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Instrumental prediction of in vivo sensory properties of emollients to allow the development of new biobased ingredients 用仪器预测润肤剂的体内感官特性,以开发新的生物基成分
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-10-30 DOI: 10.1016/j.chemolab.2025.105559
Floriane Rischard , Amandine Flourat , Ecaterina Gore , Géraldine Savary
An important step in the development of novel cosmetic ingredients is the setting up of sensory analyses to assess their tactile properties. A recent work allowed the obtention of 12 novel biobased emollients with interesting physico-chemical properties. Four of the most promising emollients were selected in the present study and their safety was tested to ensure they are suitable for use on human skin. Their tactile properties, along with ten commercial emollients, were assessed by 16 expert assessors: circular spreading behavior, thickness of residual film and slippery after feel. In addition to characterizing a wide range of emollients, the results made possible the establishment of three predictive models using Partial Least Squares regressions. These original models correspond to various sensory attributes of the emollients, both during and after their application on the skin. All predictive models were then validated by leave-one-out cross validations. Only three instrumental parameters (viscosity, friction, stickiness) were necessary to build the models and predict the tactile properties. This approach was then applied to the eight other biobased emollients that were not initially used to establish the predictions in order to validate the models. Results demonstrate the significant value of such models for developing new ingredients. Ultimately, these predictive models could override the time-consuming and costly process of safety testing and sensory analyses in the research in development of future newly produced emollients for dermocosmetic applications.
开发新型化妆品成分的一个重要步骤是建立感官分析来评估其触觉特性。最近的一项工作使人们注意到12种具有有趣的物理化学性质的新型生物基润肤剂。在本研究中选择了四种最有前途的润肤剂,并对其安全性进行了测试,以确保它们适合用于人体皮肤。他们的触觉特性,连同十种商业润肤剂,由16位专家评估:圆形扩散行为,残余膜的厚度和光滑后的感觉。除了表征范围广泛的润肤剂外,结果还可以使用偏最小二乘回归建立三种预测模型。这些原始模型对应于润肤剂的各种感官属性,无论是在他们的应用在皮肤上。然后通过留一交叉验证验证所有预测模型。只需要三个仪器参数(粘度,摩擦力,粘性)就可以建立模型并预测触觉特性。然后将这种方法应用于其他八种最初未用于建立预测的生物基润肤剂,以验证模型。结果表明,这些模型对开发新成分具有重要的价值。最终,这些预测模型可以在未来新生产的皮肤美容应用润肤剂的研究开发中超越耗时和昂贵的安全测试和感官分析过程。
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引用次数: 0
A reliable deep neural network using the radial basis for the spreading virus in computers with kill signals 一种基于径向基的可靠深度神经网络,用于具有杀伤信号的计算机中病毒的传播
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-01 DOI: 10.1016/j.chemolab.2025.105560
Zulqurnain Sabir , Bahaa Basbous , Basma Souayeh , Muhammad Umar , Soheil Salahshour

Purpose

The purpose of this work is to provide a reliable neural network process for the spreading virus in computers with kill signals. The mathematical model shows susceptible, exposed, infected individuals to form the virus inactive, and kill signals classes.

Method

A structure of deep neural network (DNN) is designed by using two different hidden layers having radial basis activation functions in both layers, optimization through the Bayesian regularization, twenty and thirty numbers of neurons in primary and secondary hidden layers for the spreading virus in computers with kill signals. The stochastic DNN framework is presented to solve the spreading virus in computers with kill signals by selecting the data for training as 70 %, and 15 %, 15 % for both validation and testing.

Results

The accuracy of the scheme is observed through the overlapping of the solutions along with negligible absolute error for solving the model. The consistency of the solver is observed through the process of error histogram, regression, and state transition.

Novelty

The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.
