Chemometrics and Intelligent Laboratory Systems最新文献_第7页

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

Chemometrics and Intelligent Laboratory 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

{"title":"Instrumental prediction of in vivo sensory properties of emollients to allow the development of new biobased ingredients","authors":"Floriane Rischard , Amandine Flourat , Ecaterina Gore , Géraldine Savary","doi":"10.1016/j.chemolab.2025.105559","DOIUrl":"10.1016/j.chemolab.2025.105559","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105559"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"A reliable deep neural network using the radial basis for the spreading virus in computers with kill signals","authors":"Zulqurnain Sabir , Bahaa Basbous , Basma Souayeh , Muhammad Umar , Soheil Salahshour","doi":"10.1016/j.chemolab.2025.105560","DOIUrl":"10.1016/j.chemolab.2025.105560","url":null,"abstract":"<div><h3>Purpose</h3><div>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.</div></div><div><h3>Method</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Novelty</h3><div>The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105560"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory Systems Pub Date : 2026-01-15 Epub Date: 2025-11-18 DOI: 10.1016/j.chemolab.2025.105583

Yuan-yuan Chen

{"title":"Decentralized federated learning enables privacy-preserving NIR spectroscopy calibration: A proof-of-concept study","authors":"Yuan-yuan Chen","doi":"10.1016/j.chemolab.2025.105583","DOIUrl":"10.1016/j.chemolab.2025.105583","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105583"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"Machine learning and evolutionary computation on e-nose datasets: A preliminary approach to ergot alkaloid detection in wheat","authors":"Chiara Giliberti , Giulia Magnani , Monica Mattarozzi , Marco Giannetto , Federica Bianchi , Maria Careri , Stefano Cagnoni","doi":"10.1016/j.chemolab.2025.105574","DOIUrl":"10.1016/j.chemolab.2025.105574","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105574"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"Advancing molecular property prediction for environmental fate using graph neural networks: A comparative analysis","authors":"Pravinkumar M. Sonsare , Roshni Khedgaonkar , Kavita Singh , Pratik Agrawal","doi":"10.1016/j.chemolab.2025.105581","DOIUrl":"10.1016/j.chemolab.2025.105581","url":null,"abstract":"<div><div>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 (K<sub>o</sub>w), Air-Water Partition Coefficient (K<sub>a</sub>w) and Soil-Water Partition Coefficient (K_d). Graphormer achieves the best performance on log K<sub>o</sub>w (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 K<sub>a</sub>w (0.25) and log K_d (0.22). The significance of architectural alignment with molecular property traits is underscored by these findings.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105581"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"Environment aging analysis of animal bloodstains with ATR-FTIR and CNN","authors":"Chun-Ta Wei , Zexin Shen , Wenbin Luo , Jingyi Zhao , Tingting Yin , Kaining Cheng , Miao Zhang","doi":"10.1016/j.chemolab.2025.105576","DOIUrl":"10.1016/j.chemolab.2025.105576","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105576"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory Systems Pub Date : 2026-01-15 Epub Date: 2025-11-20 DOI: 10.1016/j.chemolab.2025.105587

Atifa Rafique , Xue Yu , Kashif Iqbal

{"title":"Recent developments in evolutionary computation for generative adversarial networks: A comprehensive survey","authors":"Atifa Rafique , Xue Yu , Kashif Iqbal","doi":"10.1016/j.chemolab.2025.105587","DOIUrl":"10.1016/j.chemolab.2025.105587","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105587"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory Systems Pub Date : 2026-01-15 Epub Date: 2025-11-18 DOI: 10.1016/j.chemolab.2025.105584

Jyoti Kumari , Sapna Sinha , Laxman Singh

{"title":"Advanced hyperparameter optimization for lung cancer detection using DenseBeetle network","authors":"Jyoti Kumari , Sapna Sinha , Laxman Singh","doi":"10.1016/j.chemolab.2025.105584","DOIUrl":"10.1016/j.chemolab.2025.105584","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105584"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"A classification model for early detection of breast cancer by Raman spectroscopy based on categorical embedding transformer","authors":"Chaoyuan Hou , Fei Xie , Guohua Wu , Wenting Yu , Houpu Yang , Liu Yang , Xuewen Long , Longfei Yin , Shu Wang","doi":"10.1016/j.chemolab.2025.105589","DOIUrl":"10.1016/j.chemolab.2025.105589","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105589"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Chemometrics and Intelligent Laboratory 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

{"title":"A combination of gas detection system and adaptive deep learning network (GFC-Net) to identify different production batches of beer","authors":"Junliang Han , Feifei Tong , Chuansheng Tang , Titi Liu","doi":"10.1016/j.chemolab.2025.105557","DOIUrl":"10.1016/j.chemolab.2025.105557","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"268 ","pages":"Article 105557"},"PeriodicalIF":3.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145464515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0