Vibrational Spectroscopy最新文献

Authentication analysis of animal fats adulteration in nail polish simulation using Raman spectroscopy coupled with chemometrics

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-06 DOI: 10.1016/j.vibspec.2025.103785

Nurrulhidayah Ahmad Fadzillah , Amal Elgharbawy , Mohammad Aizat Jamaluddin , Nur Azira Tukiran , Anjar Windarsih , Abdul Rohman , Siti Jamilah Mohd Sukri , Nurul Widad Fitri Muhammad , Anis Hamizah Hamid

Cosmetics are being used daily by many people, and their consumption is on the rise every year. These products are adulterated with cheaper alternatives to increase their profit. As more cosmetics are available in the market, the authenticity of halal cosmetics has raised much concern among Muslim consumers throughout the world. Therefore, authentication analysis of cosmetic products is urgently needed. This study was conducted to detect beef tallow (BT), chicken fat (CF), lard (LD), and mutton fat (MF) in nail polish using Raman spectrometry combined with chemometrics. Partial least square-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were successfully used to differentiate animal fats into four subclasses. In addition, partial least square (PLS) and orthogonal PLS (OPLS) regression were adequate to detect and predict the levels of BT, CF, LD, and MF in nail polish with R²> 0.990 both in calibration and validation models. The best prediction model for BT was from OPLS at the wavenumber range of 100–3200 cm⁻¹ with R²> 0.990 and RMSEC as well as RMSEP lower than 2.0 %. Meanwhile PLS model demonstrated the best model to predict CF, LD, and MF was the PLS with R²> 0.990 and RMSEC as well as RMSEP around 1–2.40 %. This study revealed the potential application of Raman spectroscopy in combination with chemometrics as an effective and efficient technique for authenticating nail polish base formulation adulterated with animal fats.

{"title":"Authentication analysis of animal fats adulteration in nail polish simulation using Raman spectroscopy coupled with chemometrics","authors":"Nurrulhidayah Ahmad Fadzillah , Amal Elgharbawy , Mohammad Aizat Jamaluddin , Nur Azira Tukiran , Anjar Windarsih , Abdul Rohman , Siti Jamilah Mohd Sukri , Nurul Widad Fitri Muhammad , Anis Hamizah Hamid","doi":"10.1016/j.vibspec.2025.103785","DOIUrl":"10.1016/j.vibspec.2025.103785","url":null,"abstract":"<div><div>Cosmetics are being used daily by many people, and their consumption is on the rise every year. These products are adulterated with cheaper alternatives to increase their profit. As more cosmetics are available in the market, the authenticity of halal cosmetics has raised much concern among Muslim consumers throughout the world. Therefore, authentication analysis of cosmetic products is urgently needed. This study was conducted to detect beef tallow (BT), chicken fat (CF), lard (LD), and mutton fat (MF) in nail polish using Raman spectrometry combined with chemometrics. Partial least square-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA) were successfully used to differentiate animal fats into four subclasses. In addition, partial least square (PLS) and orthogonal PLS (OPLS) regression were adequate to detect and predict the levels of BT, CF, LD, and MF in nail polish with R<sup>2</sup>> 0.990 both in calibration and validation models. The best prediction model for BT was from OPLS at the wavenumber range of 100–3200 cm<sup>−1</sup> with R<sup>2</sup>> 0.990 and RMSEC as well as RMSEP lower than 2.0 %. Meanwhile PLS model demonstrated the best model to predict CF, LD, and MF was the PLS with R<sup>2</sup>> 0.990 and RMSEC as well as RMSEP around 1–2.40 %. This study revealed the potential application of Raman spectroscopy in combination with chemometrics as an effective and efficient technique for authenticating nail polish base formulation adulterated with animal fats.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103785"},"PeriodicalIF":2.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143576745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Detection of Early Subtle Bruising in Strawberries Using VNIR Hyperspectral Imaging and Deep Learning