目的为具有杀伤信号的计算机中病毒的传播提供一种可靠的神经网络过程。该数学模型显示了易感、暴露、感染个体形成的病毒灭活和杀伤信号等级。方法采用两层具有径向基激活函数的不同隐层设计深度神经网络(DNN)结构,通过贝叶斯正则化优化,在主隐层和次隐层分别设置20和30个神经元,用于在具有杀伤信号的计算机中传播病毒。提出了随机深度神经网络框架,通过选择训练数据为70%,验证数据为15%,测试数据为15%,来解决具有杀死信号的计算机中病毒的传播问题。结果通过解的重叠观察到该方案的精度,求解模型的绝对误差可以忽略不计。通过误差直方图、回归和状态转移的过程来观察求解器的一致性。新颖提出的具有径向基激活函数的深度神经网络结构尚未应用于具有杀伤信号的计算机中病毒的传播。
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引用次数: 0
Decentralized federated learning enables privacy-preserving NIR spectroscopy calibration: A proof-of-concept study 分散的联邦学习使保护隐私的近红外光谱校准成为可能:概念验证研究
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-18 DOI: 10.1016/j.chemolab.2025.105583
Yuan-yuan Chen
Near-infrared (NIR) spectroscopy is a key analytical tool across industries, providing fast, non-destructive measurements. However, traditional centralized models face key challenges regarding data privacy, instrument heterogeneity, and limited inter-institutional collaboration. We present a decentralized federated learning (DFL) system for NIR spectroscopy that enables institutions to collaboratively train accurate models without sharing raw data. The proposed system combines standardized spectral preprocessing with lightweight communication protocols to achieve modeling efficiency and data confidentiality. Extensive experiments were conducted on augmented Corn and Gasoline datasets using PLSR, SVR, and 1D-CNN models. In our simulations, we modeled a network of 30 clients communicating via a ring topology and applied FedProx regularization (μ = 0.1). The proposed DFL system produces predictions within 5–8 % of centralized results, while its architecture inherently offers improved scalability, fault tolerance, and privacy protection. The combination of FedProx and model personalization preserves training stability under non-IID data conditions, recovering 20 % of lost accuracy. In cross-instrument scenarios, the DFL approach outperforms both local-only and standard centralized FL models, reducing prediction errors by up to 52 % and showing strong generalization to new devices. While DFL requires more training rounds, system efficiency analysis shows its total communication cost is 25 % lower than centralized FL. Our research indicates DFL as a promising and practical approach for NIR spectroscopy, offering privacy, scalability, and generalizability for real-world, multi-party deployments with heterogeneous devices. However, performance can decline under extreme data heterogeneity, highlighting the need for further enhancements in model personalization.
近红外(NIR)光谱是跨行业的关键分析工具,提供快速,非破坏性的测量。然而,传统的集中式模型面临着数据隐私、工具异质性和有限的机构间协作方面的关键挑战。我们提出了一种用于近红外光谱的分散联邦学习(DFL)系统,该系统使机构能够在不共享原始数据的情况下协作训练准确的模型。该系统将标准化的频谱预处理与轻量级通信协议相结合,以实现建模效率和数据保密性。使用PLSR、SVR和1D-CNN模型对增强的玉米和汽油数据集进行了广泛的实验。在我们的模拟中,我们模拟了一个由30个客户端通过环形拓扑进行通信的网络,并应用FedProx正则化(μ = 0.1)。所提出的DFL系统在集中结果的5 - 8%内产生预测,而其架构固有地提供了改进的可伸缩性、容错性和隐私保护。FedProx和模型个性化的结合在非iid数据条件下保持了训练的稳定性,恢复了20%的准确度损失。在跨仪器场景中,DFL方法优于局部和标准集中式FL模型,将预测误差降低了52%,并显示出对新设备的强泛化。虽然DFL需要更多的训练轮次,但系统效率分析表明,它的总通信成本比集中式FL低25%。我们的研究表明,DFL是一种有前途和实用的近红外光谱方法,为现实世界中使用异构设备的多方部署提供隐私、可扩展性和通用性。然而,在极端的数据异构情况下,性能可能会下降,这突出了进一步增强模型个性化的必要性。
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引用次数: 0
Machine learning and evolutionary computation on e-nose datasets: A preliminary approach to ergot alkaloid detection in wheat 基于电子鼻数据集的机器学习和进化计算:小麦麦角生物碱检测的初步方法
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-13 DOI: 10.1016/j.chemolab.2025.105574
Chiara Giliberti , Giulia Magnani , Monica Mattarozzi , Marco Giannetto , Federica Bianchi , Maria Careri , Stefano Cagnoni
To the best of the authors' knowledge, this is the first time that an approach based on the use of machine learning (ML) algorithms combined with genetic programming (GP) was used to process small-sample-size e-nose data. The approach was proposed to classify the volatile compound information of wheat samples based on the contamination of ergot alkaloids, a class of emerging mycotoxins which pose a severe threat to food safety and consumer health. Unlike previous studies that applied convolutional neural networks to full e-nose response profiles, our approach focused on a small set of features extracted from the steady-state region of each response curve. Despite the low dimensionality, using GP to generate optimal features significantly improved the classification performance of several ML models. Different classifiers, including Decision Tree, Linear Discriminant Analysis, the Mahalanobis Distance Classifier, an artificial neural network-based method and ensemble methods were assessed and applied to a dataset of 21 wheat samples. These samples were classified according to their compliance with the EU maximum limit of 150 μg/kg for ergot alkaloids in wheat. The combined application of GP-based feature transformations, specifically using M3GP, and ML classifiers resulted in significant improvements in accuracy, F1 score, precision and recall compared to models trained on untransformed features. These findings highlight the unexplored potential of GP as a powerful tool for feature construction in sensor-based classification tasks for food safety signal processing.
