To improve the classification and regression performance of the total volatile basic nitrogen (TVB-N) and acid value (AV) of different freshness fish meal samples detected by a metal-oxide semiconductor electronic nose (MOS e-nose), 402 original features, 62 manually extracted features, manually extracted and selected features by the RFRFE method, and the features extracted by the long short-term memory (LSTM) network were used as inputs to identify the freshness. The classification performance of the freshness grades and the estimation performance of the TVB-N and AV values of fish meal with different freshness were compared. According to the sensor response curve, preprocessing and feature extraction steps were first applied to the original data. Then, five classification algorithms and four regression algorithms were used for modeling. The results showed that a total of 30 features were extracted using the LSTM network, and the number of extracted features was significantly reduced. In the classification, the highest accuracy rate of 95.4% was obtained using the support vector machine method. In the regression, the least squares support vector regression method obtained the best root mean square error (RMSE). The coefficient of determination (R2), RMSE, and relative standard deviation (RSD) between the predicted value of TVBN and the actual value were 0.963, 11.01, and 7.9%, respectively. The R2, RMSE, and RSD between the predicted value of AV and the actual value were 0.972, 0.170, and 6.05%, respectively. The LSTM feature extraction method provided a new method and reference for feature extraction using an E-nose to identify other animal-derived material samples.

OBJECTIVE: We conducted a study on women with sensitive skin of various skin tones to analyse their skin characteristics and preferences for foundation shades.
METHODS: Volunteers were categorized based on their individual typological angle, and their preferences were assessed using self-perception and software-based mass aesthetic assessment. The Baumann Questionnaire is a valuable tool for identifying patients with sensitive skin and gaining a comprehensive understanding of their skin sensitivity. The skin characteristics of two groups were compared using a more suitable classification method.
RESULTS: Individuals diagnosed with sensitive skin typically have skin tones classified as Types I, II and III, with Type I being the most common in sensitive skin cases. The sensitive group exhibited higher levels of transepidermal water loss, lighter skin tone, lower yellowness, increased glossiness, higher haemoglobin content, more acne, fewer blackheads, and fewer pores. Among them, Type I skin is characterized by lower elasticity, increased oiliness, higher hydration levels and fewer visible pores. Type II skin is characterized by lower hydration levels, higher oiliness and increased redness. Type III exhibits more pores, decreased oiliness and enhanced elasticity. Foundations No. 2 and No. 3 are fairer than foundations No. 1 and No. 4. In the self-assessment, Type I and Type II subjects preferred No. 3, while Type III subjects preferred No. 1 and No. 4 because they matched their skin tone. The results of the software evaluation showed that popular aesthetics preferred Type I and Type II to use No. 2, and Type III to use No. 2 and No. 3, as they resulted in a fairer complexion.
CONCLUSIONS: Sensitive skin of different skin tone types confronts different skin problems. The findings also highlight the public\'s inclination towards lighter foundation shades, despite the common practice of selecting shades that harmonize with one\'s inherent skin tone.
OBJECTIVE: Nous avons mené une étude sur des femmes à la peau sensible de différentes carnations afin d\'analyser les caractéristiques de leur peau et leurs préférences en matière de teintes de fond de teint. MÉTHODES: Les volontaires ont été classées en fonction de leur angle typologique individuel et leurs préférences ont été évaluées à l\'aide d\'une auto‐perception et d\'une évaluation esthétique de masse basée sur un logiciel. Le questionnaire de Baumann est un outil précieux pour identifier les patients à la peau sensible et obtenir une compréhension globale de leur sensibilité cutanée. Les caractéristiques cutanées de deux groupes ont été comparées à l\'aide d\'une méthode de classification plus appropriée. RÉSULTATS: Les personnes chez qui l\'on a diagnostiqué une peau sensible ont généralement des teintes de peau classées en types I, II et III, le type I étant le plus courant dans les cas de peau sensible. Le groupe sensible présente des niveaux plus élevés de perte d\'eau transépidermique, un teint plus clair, une couleur moins jaune, une brillance accrue, une teneur en hémoglobine plus élevée, plus d\'acné, moins de points noirs et moins de pores. Parmi eux, la peau de type I se caractérise par une élasticité plus faible, un taux de sébum plus élevé, des niveaux d\'hydratation plus élevés et moins de pores visibles. La peau de type II se caractérise par des niveaux d\'hydratation plus faibles, un taux de sébum plus élevé et des rougeurs plus importantes. Le type III présente plus de pores, une diminution de l\'aspect gras et une meilleure élasticité. Les fonds de teint n° 2 et n° 3 sont plus clairs que les fonds de teint n° 1 et n° 4. Lors de l\'auto‐évaluation, les sujets des types I et II ont préféré le fond de teint n° 3, tandis que les sujets du type III ont préféré le fond de teint n° 1 et le fond de teint n° 4 parce qu\'ils correspondaient à leur carnation. Les résultats de l\'évaluation du logiciel ont montré que l\'esthétique populaire préférait que les sujets de type I et de type II utilisent le n° 2, et que les sujets de type III utilisent le n° 2 et le n° 3, car ils donnaient un teint plus clair.
CONCLUSIONS: Les peaux sensibles de différents types de carnation sont confrontées à des problèmes cutanés différents. Les résultats mettent également en évidence le penchant du public pour les teintes de fond de teint plus claires, malgré la pratique courante consistant à choisir des teintes qui s\'harmonisent avec le teint inhérent à la peau.

The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain-computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5-10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.