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-04 DOI: 10.1016/j.vibspec.2025.103786

Runze Feng , Xin Han , Yubin Lan , Xinyue Gou , Jingzhi Zhang , Huizheng Wang , Shuo Zhao , Fanxia Kong

Detecting early surface bruising in strawberries during postharvest storage is crucial for maintaining product quality and reducing waste. In this paper, we combined visible-near infrared hyperspectral imaging (VNIR-HSI) technology with deep learning methods to efficiently detect early surface bruising in strawberries. Specifically, we created a hyperspectral image dataset of strawberries, captured in the 454–998 nm wavelength range at five intervals: 1, 12, 24, 36, and 48 hours after applying four levels of bruising: none, slight, moderate, and severe. To address the challenges of a limited sample size and redundant hyperspectral data, we employed data augmentation and two feature wavelength extraction techniques: Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS). We then developed several classification models, including SVM, CNN, CNN-LSTM, and CNN-BiLSTM. Experimental results showed that the CNN-BiLSTM model, which used feature wavelengths selected by CARS, achieved a 97.8 % classification accuracy for detecting slight bruising 12 hours post-treatment, with an average bruised area of 24.09 ± 6.38 mm². This performance surpassed the SVM, CNN, and CNN-LSTM models by 14.7, 10.5, and 4.5 percentage points, respectively. This study effectively classified early bruising in strawberries and visualized bruised areas, demonstrating significant improvements in detection and classification of early bruising, particularly for smaller areas.

{"title":"Detection of Early Subtle Bruising in Strawberries Using VNIR Hyperspectral Imaging and Deep Learning","authors":"Runze Feng , Xin Han , Yubin Lan , Xinyue Gou , Jingzhi Zhang , Huizheng Wang , Shuo Zhao , Fanxia Kong","doi":"10.1016/j.vibspec.2025.103786","DOIUrl":"10.1016/j.vibspec.2025.103786","url":null,"abstract":"<div><div>Detecting early surface bruising in strawberries during postharvest storage is crucial for maintaining product quality and reducing waste. In this paper, we combined visible-near infrared hyperspectral imaging (VNIR-HSI) technology with deep learning methods to efficiently detect early surface bruising in strawberries. Specifically, we created a hyperspectral image dataset of strawberries, captured in the 454–998 nm wavelength range at five intervals: 1, 12, 24, 36, and 48 hours after applying four levels of bruising: none, slight, moderate, and severe. To address the challenges of a limited sample size and redundant hyperspectral data, we employed data augmentation and two feature wavelength extraction techniques: Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS). We then developed several classification models, including SVM, CNN, CNN-LSTM, and CNN-BiLSTM. Experimental results showed that the CNN-BiLSTM model, which used feature wavelengths selected by CARS, achieved a 97.8 % classification accuracy for detecting slight bruising 12 hours post-treatment, with an average bruised area of 24.09 ± 6.38 mm². This performance surpassed the SVM, CNN, and CNN-LSTM models by 14.7, 10.5, and 4.5 percentage points, respectively. This study effectively classified early bruising in strawberries and visualized bruised areas, demonstrating significant improvements in detection and classification of early bruising, particularly for smaller areas.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103786"},"PeriodicalIF":2.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Uncovering the vibrational modes of zwitterion glycine in aqueous solution

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-03-01 DOI: 10.1016/j.vibspec.2025.103783

Mark Christie , Mozhdeh Mohammadpour , Jan Sefcik , Karen Faulds , Karen Johnston

