Pub Date : 2025-07-01Epub Date: 2025-06-09DOI: 10.1016/j.vibspec.2025.103826
Yanqing Xie , Qiang Xi , Xiangli Han , Zheng Li , Gang Li , Haixia Wang , Ming Liu , Jing Zhao
Near infrared (NIR) spectroscopy is promising for fruit quality assessment but faces robustness challenges in damage detection, as surface reflectance alone cannot fully characterize internal and external damage features. To overcome this limitation, we propose combining NIR spectroscopy with multi-position light scattering information to improve the accuracy of non-destructive jujube damage grading. The Huping jujube was impacted and the damaged jujube was taken as the sample. The NIR spectra of three kinds of samples with different damage grades are collected. With the damage degree as the reference index, five machine learning algorithms of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Radial Basis Function network(RBF), and Long Short-Term Memory (LSTM) are combined to construct the damage degree identification model of single-position spectral and multi-position detection data fusion. The test set accuracy of the optimal multi-position spectral modeling (MPSM) method is 100.00 %. Compared with the single-position spectral modeling (SPSM) method, the stability of the MPSM fusion method is significantly improved, and the accuracy rate is increased by more than 13.89 %. This study established a reliable non-destructive detection method for subtle fruit damage, demonstrating the effectiveness of multi-position spectral fusion in capturing sub-surface damage and providing a transferable framework applicable to other bruise-prone delicate fruits.
{"title":"A feasibility study on improving the non-destructive detection accuracy of Huping jujube (Ziziphus jujuba Mill. cv. Huping) damage degree using near infrared spectroscopy","authors":"Yanqing Xie , Qiang Xi , Xiangli Han , Zheng Li , Gang Li , Haixia Wang , Ming Liu , Jing Zhao","doi":"10.1016/j.vibspec.2025.103826","DOIUrl":"10.1016/j.vibspec.2025.103826","url":null,"abstract":"<div><div>Near infrared (NIR) spectroscopy is promising for fruit quality assessment but faces robustness challenges in damage detection, as surface reflectance alone cannot fully characterize internal and external damage features. To overcome this limitation, we propose combining NIR spectroscopy with multi-position light scattering information to improve the accuracy of non-destructive jujube damage grading. The Huping jujube was impacted and the damaged jujube was taken as the sample. The NIR spectra of three kinds of samples with different damage grades are collected. With the damage degree as the reference index, five machine learning algorithms of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Radial Basis Function network(RBF), and Long Short-Term Memory (LSTM) are combined to construct the damage degree identification model of single-position spectral and multi-position detection data fusion. The test set accuracy of the optimal multi-position spectral modeling (MPSM) method is 100.00 %. Compared with the single-position spectral modeling (SPSM) method, the stability of the MPSM fusion method is significantly improved, and the accuracy rate is increased by more than 13.89 %. This study established a reliable non-destructive detection method for subtle fruit damage, demonstrating the effectiveness of multi-position spectral fusion in capturing sub-surface damage and providing a transferable framework applicable to other bruise-prone delicate fruits.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"139 ","pages":"Article 103826"},"PeriodicalIF":2.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270844","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}
Pub Date : 2025-07-01Epub Date: 2025-05-28DOI: 10.1016/j.vibspec.2025.103816
Seung-Hyun Im , Mohammad Akbar Faqeerzada , Byoung-Kwan Cho , Geonwoo Kim , Hoonsoo Lee
Soil volumetric water content (SVWC) is a critical factor in plant health, influencing water uptake, nutrient transport, and overall physiological performance. Adverse environmental conditions like drought and high temperatures challenge crop growth and reduce yields. Accurate monitoring of SVWC is essential for optimizing growing conditions, preventing water stress, and promoting sustainable agriculture. This study explores a non-destructive method for predicting SVWC in Chinese cabbage seedlings using short-wave infrared (SWIR, 894–2504 nm) hyperspectral imaging coupled with machine learning. Daily hyperspectral images and corresponding SVWC measurements were collected over three days following irrigation cessation, resulting in a dataset of 2700 spectra. Gaussian process regression (GPR) and support vector regression (SVR) models were applied, with Lasso and Ridge regression used for feature selection. The models were evaluated using all spectral bands (E164) and 30 selected bands (L30 and R30). The GPR model with Lasso-selected bands and smoothing preprocessing achieved the highest accuracy (R² = 0.87, RMSE = 1.33). The SVR model with smoothing preprocessing and the entire spectral range demonstrated R² = 0.82 and RMSE = 1.52. Multivariate regression models using 14 shared bands selected by Lasso and Ridge regression yielded moderate performance (R² = 0.67, RMSE = 2.07). These findings highlight the potential of hyperspectral imaging combined with machine learning for non-destructive SVWC prediction, enabling early crop detection of water stress.