据作者所知,这是第一次使用基于机器学习(ML)算法结合遗传编程(GP)的方法来处理小样本电子鼻数据。麦角生物碱是一类严重威胁食品安全和消费者健康的新型真菌毒素,提出了基于麦角生物碱污染对小麦样品挥发性化合物信息进行分类的方法。与之前将卷积神经网络应用于完整电子鼻响应剖面的研究不同,我们的方法侧重于从每个响应曲线的稳态区域提取的一小部分特征。尽管维数较低,但使用GP生成最优特征显著提高了几种ML模型的分类性能。采用决策树、线性判别分析、马氏距离分类器、基于人工神经网络的方法和集成方法对21个小麦样本数据集进行了评估和应用。这些样品符合欧盟对小麦中麦角生物碱的最高限量150 μg/kg进行分类。与未转换特征训练的模型相比,基于gp的特征转换(特别是使用M3GP)和ML分类器的组合应用在准确性、F1分数、精度和召回率方面都有显著提高。这些发现突出了GP作为基于传感器的食品安全信号处理分类任务中特征构建的强大工具的潜力。
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引用次数: 0
Advancing molecular property prediction for environmental fate using graph neural networks: A comparative analysis 利用图神经网络推进环境命运的分子性质预测:比较分析
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-19 DOI: 10.1016/j.chemolab.2025.105581
Pravinkumar M. Sonsare , Roshni Khedgaonkar , Kavita Singh , Pratik Agrawal
In cheminformatics, predicting molecular properties is crucial for enhancing material research, toxicity assessment and drug discovery. This research investigates the use of Graph Neural Networks (GNNs) for predicting molecular properties by examining three different architectures: Graph Isomorphism Network (GIN), Equivariant Graph Neural Network (EGNN) and Graphormer. We show that GNNs that include structural and geometric information perform noticeably better than conventional descriptor-based machine learning models using benchmark datasets like as QM9, ZINC, and OGB-MolHIV. This study further plays a pivotal role in understanding the environmental fate and transport of chemical compounds by find partition coefficients like Octanol-Water Partition Coefficient (Kow), Air-Water Partition Coefficient (Kaw) and Soil-Water Partition Coefficient (K_d). Graphormer achieves the best performance on log Kow (MAE = 0.18) and MolHIV classification (ROC-AUC = 0.807). EGNN with its E(n)-equivariant updates and 3D coordinate integration achieves the lowest mean absolute error (MAE) on geometry-sensitive properties like log Kaw (0.25) and log K_d (0.22). The significance of architectural alignment with molecular property traits is underscored by these findings.