Vibrational spectroscopy is widely employed to probe and characterise chemical, biological and biomedical samples. Glycine solutions are relevant in a variety of biological and chemical systems, yet the reported experimental vibrational wavenumbers of the glycine zwitterion, which is the dominant species in aqueous solution, are inconsistent and incomplete. This study presents a procedure that obtained a complete set of vibrational frequencies for the glycine zwitterion in aqueous solution, apart from the two lowest wavenumber modes which are available from a previous THz study. Vibrational spectra were measured using IR and Raman spectroscopy, to obtain both IR and Raman-active modes for a range of different glycine solution concentrations using four different instruments. Insight from a literature survey of density functional theory calculations in implicit and explicit water was used to guide the deconvolution of the experimental spectra into vibrational modes, giving 22 out of 24 vibrational wavenumbers with a standard error of less than 3 cm⁻¹. This thorough analysis of the glycine vibrational spectra has enabled missing and erroneous wavenumbers in literature to be identified, and the systematic procedure for determining vibrational modes will pave the way for deeper quantitative analysis of glycine systems, and serve as a benchmark for computational method development.

{"title":"Uncovering the vibrational modes of zwitterion glycine in aqueous solution","authors":"Mark Christie , Mozhdeh Mohammadpour , Jan Sefcik , Karen Faulds , Karen Johnston","doi":"10.1016/j.vibspec.2025.103783","DOIUrl":"10.1016/j.vibspec.2025.103783","url":null,"abstract":"<div><div>Vibrational spectroscopy is widely employed to probe and characterise chemical, biological and biomedical samples. Glycine solutions are relevant in a variety of biological and chemical systems, yet the reported experimental vibrational wavenumbers of the glycine zwitterion, which is the dominant species in aqueous solution, are inconsistent and incomplete. This study presents a procedure that obtained a complete set of vibrational frequencies for the glycine zwitterion in aqueous solution, apart from the two lowest wavenumber modes which are available from a previous THz study. Vibrational spectra were measured using IR and Raman spectroscopy, to obtain both IR and Raman-active modes for a range of different glycine solution concentrations using four different instruments. Insight from a literature survey of density functional theory calculations in implicit and explicit water was used to guide the deconvolution of the experimental spectra into vibrational modes, giving 22 out of 24 vibrational wavenumbers with a standard error of less than 3 cm<sup>−1</sup>. This thorough analysis of the glycine vibrational spectra has enabled missing and erroneous wavenumbers in literature to be identified, and the systematic procedure for determining vibrational modes will pave the way for deeper quantitative analysis of glycine systems, and serve as a benchmark for computational method development.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103783"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An modified RamanNet model integrated with serum Raman spectroscopy for breast cancer screening

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-06 DOI: 10.1016/j.vibspec.2025.103782

Ningning Sun , Fei Xie , Longfei Yin , Houpu Yang , Guohua Wu , Shu Wang

Based on the characteristics of spectral data, Nabil Ibtehaz et al. (2023) proposed a generalized neural network architecture for Raman spectroscopy analysis, called RamanNet. This paper applies it to breast cancer screening and proposes an modified RamanNet method to optimize the classification performance of breast cancer and healthy individuals. The modified model accelerates convergence and reduces overfitting by incorporating L2 regularization, removing TripletLoss, and adjusting the learning rate. Results demonstrate that the modified RamanNet achieved a higher accuracy (96.0 ± 1.7 %) and sensitivity (96.8 ± 3.0 %) in distinguishing between breast cancer patients and healthy controls, outperforming both the 1D-CNN (accuracy: 91.8 ± 2.9 %; sensitivity: 89.3 ± 5.1 %) and the original RamanNet (accuracy: 92.5 ± 3.2 %; sensitivity: 94.6 ± 5.6 %). Furthermore, the model demonstrated enhancements in training time, convergence speed and stability, which provides a new technological approach for non-invasive and rapid breast cancer screening with great potential for clinical application.