{"title":"Optimized feature selection and machine learning for non-destructive estimation of soil volumetric water content in Chinese cabbage using hyperspectral imaging","authors":"Seung-Hyun Im , Mohammad Akbar Faqeerzada , Byoung-Kwan Cho , Geonwoo Kim , Hoonsoo Lee","doi":"10.1016/j.vibspec.2025.103816","DOIUrl":"10.1016/j.vibspec.2025.103816","url":null,"abstract":"<div><div>Soil volumetric water content (SVWC) is a critical factor in plant health, influencing water uptake, nutrient transport, and overall physiological performance. Adverse environmental conditions like drought and high temperatures challenge crop growth and reduce yields. Accurate monitoring of SVWC is essential for optimizing growing conditions, preventing water stress, and promoting sustainable agriculture. This study explores a non-destructive method for predicting SVWC in Chinese cabbage seedlings using short-wave infrared (SWIR, 894–2504 nm) hyperspectral imaging coupled with machine learning. Daily hyperspectral images and corresponding SVWC measurements were collected over three days following irrigation cessation, resulting in a dataset of 2700 spectra. Gaussian process regression (GPR) and support vector regression (SVR) models were applied, with Lasso and Ridge regression used for feature selection. The models were evaluated using all spectral bands (E164) and 30 selected bands (L30 and R30). The GPR model with Lasso-selected bands and smoothing preprocessing achieved the highest accuracy (R² = 0.87, RMSE = 1.33). The SVR model with smoothing preprocessing and the entire spectral range demonstrated R² = 0.82 and RMSE = 1.52. Multivariate regression models using 14 shared bands selected by Lasso and Ridge regression yielded moderate performance (R² = 0.67, RMSE = 2.07). These findings highlight the potential of hyperspectral imaging combined with machine learning for non-destructive SVWC prediction, enabling early crop detection of water stress.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"139 ","pages":"Article 103816"},"PeriodicalIF":2.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190377","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}
Pub Date : 2025-05-01Epub Date: 2025-03-23DOI: 10.1016/j.vibspec.2025.103800
Mingyan Deng , Xinggong Liang , Wanqing Zhang , Shiyang Xie , Shuo Wu , Gengwang Hu , Jianliang Luo , Hao Wu , Zhengyang Zhu , Run Chen , Qinru Sun , Gongji Wang , Zhenyuan Wang
Due to the lack of simple, accurate, and reliable methods, the determination of PMI remains one of the most challenging tasks in forensic pathology, particularly during advanced stages of decomposition. Although numerous methods have been developed for PMI estimation, most are based on animal studies, and the extrapolation of these results to humans remains limited and questionable, providing limited practical utility. To address this gap, we collected a substantial number of human samples and focused on skin tissue, which shows significant potential but has been less extensively studied. ATR-FTIR spectroscopy combined with multiple machine learning algorithms was employed to monitor the spectral changes of skin at different PMI groups. Various algorithms (PLS-R, CLR, PCR, MLR, SVR, XGB-R, and ANN) were utilized to predict PMI. The results demonstrated that the chemical changes in lipids and proteins within postmortem skin tissue exhibited a strong time-dependent pattern. The intensity of lipid absorption peaks in fresh skin tissue was significantly higher than that in decomposed tissue, whereas amide I and II bands demonstrated the opposite trend, initially increasing and subsequently decreasing, which played a crucial role in distinguishing different time points and estimating PMI. The SVR model yielded highly satisfactory results, with the actual PMI showing close alignment with the predicted PMI. The R²CV reached 0.92, while the R²P achieved 0.96, with the RMSE as low as 2.0 days. The RMSEP/RMSECV value of 0.77 indicates the model's strong stability. These findings demonstrate that ATR-FTIR spectroscopy combined with machine learning holds significant potential and practical applicability for PMI estimation in actual forensic cases. This approach not only addresses the research gap in PMI estimation based on human skin samples but also establishes a new research direction in this field.
