue Distributed Mechanical Testing Device.

In this study, to obtain a texture perception that is closer to the human sense, we designed eight bionic tongue indenters based on the law of the physiology of mandibular movements and tongue movements features, set up a bionic tongue distributed mechanical testing device, performed in vitro simulations to obtain the distributed mechanical information over the tongue surface, and preliminarily constructed a food fineness perception evaluation model. By capturing a large number of tongue movements during chewing, we analyzed and simulated four representative tongue movement states including the tiled state, sunken state, raised state, and overturned state of the tongue. By analyzing curvature parameters and the Gauss curvature of the tongue surface, we selected the regional circle of interest. With that, eight bionic tongue indenters with different curvatures over the tongue surface were designed. Together with an arrayed film pressure sensor, we set up a bionic tongue distributed mechanical testing device, which was used to do contact pressure experiments on three kinds of cookies-WZ Cookie, ZL Cookie and JSL Cookie-with different fineness texture characteristics. Based on the distributed mechanical information perceived by the surface of the bionic tongue indenter, we established a food fineness perception evaluation model by defining three indicators, including gradient, stress change rate and areal density. The correlation between the sensory assessment and model result was analyzed. The results showed that the average values of correlation coefficients among the three kinds of food with the eight bionic tongue indenters reached 0.887, 0.865, and 0.870, respectively, that is, a significant correlation was achieved. The results illustrate that the food fineness perception evaluation model is effective, and the bionic tongue distributed mechanical testing device has a good practical significance for obtaining food texture mouthfeel information.

Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation.

In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.

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.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose.

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.

Olfactory EEG induced by odor: Used for food identification and pleasure analysis.

As the need for food authenticity verification increases, sensory evaluation of food odors has become widely recognized. This study presents a theory based on electroencephalography (EEG) to create an Olfactory Perception Dimensional Space (EEG-OPDS), using feature engineering and ensemble learning to establish material and emotional spaces based on odor perception and pleasure. The study examines the intrinsic connection between these two spaces and explores the mechanisms of integration and differentiation in constructing the OPDS. This method effectively visualizes various types of food odors while identifying their perceptual intensity and pleasantness. The average classification accuracy for odor recognition in an eight-category experiment is 96.1%. Conversely, the average classification accuracy for sensory pleasantness recognition in a two-category experiment is 98.8%. The theoretical approach proposed in this study, based on olfactory EEG signals to construct an OPDS, captures the subtle perceptual differences and individualized pleasantness responses to food odors.

Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests that plants have the potential to be used as biosensors for monitoring environmental information. However, there are current challenges in linking environmental information with plant electrical signals, especially in collecting and classifying the corresponding electrical signals under soil moisture gradients. This study documented the electrical signals of clivia under different soil moisture gradients and created a dataset for classifying electrical signals. Subsequently, we proposed a lightweight convolutional neural network (CNN) model (PlantNet) for classifying the electrical signal dataset. Compared to traditional CNN models, our model achieved optimal classification performance with the lowest computational resource consumption. The model achieved an accuracy of 99.26%, precision of 99.31%, recall of 92.26%, F1-score of 99.21%, with 0.17M parameters, a size of 7.17MB, and 14.66M FLOPs. Therefore, this research provides scientific evidence for the future development of plants as biosensors for detecting soil moisture, and offers insight into developing plants as biosensors for detecting signals such as ozone, PM2.5, Volatile Organic Compounds(VOCs), and more. These studies are expected to drive the development of environmental monitoring technology and provide new pathways for better understanding the interaction between plants and the environment.

FBANet: An Effective Data Mining Method for Food Olfactory EEG Recognition.

At present, the sensory evaluation of food mostly depends on artificial sensory evaluation and machine perception, but artificial sensory evaluation is greatly interfered with by subjective factors, and machine perception is difficult to reflect human feelings. In this article, a frequency band attention network (FBANet) for olfactory electroencephalogram (EEG) was proposed to distinguish the difference in food odor. First, the olfactory EEG evoked experiment was designed to collect the olfactory EEG, and the preprocessing of olfactory EEG, such as frequency division, was completed. Second, the FBANet consisted of frequency band feature mining and frequency band feature self-attention, in which frequency band feature mining can effectively mine multiband features of olfactory EEG with different scales, and frequency band feature self-attention can integrate the extracted multiband features and realize classification. Finally, compared with other advanced models, the performance of the FBANet was evaluated. The results show that FBANet was better than the state-of-the-art techniques. In conclusion, FBANet effectively mined the olfactory EEG data information and distinguished the differences between the eight food odors, which proposed a new idea for food sensory evaluation based on multiband olfactory EEG analysis.