{"title":"An modified RamanNet model integrated with serum Raman spectroscopy for breast cancer screening","authors":"Ningning Sun , Fei Xie , Longfei Yin , Houpu Yang , Guohua Wu , Shu Wang","doi":"10.1016/j.vibspec.2025.103782","DOIUrl":"10.1016/j.vibspec.2025.103782","url":null,"abstract":"<div><div>Based on the characteristics of spectral data, Nabil Ibtehaz et al. (2023) proposed a generalized neural network architecture for Raman spectroscopy analysis, called RamanNet. This paper applies it to breast cancer screening and proposes an modified RamanNet method to optimize the classification performance of breast cancer and healthy individuals. The modified model accelerates convergence and reduces overfitting by incorporating L2 regularization, removing TripletLoss, and adjusting the learning rate. Results demonstrate that the modified RamanNet achieved a higher accuracy (96.0 ± 1.7 %) and sensitivity (96.8 ± 3.0 %) in distinguishing between breast cancer patients and healthy controls, outperforming both the 1D-CNN (accuracy: 91.8 ± 2.9 %; sensitivity: 89.3 ± 5.1 %) and the original RamanNet (accuracy: 92.5 ± 3.2 %; sensitivity: 94.6 ± 5.6 %). Furthermore, the model demonstrated enhancements in training time, convergence speed and stability, which provides a new technological approach for non-invasive and rapid breast cancer screening with great potential for clinical application.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103782"},"PeriodicalIF":2.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Development of a data fusion strategy combining FT-NIR and Vis/NIR-HSI for non-destructive prediction of critical quality attributes in traditional Chinese medicine particles

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-01 DOI: 10.1016/j.vibspec.2025.103780

Ziqian Wang , Xinhao Wan , Xiaorong Luo , Ming Yang , Xuecheng Wang , Zhijian Zhong , Qing Tao , Zhenfeng Wu

This study explores the complementary capabilities of Fourier Transform Near Infrared Spectroscopy (FT-NIR) and Visible/Near Infrared Hyperspectral Imaging (Vis/NIR-HSI) in developing a data fusion strategy to predict the critical quality attributes (CQAs) of Traditional Chinese Medicine Particles (TCMP). The research emphasizes integrating these techniques into an advanced process analytical technology (PAT) platform. By leveraging the unique strengths of FT-NIR for molecular characterization and Vis/NIR-HSI for spatial quality assessment, the study evaluates multiple data fusion strategies to enhance prediction accuracy. Twenty batches of TCMP were produced using fluidized bed granulation, and their properties were characterized using FT-NIR and Vis/NIR-HSI. Comparative analysis revealed that FT-NIR outperformed Vis/NIR-HSI in standalone predictions of moisture content and particle size. Advanced fusion schemes were then developed to combine the complementary information from both spectral ranges, resulting in partial least squares (PLS) models. Among the three fusion levels evaluated, the high-level fusion strategy achieved the most accurate predictions for flowability, particle size, and moisture content. This study demonstrates that high-level fusion of FT-NIR and Vis/NIR-HSI data can significantly improve the efficiency and accuracy of CQAs prediction for TCMP. Moreover, the proposed approach facilitates rapid and non-destructive quality analysis of granular medicines, enables real-time online monitoring, and offers practical insights into advancing automated drug safety process control.