{"title":"A novel perspective of ATR-FTIR spectroscopy combined with multiple machine learning methods for postmortem interval (PMI) human skin","authors":"Mingyan Deng , Xinggong Liang , Wanqing Zhang , Shiyang Xie , Shuo Wu , Gengwang Hu , Jianliang Luo , Hao Wu , Zhengyang Zhu , Run Chen , Qinru Sun , Gongji Wang , Zhenyuan Wang","doi":"10.1016/j.vibspec.2025.103800","DOIUrl":"10.1016/j.vibspec.2025.103800","url":null,"abstract":"<div><div>Due to the lack of simple, accurate, and reliable methods, the determination of PMI remains one of the most challenging tasks in forensic pathology, particularly during advanced stages of decomposition. Although numerous methods have been developed for PMI estimation, most are based on animal studies, and the extrapolation of these results to humans remains limited and questionable, providing limited practical utility. To address this gap, we collected a substantial number of human samples and focused on skin tissue, which shows significant potential but has been less extensively studied. ATR-FTIR spectroscopy combined with multiple machine learning algorithms was employed to monitor the spectral changes of skin at different PMI groups. Various algorithms (PLS-R, CLR, PCR, MLR, SVR, XGB-R, and ANN) were utilized to predict PMI. The results demonstrated that the chemical changes in lipids and proteins within postmortem skin tissue exhibited a strong time-dependent pattern. The intensity of lipid absorption peaks in fresh skin tissue was significantly higher than that in decomposed tissue, whereas amide I and II bands demonstrated the opposite trend, initially increasing and subsequently decreasing, which played a crucial role in distinguishing different time points and estimating PMI. The SVR model yielded highly satisfactory results, with the actual PMI showing close alignment with the predicted PMI. The R²CV reached 0.92, while the R²P achieved 0.96, with the RMSE as low as 2.0 days. The RMSEP/RMSECV value of 0.77 indicates the model's strong stability. These findings demonstrate that ATR-FTIR spectroscopy combined with machine learning holds significant potential and practical applicability for PMI estimation in actual forensic cases. This approach not only addresses the research gap in PMI estimation based on human skin samples but also establishes a new research direction in this field.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103800"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724630","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}
Pub Date : 2025-05-01Epub Date: 2025-04-30DOI: 10.1016/j.vibspec.2025.103807
Chun Li , Yanan Lu , Shengzhu Fu, Yulong Guo, Zhengwei Huang, Lei Wen, Ling Jiang
With the increased awareness of food safety, rapid, accurate, and non-destructive detection of pesticide residues on fruit peels has attracted widespread attention. In this work, we utilize the attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) to directly detect multiple pesticide residues (including carbendazim, thiophanate-methyl, and thiabendazole) on the surface of the apple peels. To further improve the efficiency of detection and meet the practical application needs, a multi-task learning (MTL) model based on multi-task neural networks is introduced to perform qualitative and quantitative analysis of three pesticides, simultaneously. The optimal results in the testing set demonstrate an average accuracy of 100 % for the qualitative task, while the average R2 of 0.9415 and root mean square error (RMSE) of 2.567 μg/cm2 can be achieved in the quantitative task. The limit of detection (LOD) of carbendazim, thiophanate-methyl, and thiabendazole were determined as 7.308 μg/cm2, 1.595 μg/cm2 and 0.159 μg/cm2, respectively. Compared with the traditional single-task model, our work greatly simplifies the complexity of pesticide detection while ensuring prediction accuracy, which offers an alternative approach for further deployment and operation of the on-site system.