在化学信息学中,预测分子性质对加强材料研究、毒性评估和药物发现至关重要。本研究通过考察三种不同的结构:图同构网络(GIN)、等变图神经网络(EGNN)和graphhormer,探讨了图神经网络(GNNs)在预测分子性质方面的应用。我们表明,包含结构和几何信息的gnn比使用基准数据集(如QM9、ZINC和OGB-MolHIV)的传统基于描述符的机器学习模型表现得明显更好。本研究通过寻找辛醇-水分配系数(Kow)、空气-水分配系数(Kaw)和土壤-水分配系数(K_d)等分配系数,进一步了解化合物的环境命运和迁移。graphhormer在log Kow (MAE = 0.18)和MolHIV分类(ROC-AUC = 0.807)上表现最佳。EGNN具有E(n)等变更新和三维坐标积分,在几何敏感属性(如log law(0.25)和log K_d(0.22))上实现了最低的平均绝对误差(MAE)。这些发现强调了与分子特性特征的结构对齐的重要性。
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引用次数: 0
Environment aging analysis of animal bloodstains with ATR-FTIR and CNN 动物血迹的ATR-FTIR和CNN环境老化分析
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-14 DOI: 10.1016/j.chemolab.2025.105576
Chun-Ta Wei , Zexin Shen , Wenbin Luo , Jingyi Zhao , Tingting Yin , Kaining Cheng , Miao Zhang
Bloodstain analysis is a critical component of forensic science, particularly for determining the time of deposition and understanding the effects of environmental conditions on evidence. This study presents an innovative bloodstains environment aging model, which integrates attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) with a convolutional neural network (CNN) optimized using the black-winged kite algorithm. Bloodstains from common sources (pig, cow, and chicken) were analyzed under varying environmental conditions, including temperature fluctuations (0 °C, 40 °C, 100 °C) and simulated sunlight exposure, across multiple aging periods (1, 2, 4, 8 days). Spectral data obtained through ATR-FTIR scanning served as the input for the optimized CNN, enabling precise differentiation and classification of bloodstains based on aging and environmental factors. The model achieved high predictive accuracy, with 97.86 % for pig blood, 95.47 % for cow blood, and 97.15 % for chicken blood under 0 °C conditions, demonstrating its robustness and reliability in forensic applications. Additionally, this research highlights the potential for integrating spectroscopic data with advanced deep learning techniques to enhance forensic methodologies. By improving accuracy, accessibility, and cost-effectiveness, this work represents a significant advancement in bloodstain analysis and forensic science.
血迹分析是法医科学的一个重要组成部分,特别是在确定沉积时间和了解环境条件对证据的影响方面。本研究提出了一种创新的血迹环境老化模型,该模型将衰减全反射傅立叶变换红外光谱(ATR-FTIR)与使用黑翼风筝算法优化的卷积神经网络(CNN)相结合。研究人员在不同的环境条件下分析了常见来源(猪、牛和鸡)的血迹,包括温度波动(0°C、40°C、100°C)和模拟阳光照射,并在多个老化期(1、2、4、8天)进行了分析。通过ATR-FTIR扫描获得的光谱数据作为优化后的CNN的输入,可以根据年龄和环境因素对血迹进行精确的区分和分类。在0°C条件下,该模型对猪血、牛血和鸡血的预测准确率分别为97.86%、95.47%和97.15%,显示了其在法医应用中的鲁棒性和可靠性。此外,本研究强调了将光谱数据与先进的深度学习技术相结合以增强法医方法的潜力。通过提高准确性、可及性和成本效益,这项工作代表了血迹分析和法医科学的重大进步。
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引用次数: 0
Recent developments in evolutionary computation for generative adversarial networks: A comprehensive survey 生成对抗网络进化计算的最新进展:综合综述
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-20 DOI: 10.1016/j.chemolab.2025.105587
Atifa Rafique , Xue Yu , Kashif Iqbal
In recent years, evolutionary generative adversarial networks (EGANs) have been proposed as a emerging research area that merges the well-known original concept of generative adversarial networks (GAN) for generating realistic data and evolutionary computation (EC) techniques to optimize solutions by inspiration from nature. In this review paper, we delve into the synergetic relationship between EC and GAN with an emphasis on EGANs — an emerging direction that has the potential to spark a multitude of practical applications. To this end, we first introduce the key concepts of GANs and EC respectively in detail to illustrate their synergism for modeling novel data efficiently while keeping consistency with reality. Then we describe how EC techniques have been incorporated into these architectures to improve both performance and diversity. This paper presents a thorough analysis of the EGANs in various domains. In this perspective, EGANs have been proven to be very effective in various real-world problems like data scarcity as well as mode collapse and training instability. We also consider the limitations of EGANs and suggest methods for addressing them. For the future, we present new research directions for EGANs and suggest that it could potentially transform artificial intelligence (AI) as well as push forward cutting-edge applications in personalized content generation, virtual reality (VR) experiences, and medical diagnosis. In conclusion, it will provide a solid foundation for EGANs. It represents a promising trajectory for AI space due to a combination of two powerful paradigms, GAN and EC. It aims to handle the challenges which will result in enabling the new world in data synthesis and optimization.