{"title":"Development of a data fusion strategy combining FT-NIR and Vis/NIR-HSI for non-destructive prediction of critical quality attributes in traditional Chinese medicine particles","authors":"Ziqian Wang , Xinhao Wan , Xiaorong Luo , Ming Yang , Xuecheng Wang , Zhijian Zhong , Qing Tao , Zhenfeng Wu","doi":"10.1016/j.vibspec.2025.103780","DOIUrl":"10.1016/j.vibspec.2025.103780","url":null,"abstract":"<div><div>This study explores the complementary capabilities of Fourier Transform Near Infrared Spectroscopy (FT-NIR) and Visible/Near Infrared Hyperspectral Imaging (Vis/NIR-HSI) in developing a data fusion strategy to predict the critical quality attributes (CQAs) of Traditional Chinese Medicine Particles (TCMP). The research emphasizes integrating these techniques into an advanced process analytical technology (PAT) platform. By leveraging the unique strengths of FT-NIR for molecular characterization and Vis/NIR-HSI for spatial quality assessment, the study evaluates multiple data fusion strategies to enhance prediction accuracy. Twenty batches of TCMP were produced using fluidized bed granulation, and their properties were characterized using FT-NIR and Vis/NIR-HSI. Comparative analysis revealed that FT-NIR outperformed Vis/NIR-HSI in standalone predictions of moisture content and particle size. Advanced fusion schemes were then developed to combine the complementary information from both spectral ranges, resulting in partial least squares (PLS) models. Among the three fusion levels evaluated, the high-level fusion strategy achieved the most accurate predictions for flowability, particle size, and moisture content. This study demonstrates that high-level fusion of FT-NIR and Vis/NIR-HSI data can significantly improve the efficiency and accuracy of CQAs prediction for TCMP. Moreover, the proposed approach facilitates rapid and non-destructive quality analysis of granular medicines, enables real-time online monitoring, and offers practical insights into advancing automated drug safety process control.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103780"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143202533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-technique analysis of the mural materials and techniques in the 5th cave of the five temple grottoes in Subei, China

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-02-01 DOI: 10.1016/j.vibspec.2025.103781

Ping Li , Biwen Shui , Bin Zhang , Yufei Liu , Zhiyuan Yin , Qiang Cui

As part of the Dunhuang grottoes, the Five Temple Grottoes are notable for their overlapping mural structures, painted over multiple dynasties, offering valuable insights into Dunhuang's cultural and artistic evolution. However, due to historical changes and both human and natural impacts, Cave 5 is in poor condition, with various mural diseases. Research on the materials and techniques used in the Five Temple Grottoes is limited. In this study, we employed polarizing microscopy, laser particle size analysis, FT-IR, XRD, and SEM-EDX to analyze the materials and techniques of the collapsed murals in Cave 5. Results showed that Cave 5 murals consist of multiple layers, including clay texture pillars and paint layers. The Northern Zhou Dynasty murals used hematite, calcite, muscovite, and talc, reflecting techniques similar to the Mogao Grottoes. The Northern Song Dynasty murals incorporated hematite, azurite, chlorite, calcite, and gypsum. Additionally, clay in the Northern Zhou Dynasty murals had smaller particle sizes but higher clay content. The use of straw fiber in the Northern Zhou Dynasty murals contrasts with the flax fiber used in the Northern Song Dynasty murals. This study aims to understand the artistic materials and technological characteristics of the murals in Cave 5 and to provide scientific support for their protection and restoration.

{"title":"Multi-technique analysis of the mural materials and techniques in the 5th cave of the five temple grottoes in Subei, China","authors":"Ping Li , Biwen Shui , Bin Zhang , Yufei Liu , Zhiyuan Yin , Qiang Cui","doi":"10.1016/j.vibspec.2025.103781","DOIUrl":"10.1016/j.vibspec.2025.103781","url":null,"abstract":"<div><div>As part of the Dunhuang grottoes, the Five Temple Grottoes are notable for their overlapping mural structures, painted over multiple dynasties, offering valuable insights into Dunhuang's cultural and artistic evolution. However, due to historical changes and both human and natural impacts, Cave 5 is in poor condition, with various mural diseases. Research on the materials and techniques used in the Five Temple Grottoes is limited. In this study, we employed polarizing microscopy, laser particle size analysis, FT-IR, XRD, and SEM-EDX to analyze the materials and techniques of the collapsed murals in Cave 5. Results showed that Cave 5 murals consist of multiple layers, including clay texture pillars and paint layers. The Northern Zhou Dynasty murals used hematite, calcite, muscovite, and talc, reflecting techniques similar to the Mogao Grottoes. The Northern Song Dynasty murals incorporated hematite, azurite, chlorite, calcite, and gypsum. Additionally, clay in the Northern Zhou Dynasty murals had smaller particle sizes but higher clay content. The use of straw fiber in the Northern Zhou Dynasty murals contrasts with the flax fiber used in the Northern Song Dynasty murals. This study aims to understand the artistic materials and technological characteristics of the murals in Cave 5 and to provide scientific support for their protection and restoration.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103781"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wine composition detection utilizing 1DCNN and the self-attention mechanism