{"title":"Simultaneous qualitative and quantitative analyses of pesticide residues on fruit peels with ATR-FTIR spectroscopy and multi-task learning","authors":"Chun Li , Yanan Lu , Shengzhu Fu, Yulong Guo, Zhengwei Huang, Lei Wen, Ling Jiang","doi":"10.1016/j.vibspec.2025.103807","DOIUrl":"10.1016/j.vibspec.2025.103807","url":null,"abstract":"<div><div>With the increased awareness of food safety, rapid, accurate, and non-destructive detection of pesticide residues on fruit peels has attracted widespread attention. In this work, we utilize the attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) to directly detect multiple pesticide residues (including carbendazim, thiophanate-methyl, and thiabendazole) on the surface of the apple peels. To further improve the efficiency of detection and meet the practical application needs, a multi-task learning (MTL) model based on multi-task neural networks is introduced to perform qualitative and quantitative analysis of three pesticides, simultaneously. The optimal results in the testing set demonstrate an average accuracy of 100 % for the qualitative task, while the average R<sup>2</sup> of 0.9415 and root mean square error (RMSE) of 2.567 μg/cm<sup>2</sup> can be achieved in the quantitative task. The limit of detection (LOD) of carbendazim, thiophanate-methyl, and thiabendazole were determined as 7.308 μg/cm<sup>2</sup>, 1.595 μg/cm<sup>2</sup> and 0.159 μg/cm<sup>2</sup>, respectively. Compared with the traditional single-task model, our work greatly simplifies the complexity of pesticide detection while ensuring prediction accuracy, which offers an alternative approach for further deployment and operation of the on-site system.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103807"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916361","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}
Pub Date : 2025-05-01Epub Date: 2025-04-09DOI: 10.1016/j.vibspec.2025.103804
Lu Tian , Yankun Li , Mengsha Zhang
In spectral modelling analysis, multicollinearity problems among the spectral variables are prevalent, which may reduce the accuracy of the analysis result. To reduce the effect of multicollinearity between variables in classification analysis, a new strategy of variable selection named as multicollinearity reduction-based variable selection (MR-based VS) is proposed. Characteristic variables were selected based on inter-class significant difference and intra-class correlation evaluation, which reduced data multicollinearity and ensured the selected variables were more relevant to the categories. It was combined with supervised pattern recognition methods of least squares discrimination analysis (PLS-DA) and uncorrelated linear discriminant analysis (ULDA) for the identification of the red wine and olive oil from different geographical origins. The results show that compared with the full-spectrum model and the traditional successive projection algorithm (SPA) variable screening model, the MR-based VS strategy reduces the multicollinearity between variables while ensuring the maximum difference among the different classes, as a result, it obtained the superior classification results. Therefore, MR-based VS can effectively extract categorical features, eliminate redundant information, and improve model interpretability, which shows potential for enhancing the ability of the spectral qualitative analysis model in different fields.
{"title":"A variable selection method based on multicollinearity reduction for food origin traceability identification","authors":"Lu Tian , Yankun Li , Mengsha Zhang","doi":"10.1016/j.vibspec.2025.103804","DOIUrl":"10.1016/j.vibspec.2025.103804","url":null,"abstract":"<div><div>In spectral modelling analysis, multicollinearity problems among the spectral variables are prevalent, which may reduce the accuracy of the analysis result. To reduce the effect of multicollinearity between variables in classification analysis, a new strategy of variable selection named as multicollinearity reduction-based variable selection (MR-based VS) is proposed. Characteristic variables were selected based on inter-class significant difference and intra-class correlation evaluation, which reduced data multicollinearity and ensured the selected variables were more relevant to the categories. It was combined with supervised pattern recognition methods of least squares discrimination analysis (PLS-DA) and uncorrelated linear discriminant analysis (ULDA) for the identification of the red wine and olive oil from different geographical origins. The results show that compared with the full-spectrum model and the traditional successive projection algorithm (SPA) variable screening model, the MR-based VS strategy reduces the multicollinearity between variables while ensuring the maximum difference among the different classes, as a result, it obtained the superior classification results. Therefore, MR-based VS can effectively extract categorical features, eliminate redundant information, and improve model interpretability, which shows potential for enhancing the ability of the spectral qualitative analysis model in different fields.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103804"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830295","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}
Pub Date : 2025-05-01Epub Date: 2025-03-04DOI: 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":"2025-05-01","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}
Pub Date : 2025-05-01Epub Date: 2025-03-20DOI: 10.1016/j.vibspec.2025.103799
Thomas G. Mayerhöfer , Susanne Pahlow , Uwe Hübner , Jürgen Popp
Using theoretical and experimental transmission, transflection, and attenuated total reflection (ATR) spectra, we investigated how well corresponding absorbance spectra correlate with true absorbance, defined as the absorption index function multiplied by the wavenumber, using poly(methyl methacrylate) layers on CaF2, Si, and gold substrates. To improve correlation, the substrate spectrum is often subtracted from the sample spectrum. A typical example is layers on CaF2, where this approach is sufficient to establish a strong linear correlation. However, in many cases, the substrate spectrum is not a suitable reference for removing unwanted physical contributions, such as substrate-related effects. One such example is layers on Si substrates, where high reflectance causes the spectrum to be dominated by interference fringes. Instead of using the spectrum of an uncoated substrate, one must use the spectrum of a substrate with a non-absorbing layer that has the same refractive index in the transparency region between the MIR and visible spectral regions. For ATR spectra, a simple multiplicative correction based on the wavelength dependence of the penetration depth significantly increases the Pearson coefficient, though not to levels high enough for spectral recognition. To achieve higher accuracy, the Poor Man’s ATR Correction can be employed. For transflection spectra, all relatively simple methods generally fail, and only methods that ultimately determine the optical constant function show promise for success.