近年来,进化生成对抗网络(EGANs)作为一个新兴的研究领域被提出,它融合了著名的生成对抗网络(GAN)的原始概念,用于生成现实数据和进化计算(EC)技术,以从自然中获得灵感来优化解决方案。在这篇综述论文中,我们深入研究了EC和GAN之间的协同关系,重点是EGANs -一个新兴的方向,有可能引发大量的实际应用。为此,我们首先分别详细介绍了gan和EC的关键概念,以说明它们在有效建模新数据同时保持与现实一致方面的协同作用。然后,我们描述了如何将EC技术集成到这些体系结构中,以提高性能和多样性。本文对各个领域的EGANs进行了深入的分析。从这个角度来看,EGANs已经被证明在数据稀缺、模式崩溃和训练不稳定等各种现实问题中非常有效。我们还考虑了EGANs的局限性,并提出了解决这些问题的方法。展望未来,我们提出了EGANs的新研究方向,并建议它可能会改变人工智能(AI),并推动个性化内容生成,虚拟现实(VR)体验和医疗诊断方面的前沿应用。总之,它将为EGANs提供坚实的基础。由于GAN和EC这两种强大的范式的结合,它代表了人工智能领域的一个有希望的发展轨迹。它旨在应对挑战,这些挑战将导致数据合成和优化的新世界。
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引用次数: 0
Advanced hyperparameter optimization for lung cancer detection using DenseBeetle network 基于DenseBeetle网络的肺癌检测高级超参数优化
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-18 DOI: 10.1016/j.chemolab.2025.105584
Jyoti Kumari , Sapna Sinha , Laxman Singh
Lung cancer remains a leading cause of cancer-related mortality, underscoring the urgent need for accurate and early detection to improve patient outcomes. However, current detection systems often struggle with issues like elevated false-positive rates and insufficient feature extraction. These challenges largely stem from the visual resemblance between nodules and nearby tissues, as well as the inability of conventional models to effectively capture the complex features of pulmonary nodules. This research presents a deep learning-based approach for identifying lung nodules in CT images. The framework incorporates advanced preprocessing steps such as Gaussian filtering and Contrast Limited Adaptive Histogram Equalization to enhance image sharpness and overall visual quality. A Residual Pyramid Attention-Enhanced DenseNet201, integrated with SE and CBAM modules, is used for effective feature extraction, while a sigmoid function supports binary classification. Hyperparameter tuning is performed using a novel optimizer based on Latin Hypercube Sampling and Mean Differential Variation. Evaluated on LUNA16 dataset with 888 CT scans, the model reached 98.7 % accuracy, 99.2 % sensitivity, and a 95.38 % F1-score on the test set. The framework significantly reduces false positives and demonstrates strong generalization for clinical lung cancer identification.
肺癌仍然是癌症相关死亡的主要原因,强调迫切需要准确和早期发现以改善患者的预后。然而,目前的检测系统经常面临假阳性率升高和特征提取不足等问题。这些挑战主要源于结节和附近组织之间的视觉相似性,以及传统模型无法有效捕获肺结节的复杂特征。本研究提出了一种基于深度学习的方法来识别CT图像中的肺结节。该框架结合了先进的预处理步骤,如高斯滤波和对比度有限的自适应直方图均衡化,以增强图像清晰度和整体视觉质量。残差金字塔注意力增强的DenseNet201集成了SE和CBAM模块,用于有效的特征提取,而sigmoid函数支持二元分类。采用基于拉丁超立方采样和均值微分变异的优化器进行超参数调优。在LUNA16数据集上对888次CT扫描进行评估,该模型在测试集上达到98.7%的准确率,99.2%的灵敏度和95.38%的f1得分。该框架显著减少假阳性,对临床肺癌鉴定具有很强的通用性。
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引用次数: 0
A classification model for early detection of breast cancer by Raman spectroscopy based on categorical embedding transformer 基于分类嵌入变压器的乳腺癌早期检测拉曼光谱分类模型
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-24 DOI: 10.1016/j.chemolab.2025.105589
Chaoyuan Hou , Fei Xie , Guohua Wu , Wenting Yu , Houpu Yang , Liu Yang , Xuewen Long , Longfei Yin , Shu Wang
At present, Raman spectroscopy combined with deep learning has been widely used in the field of disease screening. Transformer is an important architecture for deep learning and has excelled in several areas with technologies such as its self-attention mechanism. However, as an architecture originally designed for the field of natural language processing, Transformer has disadvantages such as high computational complexity and easy overfitting in small data sets when processing spectral data. In this study, we propose a spectral classification model called Categorical Embedding Transformer (CET) and apply it to the screening of breast cancer and ductal carcinoma in situ combined with Raman spectroscopy. The core principle of CET model is to embed class labels to fixed dimensional vectors and update them as learnable parameters during training. The CET model also removes the positional encoding in transformer encoder and the initial linear layer used for dimensionality reduction or dimensionality enhancement, and retains the structure used for feature extraction and dimensionality reduction of spectral data. The ability of feature extraction and dimensionality reduction of spectral data is retained while the computational complexity is reduced. Finally, the dot product is used to calculate the similarity between the class vector and the spectrum after dimensionality reduction, and the cross entropy loss function is used to maximize the dot product similarity of the real class during training. The model we built achieved 100 % accuracy on the validation set and 98.2 % accuracy on the unknown test set, which is better than other compared models.