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-15 DOI: 10.1016/j.vibspec.2025.103768

Keda Chen, Shengwei Wang, Shenghui Liu

This study proposes a one-dimensional convolutional autoencoder model that incorporates self-attention mechanisms—1DCNN-ATTENTION-SAE. This model solves the problem of unstable prediction performance in quantitative modeling of multiple components in infrared spectroscopy, especially when dealing with complex nonlinear problems involving severe overlap of characteristic peak bands and difficulty in capturing high-dimensional nonlinear features. The model effectively captures long-term dependencies in infrared spectral data and is particularly suitable for the rapid detection of key components such as pH, total phenols, total sugars, and alcohol in wine. On the ATR-FTIR dataset of dry red wine, the proposed model demonstrates robust performance, achieving a root mean square error (RMSE) of 2.017 g/L and a coefficient of determination (R²) of 0.967 g/L. The RMSE represents the average prediction error across the chemical properties measured (pH, total phenols, total sugars, and alcohol). Similarly, the R² value reflects the overall predictive accuracy of the model for these properties. Additionally, the 1DCNN-ATTENTION-SAE model was further optimized by integrating the DeepHealth algorithm, which is based on the TRANSFORMER structure, forming the hybrid DeepHealth & 1DCNN-ATTENTION-SAE feature fusion model. When applied to the near-infrared spectral dataset of open-source pharmaceuticals to predict bioactivity values, the hybrid model achieved an RMSE of 3.262 g/L and an R² of 0.914 g/L, validating its transfer learning capability in handling "cross-instrument, cross-wavelength" infrared spectral data.

{"title":"Wine composition detection utilizing 1DCNN and the self-attention mechanism","authors":"Keda Chen, Shengwei Wang, Shenghui Liu","doi":"10.1016/j.vibspec.2025.103768","DOIUrl":"10.1016/j.vibspec.2025.103768","url":null,"abstract":"<div><div>This study proposes a one-dimensional convolutional autoencoder model that incorporates self-attention mechanisms—1DCNN-ATTENTION-SAE. This model solves the problem of unstable prediction performance in quantitative modeling of multiple components in infrared spectroscopy, especially when dealing with complex nonlinear problems involving severe overlap of characteristic peak bands and difficulty in capturing high-dimensional nonlinear features. The model effectively captures long-term dependencies in infrared spectral data and is particularly suitable for the rapid detection of key components such as pH, total phenols, total sugars, and alcohol in wine. On the ATR-FTIR dataset of dry red wine, the proposed model demonstrates robust performance, achieving a root mean square error (RMSE) of 2.017 g/L and a coefficient of determination (R²) of 0.967 g/L. The RMSE represents the average prediction error across the chemical properties measured (pH, total phenols, total sugars, and alcohol). Similarly, the R² value reflects the overall predictive accuracy of the model for these properties. Additionally, the 1DCNN-ATTENTION-SAE model was further optimized by integrating the DeepHealth algorithm, which is based on the TRANSFORMER structure, forming the hybrid DeepHealth & 1DCNN-ATTENTION-SAE feature fusion model. When applied to the near-infrared spectral dataset of open-source pharmaceuticals to predict bioactivity values, the hybrid model achieved an RMSE of 3.262 g/L and an R² of 0.914 g/L, validating its transfer learning capability in handling \"cross-instrument, cross-wavelength\" infrared spectral data.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"137 ","pages":"Article 103768"},"PeriodicalIF":2.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Sputter deposited silver film as an alternative tool for Raman signal enhancement in plasma-activated water