{"title":"Exploring correlation in infrared spectroscopy","authors":"Thomas G. Mayerhöfer , Susanne Pahlow , Uwe Hübner , Jürgen Popp","doi":"10.1016/j.vibspec.2025.103799","DOIUrl":"10.1016/j.vibspec.2025.103799","url":null,"abstract":"<div><div>Using theoretical and experimental transmission, transflection, and attenuated total reflection (ATR) spectra, we investigated how well corresponding absorbance spectra correlate with true absorbance, defined as the absorption index function multiplied by the wavenumber, using poly(methyl methacrylate) layers on CaF<sub>2</sub>, Si, and gold substrates. To improve correlation, the substrate spectrum is often subtracted from the sample spectrum. A typical example is layers on CaF<sub>2</sub>, where this approach is sufficient to establish a strong linear correlation. However, in many cases, the substrate spectrum is not a suitable reference for removing unwanted physical contributions, such as substrate-related effects. One such example is layers on Si substrates, where high reflectance causes the spectrum to be dominated by interference fringes. Instead of using the spectrum of an uncoated substrate, one must use the spectrum of a substrate with a non-absorbing layer that has the same refractive index in the transparency region between the MIR and visible spectral regions. For ATR spectra, a simple multiplicative correction based on the wavelength dependence of the penetration depth significantly increases the Pearson coefficient, though not to levels high enough for spectral recognition. To achieve higher accuracy, the Poor Man’s ATR Correction can be employed. For transflection spectra, all relatively simple methods generally fail, and only methods that ultimately determine the optical constant function show promise for success.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103799"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143687699","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}
Pub Date : 2025-05-01Epub Date: 2025-03-11DOI: 10.1016/j.vibspec.2025.103788
Michaela Klenotová , Pavel Matějka
Surface-enhanced Raman Scattering (SERS) Spectroscopy, combined with multivariate data analysis such as Principal Component Analysis (PCA), effectively detects subtle changes in complex biological samples. In this study, we applied SERS to identify subtle molecular changes in human saliva deposited on large nanostructured Ag and Au substrates, focusing on the influence of temperature variations ranging from 10°C to 45°C. The selected temperature intervals – 10°C (cooling technology), 23°C (laboratory temperature), 37°C (physiological temperature), 42°C (fever), and 45°C (extreme temperatures) – reflect real-world conditions that biological and medical samples may encounter during collection, storage, transport, and analysis. We aimed to determine whether saliva samples remain stable at these temperatures over four days or if significant changes occur. Furthermore, we investigated the reversibility of spectral alterations during thermal jumps, where samples were heated to 45°C and then cooled back to 10°C. To ensure reliability, we utilized a computer-controlled mapping stage and a thermostatic sample holder, allowing precise temperature control and repeated recordings at identical locations on the substrate. Attention was given to intensity changes of marker bands, including band ratios, such as the ratio of 1175 cm⁻¹ to 1005 cm⁻¹ bands (protein hydration marker), the ratio of 856 cm⁻¹ to 831 cm⁻¹ bands (hydrophobicity marker of the environment surrounding tyrosine), and the ratio of 1360 cm⁻¹ to 1340 cm⁻¹ bands (hydrophobicity marker of the environment surrounding tryptophan) at different temperatures. The protein hydration marker exhibited a progressive decrease with increasing temperature, indicating water loss from the protein environment. In contrast, the hydrophobicity markers for tyrosine and tryptophan residues showed an increasing trend, suggesting enhanced hydrophobicity and a temperature-dependent reorganization of the protein structure on the SERS-active surfaces. In addition to these markers, we monitored changes related to amino acid residue bands for each temperature during the stability tests and thermal cycling. The spectral changes were associated with water loss and the reorganization of molecules near the nanostructured plasmonic surface, indicating saliva's sensitivity to temperature conditions. Our findings emphasize the importance of maintaining proper storage conditions for saliva films on large-area substrates to preserve sample integrity and prevent the misinterpretation of temperature-induced spectral changes. This study contributes to best practices for SERS analysis of thermally sensitive materials, particularly biofluids, especially in the context of medical diagnostics.