Solid waste produced by the nonferrous smelting industry has a significant number of notable differences. The lack of recognition of solid waste characteristics is the main factor restricting its disposal and utilization. In this study, we analyzed the main production processes of the nonferrous smelting industry; identified the key production nodes of solid waste; and clarified the characteristics, including the physical, chemical, and pollution characteristics of solid wastes, through a large sample statistical analysis. We found similarities among solid wastes from a common generation source as well as notable differences among the different generation sources: slags and sludges from waste acid treatment and wastewater treatment units had a water content of 27.43-52.71% and 51.14-68.27%, respectively, which were significantly higher than those of other metallurgy and dust collection units; the pH of slags from an electrorefining unit was strongly alkaline; the mineral phase of sludges from wastewater treatment was only calcite; slags from a waste acid treatment unit were mainly in phase of gypsum, claudetite, and anglesite; the chemical composition of slags from pyrometallurgy and hydrometallurgy units was mainly SiO2 and Fe2O3. In this paper, we discuss a new classification method based on a common generation source for the first time. These results are beneficial to guide the disposal, utilization, and management of solid waste.

The use of tobacco stems as raw material for cigarettes reduces cost and improves the flammability of cigarettes. However, various impurities, such as plastic, reduce the purity of tobacco stems, degrade the quality of cigarettes, and endanger the health of smokers. Therefore, the correct classification of tobacco stems and impurities is crucial. This study proposes a method based on hyperspectral image superpixels and the use of light gradient boosting machine (LightGBM) classifier to categorize tobacco stems and impurities. First, the hyperspectral image is segmented using superpixels. Second, the gray-level co-occurrence matrix extracts the texture features of superpixels. Subsequently, an improved LightGBM is applied and trained with the spectral and textural features of superpixels as a classification model. Several experiments were implemented to evaluate the performance of the proposed method. The results show that the classification performance based on superpixels is better than that based on single-pixel points. The classification model based on superpixels (10 × 10 px) achieved the highest impurity recognition rate (93.8%). This algorithm has already been applied to industrial production in cigarette factories. It exhibits considerable potential in overcoming the influence of interference fringes to promote the intelligent industrial application of hyperspectral imaging.

As rice is one of the world\'s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

UNASSIGNED: Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides.
UNASSIGNED: In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set.
UNASSIGNED: The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides.
UNASSIGNED: We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.

Flower classification is of great importance to the research fields of plants, food, and medicine. Due to more abundant information on three-dimensional (3D) flower models than two-dimensional 2D images, it makes the 3D models more suitable for flower classification tasks. In this study, a feature extraction and classification method were proposed based on the 3D models of Chinese roses. Firstly, the shape distribution method was used to extract the sharpness and contour features of 3D flower models, and the color features were obtained from the Red-Green-Blue (RGB) color space. Then, the RF-OOB method was employed to rank the extracted flower features. A shape descriptor based on the unique attributes of Chinese roses was constructed, χ2 distance was adopted to measure the similarity between different Chinese roses. Experimental results show that the proposed method was effective for the retrieval and classification tasks of Chinese roses, and the average classification accuracy was approximately 87%, which can meet the basic retrieval requirements of 3D flower models. The proposed method promotes the classification of Chinese roses from 2D space to 3D space, which broadens the research method of flower classification.

The cerebellar region has four times as many brain cells as the brain, but whether the cerebellum functions in cognition, and how it does so, remain unexplored. In order to verify whether the cerebellum is involved in cognition, we chose to investigate whether the cerebellum is involved in the process of error judgment. We designed an experiment in which we could activate the subject\'s error-related potentials (ErrP). We recruited 26 subjects and asked them to wear EEG caps with cerebellar regions designed by us to participate in the experiment so that we could record their EEG activity throughout the experiment. We successfully mitigated the majority of noise interference after a series of pre-processing of the data collected from each subject. Our analysis of the preprocessed data revealed that our experiment successfully activated ErrP, and that the EEG signals, including the cerebellum, were significantly different when subjects made errors compared to when they made correct judgments. We designed a feature extraction method that requires selecting channels with large differences under different classifications, firstly by extracting the time-frequency features of these channels, and then screening these features with sequence backward feature (SBS) selection. We use the extracted features as the input and different event types in EEG data as the labels for multiple classifiers to classify the data in the executive and feedback segments, where the average accuracy for two-class classification of executive segments can reach 80.5%. The major contribution of our study is the discovery of the presence of ErrP in cerebellar regions and the extraction of an effective feature extraction method for EEG data.

Alternative routes such as virus transmission or cross-contamination by food have been suggested, due to reported cases of SARS-CoV-2 in frozen chicken wings and fish or seafood. Delay in routine testing due to the dependence on the PCR technique as the standard method leads to greater virus dissemination. Therefore, alternative detection methods such as FTIR spectroscopy emerge as an option. Here, we demonstrate a fast (3 min), simple and reagent-free methodology using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for discrimination of food (chicken, beef and fish) contaminated with the SARS-CoV-2 virus. From the IR spectra of the samples, the \"bio-fingerprint\" (800 - 1900 cm-1) was selected to investigate the distinctions caused by the virus contamination. Exploratory analysis of the spectra, using Principal Component of Analysis (PCA), indicated the differentiation in the data due to the presence of single bands, marked as contamination from nucleic acids including viral RNA. Furthermore, the partial least squares discriminant analysis (PLS-DA) classification model allowed for discrimination of each matrix in its pure form and its contaminated counterpart with sensitivity, specificity and accuracy of 100 %. Therefore, this study indicates that the use of ATR-FTIR can offer a fast and low cost and not require chemical reagents and with minimal sample preparation to detect the SARS-CoV-2 virus in food matrices, ensuring food safety and non-dissemination by consumers.