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103765

Nilton F. Azevedo Neto , Samuel A. Marques , Felipe S. Miranda , Pedro W.P. Moreira Junior , Andre L.J. Pereira , Carlos J.L. Constantino , José H. Dias da Silva , Rodrigo S. Pessoa

Plasma-activated water (PAW), generated by non-thermal plasma, has shown great potential in various applications, including bacterial inactivation, agriculture, and disinfection, primarily attributed to the presence of reactive oxygen and nitrogen species (RONS). Traditional characterization methods for RONS in PAW often encounter limitations in sensitivity and specificity, particularly at low concentrations. In this study, we investigated the application of surface-enhanced Raman spectroscopy (SERS) for the characterization of PAW. A SERS substrate was prepared by sputter-depositing a silver (Ag) film onto a cover glass. The structural, topographic, and optical properties of the film were characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and reflectance spectroscopy. Utilizing the Ag film substrates, we observed a substantial enhancement in the Raman signals of deionized water compared to measurements on glass substrates, achieving an analytical enhancement factor (AEF) of approximately 30 for the O–H stretching band. The characterization of PAW using the SERS substrate enabled the acquisition of well-defined Raman spectra and facilitated the detection of nitrate ions (NO₃⁻) in PAW generated by a dielectric barrier discharge reactor. The results obtained from the PAW Raman spectra were further supported by changes in physicochemical properties, such as decreased pH and increased conductivity, as well as UV-Vis spectroscopy results. These findings demonstrate that sputter-deposited Ag films can serve as a valuable methodological tool for the characterization of PAW using Raman spectroscopy.

{"title":"Sputter deposited silver film as an alternative tool for Raman signal enhancement in plasma-activated water","authors":"Nilton F. Azevedo Neto , Samuel A. Marques , Felipe S. Miranda , Pedro W.P. Moreira Junior , Andre L.J. Pereira , Carlos J.L. Constantino , José H. Dias da Silva , Rodrigo S. Pessoa","doi":"10.1016/j.vibspec.2024.103765","DOIUrl":"10.1016/j.vibspec.2024.103765","url":null,"abstract":"<div><div>Plasma-activated water (PAW), generated by non-thermal plasma, has shown great potential in various applications, including bacterial inactivation, agriculture, and disinfection, primarily attributed to the presence of reactive oxygen and nitrogen species (RONS). Traditional characterization methods for RONS in PAW often encounter limitations in sensitivity and specificity, particularly at low concentrations. In this study, we investigated the application of surface-enhanced Raman spectroscopy (SERS) for the characterization of PAW. A SERS substrate was prepared by sputter-depositing a silver (Ag) film onto a cover glass. The structural, topographic, and optical properties of the film were characterized by X-ray diffraction (XRD), atomic force microscopy (AFM), and reflectance spectroscopy. Utilizing the Ag film substrates, we observed a substantial enhancement in the Raman signals of deionized water compared to measurements on glass substrates, achieving an analytical enhancement factor (AEF) of approximately 30 for the O–H stretching band. The characterization of PAW using the SERS substrate enabled the acquisition of well-defined Raman spectra and facilitated the detection of nitrate ions (NO₃⁻) in PAW generated by a dielectric barrier discharge reactor. The results obtained from the PAW Raman spectra were further supported by changes in physicochemical properties, such as decreased pH and increased conductivity, as well as UV-Vis spectroscopy results. These findings demonstrate that sputter-deposited Ag films can serve as a valuable methodological tool for the characterization of PAW using Raman spectroscopy.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"136 ","pages":"Article 103765"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Influence of dielectric environments on Raman non-coincidence effects in the CO stretching and NH2 bending modes of formamide