{"title":"Investigating temperature-dependent spectral changes in human saliva using SERS on Ag and Au surfaces","authors":"Michaela Klenotová , Pavel Matějka","doi":"10.1016/j.vibspec.2025.103788","DOIUrl":"10.1016/j.vibspec.2025.103788","url":null,"abstract":"<div><div>Surface-enhanced Raman Scattering (SERS) Spectroscopy, combined with multivariate data analysis such as Principal Component Analysis (PCA), effectively detects subtle changes in complex biological samples. In this study, we applied SERS to identify subtle molecular changes in human saliva deposited on large nanostructured Ag and Au substrates, focusing on the influence of temperature variations ranging from 10°C to 45°C. The selected temperature intervals – 10°C (cooling technology), 23°C (laboratory temperature), 37°C (physiological temperature), 42°C (fever), and 45°C (extreme temperatures) – reflect real-world conditions that biological and medical samples may encounter during collection, storage, transport, and analysis. We aimed to determine whether saliva samples remain stable at these temperatures over four days or if significant changes occur. Furthermore, we investigated the reversibility of spectral alterations during thermal jumps, where samples were heated to 45°C and then cooled back to 10°C. To ensure reliability, we utilized a computer-controlled mapping stage and a thermostatic sample holder, allowing precise temperature control and repeated recordings at identical locations on the substrate. Attention was given to intensity changes of marker bands, including band ratios, such as the ratio of 1175 cm⁻¹ to 1005 cm⁻¹ bands (<strong>protein hydration</strong> marker), the ratio of 856 cm⁻¹ to 831 cm⁻¹ bands (<strong>hydrophobicity</strong> marker of the environment surrounding <strong>tyrosine</strong>), and the ratio of 1360 cm⁻¹ to 1340 cm⁻¹ bands (<strong>hydrophobicity</strong> marker of the environment surrounding <strong>tryptophan</strong>) at different temperatures. The protein hydration marker exhibited a progressive decrease with increasing temperature, indicating water loss from the protein environment. In contrast, the hydrophobicity markers for tyrosine and tryptophan residues showed an increasing trend, suggesting enhanced hydrophobicity and a temperature-dependent reorganization of the protein structure on the SERS-active surfaces. In addition to these markers, we monitored changes related to amino acid residue bands for each temperature during the stability tests and thermal cycling. The spectral changes were associated with water loss and the reorganization of molecules near the nanostructured plasmonic surface, indicating saliva's sensitivity to temperature conditions. Our findings emphasize the importance of maintaining proper storage conditions for saliva films on large-area substrates to preserve sample integrity and prevent the misinterpretation of temperature-induced spectral changes. This study contributes to best practices for SERS analysis of thermally sensitive materials, particularly biofluids, especially in the context of medical diagnostics.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103788"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642784","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}
Pub Date : 2025-05-01Epub Date: 2025-05-02DOI: 10.1016/j.vibspec.2025.103808
Ying Wang , Shouqi Zhang , Xiaowen Kong , Zhengren Xu , Zhiqiang Wang , Ruiting Zhang , Lin Ma , Ke Lin
Isoprenol has important applications in both biological and medical fields, and the measurement of its structure in living organisms is very important to understand the mechanism of its role at the molecular level. We synthesized five kinds of deuterated isoprenols, and optimized structure of the deuterated rotational isomers by quantum chemical calculations, and calculated and record the Raman spectra of all the deuterated isoprenols. These experimental and theoretical results suggest that the stretching vibrational spectra of individual C-D bonds in deuterated methylene and methanediyl group can be used to identify the conformers of isoprenol. This work not only analyzed the correlation between the structure of isoprenol and the spectra of this particular deuterated molecule, but also demonstrates the potential of combining this novel method with other techniques, such as X-ray diffraction, to obtain more precise molecular structures in complex environments.