目前,拉曼光谱与深度学习相结合已广泛应用于疾病筛查领域。Transformer是一种重要的深度学习架构,在一些领域表现出色,比如它的自关注机制。然而,作为一种最初为自然语言处理领域设计的架构,Transformer在处理光谱数据时存在计算复杂度高、小数据集容易过拟合等缺点。在本研究中,我们提出了一种称为分类嵌入变压器(CET)的光谱分类模型,并结合拉曼光谱将其应用于乳腺癌和导管原位癌的筛查。CET模型的核心原理是将类标签嵌入到固定维度的向量中,并在训练过程中更新为可学习的参数。CET模型还去掉了变压器编码器中的位置编码和用于降维或增强的初始线性层,保留了用于光谱数据特征提取和降维的结构。在降低计算复杂度的同时,保留了光谱数据的特征提取和降维能力。最后,利用点积计算降维后的类向量与谱的相似度,并利用交叉熵损失函数在训练过程中最大化真实类的点积相似度。我们建立的模型在验证集上的准确率达到100%,在未知测试集上的准确率达到98.2%,优于其他比较模型。
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引用次数: 0
A combination of gas detection system and adaptive deep learning network (GFC-Net) to identify different production batches of beer 结合气体检测系统和自适应深度学习网络(GFC-Net)来识别不同生产批次的啤酒
IF 3.8 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS Pub Date : 2026-01-15 Epub Date: 2025-11-04 DOI: 10.1016/j.chemolab.2025.105557
Junliang Han , Feifei Tong , Chuansheng Tang , Titi Liu
Even for products of the same brand, the quality of beer may vary across different production batches. Strict quality testing is essential to ensure product consistency, safety, and consumer satisfaction. In this work, an e-nose system, combined with the proposed deep learning algorithm, achieves the qualitative identification of beers from different production batches. First, the e-nose system is applied to acquire the gas information of beers from different production batches. Then, to comprehensively extract features characterizing the gas information, a fusion computational module that integrates local and global features from convolution and self-attention mechanism is proposed, called the Gas Features Calculation Module (GFCM). Finally, a Gas Features Classification Network (GFC-Net) is designed to enable the adaptive identification of beers from different production batches. Through structural optimization, ablation experiments, and comparison with state-of-the-art gas classification methods, GFC-Net achieves an accuracy of 98.50 %, a precision of 98.70 %, and a recall of 98.58 %. The integration of gas information that characterizes the overall chemical quality, along with GFC-Net, enables the qualitative identification of beers from different batches, providing an effective approach for quality monitoring.
即使是同一品牌的产品,不同批次的啤酒质量也会有所不同。严格的质量检测对于确保产品的一致性、安全性和消费者满意度至关重要。在这项工作中,电子鼻系统结合所提出的深度学习算法,实现了不同生产批次啤酒的定性识别。首先,利用电子鼻系统采集不同生产批次啤酒的气体信息。然后,为了全面提取表征气体信息的特征,提出了一种融合卷积和自关注机制的局部特征和全局特征的融合计算模块,称为气体特征计算模块(GFCM)。最后,设计了气体特征分类网络(GFC-Net),实现了不同生产批次啤酒的自适应识别。通过结构优化、烧蚀实验以及与现有气体分类方法的比较,GFC-Net的准确率为98.50%,精密度为98.70%,召回率为98.58%。整合表征整体化学质量的气体信息,以及GFC-Net,可以对不同批次的啤酒进行定性鉴定,为质量监测提供了有效的方法。
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引用次数: 0
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Chemometrics and Intelligent Laboratory Systems
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