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2025.103767

Abduvakhid Jumabaev , Utkirjon Holikulov , Shavkatjon Yormatov , Th. Gomti Devi

This paper investigates the vibrational properties and intermolecular interactions of formamide (FA) in polar solvents with varying dielectric constants and dipole moments, including dimethyl sulfoxide (DMSO), acetonitrile (AcN), and 1,4-dioxane (DiX). Raman non-coincidence effects (NCE) in the CO stretching and NH₂ bending modes were observed to decrease monotonically in all solvents, indicating a systematic solute-solvent interaction trend. To explain such trends, experimental results were compared with the Onsager-Fröhlich dielectric continuum model, revealing strong agreement in solvents with high dielectric constants and dipole moments. Complementary DFT analyses of the Raman spectra for various FA self-associations identified a closer match with experimental results in odd-numbered molecular associations. The nature and strength of intermolecular forces in FA-solvent complexes were further investigated by topological methods (AIM, NCI, RDG), which confirmed findings from experiments. These findings advance our understanding of solute-solvent dynamics in polar environments and have broad implications for studies of intermolecular forces in chemical and biological systems.

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引用次数: 0

Application of ATR-FTIR and chemometrics for rapid lard adulteration assessment in confectionery

IF 2.7 3区化学 Q2 CHEMISTRY, ANALYTICAL

Vibrational Spectroscopy

Pub Date : 2025-01-01 DOI: 10.1016/j.vibspec.2024.103762

Sobia Kunbhar, Farah Naz Talpur, Sarfraz Ahmed Mahesar, Hassan Imran Afridi, Ghulam Fareed, Noshad Razzaque, Mehr-un Nisa

Consumption of confectionery products such as chocolates and biscuits are popular among all age groups in the population around the globe. Adulteration of confectionery fats with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy (ATR-FTIR) was developed in order to determine the lard content in imported chocolates and biscuits. For quantitative measurement, the partial least square (PLS) model with multivariate calibration for adulterant prediction was developed. A lard oil calibration graph was drawn (2–35 %) with palm oil in the region of 3035–2984 cm⁻¹, with the correlation coefficient (R²) = 0.9994 with minimum errors in root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) with the value of 0.320 and 0.315 respectively. The root mean square error of cross-validation (RMSECV) was used to find the calibration model accuracy which was found to be 1.17 with the best limit of detection (LOD) 0.10 % and limit of quantification (LOQ) 0.35 %. The developed ATR-FTIR method is robust accurate and precise in terms of lard detection in confectionery products (chocolate and biscuits).

{"title":"Application of ATR-FTIR and chemometrics for rapid lard adulteration assessment in confectionery","authors":"Sobia Kunbhar, Farah Naz Talpur, Sarfraz Ahmed Mahesar, Hassan Imran Afridi, Ghulam Fareed, Noshad Razzaque, Mehr-un Nisa","doi":"10.1016/j.vibspec.2024.103762","DOIUrl":"10.1016/j.vibspec.2024.103762","url":null,"abstract":"<div><div>Consumption of confectionery products such as chocolates and biscuits are popular among all age groups in the population around the globe. Adulteration of confectionery fats with cheaper animal fats, such as lard, has become an issue in recent years. A simple and rapid analytical method of attenuated total reflectance in Fourier transform infrared spectroscopy (ATR-FTIR) was developed in order to determine the lard content in imported chocolates and biscuits. For quantitative measurement, the partial least square (PLS) model with multivariate calibration for adulterant prediction was developed. A lard oil calibration graph was drawn (2–35 %) with palm oil in the region of 3035–2984 cm<sup>−1</sup>, with the correlation coefficient (R<sup>2</sup>) = 0.9994 with minimum errors in root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) with the value of 0.320 and 0.315 respectively. The root mean square error of cross-validation (RMSECV) was used to find the calibration model accuracy which was found to be 1.17 with the best limit of detection (LOD) 0.10 % and limit of quantification (LOQ) 0.35 %. The developed ATR-FTIR method is robust accurate and precise in terms of lard detection in confectionery products (chocolate and biscuits).</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"136 ","pages":"Article 103762"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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