{"title":"Identification of the isoprenols conformers by the Raman spectra of the deuterated compounds in the C-D stretching region","authors":"Ying Wang , Shouqi Zhang , Xiaowen Kong , Zhengren Xu , Zhiqiang Wang , Ruiting Zhang , Lin Ma , Ke Lin","doi":"10.1016/j.vibspec.2025.103808","DOIUrl":"10.1016/j.vibspec.2025.103808","url":null,"abstract":"<div><div>Isoprenol has important applications in both biological and medical fields, and the measurement of its structure in living organisms is very important to understand the mechanism of its role at the molecular level. We synthesized five kinds of deuterated isoprenols, and optimized structure of the deuterated rotational isomers by quantum chemical calculations, and calculated and record the Raman spectra of all the deuterated isoprenols. These experimental and theoretical results suggest that the stretching vibrational spectra of individual C-D bonds in deuterated methylene and methanediyl group can be used to identify the conformers of isoprenol. This work not only analyzed the correlation between the structure of isoprenol and the spectra of this particular deuterated molecule, but also demonstrates the potential of combining this novel method with other techniques, such as X-ray diffraction, to obtain more precise molecular structures in complex environments.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103808"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904232","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}
Pub Date : 2025-05-01Epub Date: 2025-04-04DOI: 10.1016/j.vibspec.2025.103802
Aftab Ansari , D. Mohanta
This work reports Raman analysis of zircon-to-scheelite partial phase conversion encountered in GdVO4 nanosystem with Eu3+ around permissible substitutional doping. To be specific, among the Raman modes featured, the A1g mode is attributed to O-V-O vibration while B2 g represents the translatory vibrational mode (∼258 cm−1) attributed to the Eu–O stretching. The intense high-frequency mode, ν2 = 880 cm–1 would describe stretching internal vibration in the tetrahedral [VO4]3- anionic group for an ideal zircon-type conformation with tetragonal symmetry. Importantly, at room temperature Raman studies of GdVO4 nanosystem, the overlap of two Raman active modes namely, A1g (scissoring) and B2g (twisting) characterize scheelite-type characteristics in the nanosystem under study. Incorporation of Eu3+ in the system resulted in enhancing the intensity of the scheelite-type characteristics due to possible localized phase transition around Eu3+ sites in the matrix. The observed scheelite-type signal enhancement and consequently partial zircon lattice to scheelite lattice conversion due to inclusion of Eu3+ doping (1–7 %) has been highlighted and analyzed emphasizing manifested modes in detail.
{"title":"Raman signature of partial zircon to scheelite-type phase conversion in GdVO4 nanosystem due to structural disordering induced by Eu3+ inclusions","authors":"Aftab Ansari , D. Mohanta","doi":"10.1016/j.vibspec.2025.103802","DOIUrl":"10.1016/j.vibspec.2025.103802","url":null,"abstract":"<div><div>This work reports Raman analysis of <em>zircon</em>-to-<em>scheelite</em> partial phase conversion encountered in GdVO<sub>4</sub> nanosystem with Eu<sup>3+</sup> around permissible substitutional doping. To be specific, among the Raman modes featured, the <em>A</em><sub>1g</sub> mode is attributed to O-V-O vibration while <em>B</em><sub>2 g</sub> represents the translatory vibrational mode (∼258 cm<sup>−1</sup>) attributed to the Eu–O stretching. The intense high-frequency mode, <em>ν</em><sub>2</sub> = 880 cm<sup>–1</sup> would describe stretching internal vibration in the tetrahedral [VO<sub>4</sub>]<sup>3-</sup> anionic group for an ideal <em>zircon-</em>type conformation with tetragonal symmetry. Importantly, at room temperature Raman studies of GdVO<sub>4</sub> nanosystem, the overlap of two Raman active modes namely, <em>A</em><sub>1g</sub> (scissoring) and <em>B</em><sub>2g</sub> (twisting) characterize <em>scheelite</em>-type characteristics in the nanosystem under study. Incorporation of Eu<sup>3+</sup> in the system resulted in enhancing the intensity of the <em>scheelite</em>-type characteristics due to possible localized phase transition around Eu<sup>3+</sup> sites in the matrix. The observed <em>scheelite</em>-type signal enhancement and consequently partial <em>zircon</em> lattice to <em>scheelite</em> lattice conversion due to inclusion of Eu<sup>3+</sup> doping (1–7 %) has been highlighted and analyzed emphasizing manifested modes in detail.</div></div>","PeriodicalId":23656,"journal":{"name":"Vibrational Spectroscopy","volume":"138 ","pages":"Article 103802"},"PeriodicalIF":2.7,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792781","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}
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