Identification of white degradable and non-degradable plastics in food field: A dynamic residual network coupled with hyperspectral technology.

In the food field, with the improvement of people's health and environmental protection awareness, degradable plastics have become a trend to replace non-degradable plastics. However, their appearance is very similar, making it difficult to distinguish them. This work proposed a rapid identification method for white non-degradable and degradable plastics. Firstly, a hyperspectral imaging system was used to collect the hyperspectral images of the plastics in visible and near-infrared bands (380-1038 nm). Secondly, a residual network (ResNet) was designed according to the characteristics of hyperspectral information. Finally, a dynamic convolution module was introduced into the ResNet to establish a dynamic residual network (Dy-ResNet) to adaptively mine the data features and realize the classification of the degradable and non-degradable plastics. Dy-ResNet had better classification performance than the other classical deep learning methods. The classification accuracy of the degradable and non-degradable plastics was 99.06%. In conclusion, hyperspectral imaging technology was combined with Dy-ResNet to identify the white non-degradable and degradable plastics effectively.

Electronic nose based on an optimized competition neural network.

In view of the fact that there are disadvantages in that the class number must be determined in advance, the value of learning rates are hard to fix, etc., when using traditional competitive neural networks (CNNs) in electronic noses (E-noses), an optimized CNN method was presented. The optimized CNN was established on the basis of the optimum class number of samples according to the changes of the Davies and Bouldin (DB) value and it could increase, divide, or delete neurons in order to adjust the number of neurons automatically. Moreover, the learning rate changes according to the variety of training times of each sample. The traditional CNN and the optimized CNN were applied to five kinds of sorted vinegars with an E-nose. The results showed that optimized network structures could adjust the number of clusters dynamically and resulted in good classifications.

A residual network with attention module for hyperspectral information of recognition to trace the origin of rice.

In this work, a neural network framework for hyperspectral information recognition was proposed, combined with residual block and convolutional block attention module (CBAM) to enhance the detection performance of hyperspectral for tracing the rice quality. Firstly, the hyperspectral image system was used to obtain the hyperspectral information of the rice. Secondly, due to the small data set, the structure of the residual network was designed based on the characteristics of the hyperspectral information to prevent overfitting the model. Finally, the CBAM was introduced to calculate the channel and spatial attention to redistribute the weight parameter and enhance the classification performance of the model. The results showed that our (Res-CBAM) model had better classification performance than other classification methods. The classification accuracy of the rice was 96.33%. This study provided a strategy to enhance the detection performance of hyperspectral, and an intelligent technology to trace the rice quality.

[Effects of different irrigation modes on winter wheat grain yield and water- and nitrogen use efficiency].

Taking the widely planted winter wheat cultivar Tainong 18 as test material, a field experiment was conducted to study the effects of different irrigation modes on the winter wheat grain yield and water- and nitrogen use efficiency in drier year (2009-2010) in Tai' an City of Shandong Province, China. Five treatments were installed, i. e., irrigation before sowing (CK), irrigation before sowing and at jointing stage (W1), irrigation before sowing and at jointing stages and at over-wintering stage with alternative irrigation at milking stage (W2), irrigation before sowing and at jointing and flowering stages (optimized traditional irrigation mode, W3), and irrigation before sowing and at over-wintering, jointing, and milking stages (traditional irrigation mode, W4). The irrigation amount was 600 m3 hm(-2) one time. Under the condition of 119.7 mm precipitation in the winter wheat growth season, no significant difference was observed in the grain yield between treatments W2 and W4, but the water use efficiency was significantly higher in W2 than in W4. Comparing with treatment W3, treatments W2 and W4 had obviously higher grain yield, but the water use efficiency had no significant difference. The partial factor productivity from N fertilization was the highest in W2 and W4, and the NO3(-)-N accumulation amount in 0-100 cm soil layer at harvest was significantly higher in W2 than in W3 and W4, suggesting that W2 could reduce NO3(-)-N leaching loss. Under the conditions of our experiment, irrigation before sowing and jointing stages and at over-wintering stage with alternative irrigation at milking stage was the optimal irrigation mode in considering both the grain yield and the water- and nitrogen use efficiency.

Synthesis of the racemate of (Z)-exo-alpha-bergamotenal, a pheromone component of the white-spotted spined bug, Eysarcoris parvus Uhler.

The racemate of (Z)-exo-alpha-bergamotenal, a sex pheromone component of the white-spotted spined bug, was synthesized from racemic exo-alpha-bergamotene by a five-step sequence involving regioselective epoxidation and (Z)-selective Wittig olefination reactions. The 1H- and 13C-NMR spectra of the synthetic sample were identical with those of the natural material.