Pub Date : 2026-01-24DOI: 10.1016/j.saa.2026.127520
Caixia Lyu , Yanliang Guo , Jiyuan Wang , Kun Tao , Jiawei Zhu
5-Hydroxymethylfurfural (HMF) is a potential food contaminant that poses health risks upon long-term intake. While current fluorescence detection technologies show potential applications in HMF analysis, most reported fluorescent probes rely on short-wavelength excitation and single-signal output, which suffer from background fluorescence interference in complex matrices. This study proposed a near-infrared excited multi-output sensing strategy based on upconversion nanoparticles (UCNPs) and p-toluidine-barbituric acid chromogenic system, enabling synergistic luminescent-colorimetric analysis of HMF. This system specifically reacted with HMF, selectively quenching the green upconversion luminescence of the UCNPs while leaving the red upconversion luminescence unaffected, constructing both ratiometric luminescent and colorimetric visual sensing modes. A smartphone-based sensing platform was further developed for portable and sensitive HMF detection, providing a more convenient and sensitive strategy for food safety analysis. This study provided a novel strategy for background-free, highly sensitive, and portable visual detection of food contaminants.
{"title":"Near-infrared excited dual-mode nanoprobes for background-free and on-site detection of 5-hydroxymethylfurfural in food","authors":"Caixia Lyu , Yanliang Guo , Jiyuan Wang , Kun Tao , Jiawei Zhu","doi":"10.1016/j.saa.2026.127520","DOIUrl":"10.1016/j.saa.2026.127520","url":null,"abstract":"<div><div>5-Hydroxymethylfurfural (HMF) is a potential food contaminant that poses health risks upon long-term intake. While current fluorescence detection technologies show potential applications in HMF analysis, most reported fluorescent probes rely on short-wavelength excitation and single-signal output, which suffer from background fluorescence interference in complex matrices. This study proposed a near-infrared excited multi-output sensing strategy based on upconversion nanoparticles (UCNPs) and p-toluidine-barbituric acid chromogenic system, enabling synergistic luminescent-colorimetric analysis of HMF. This system specifically reacted with HMF, selectively quenching the green upconversion luminescence of the UCNPs while leaving the red upconversion luminescence unaffected, constructing both ratiometric luminescent and colorimetric visual sensing modes. A smartphone-based sensing platform was further developed for portable and sensitive HMF detection, providing a more convenient and sensitive strategy for food safety analysis. This study provided a novel strategy for background-free, highly sensitive, and portable visual detection of food contaminants.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"352 ","pages":"Article 127520"},"PeriodicalIF":4.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076494","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}
Pub Date : 2026-01-24DOI: 10.1016/j.saa.2026.127519
Jie Gong , Xu-rong Liu , Xuan Liu , Ru Sun , Jian-Feng Ge
Ferroptosis constitutes a type of regulated cell death that is iron-dependent and orchestrated by lipid peroxidation. Central to the maintenance of redox homeostasis in this pathway are cysteine (Cys) and homocysteine (Hcy). To visualize the dynamics of these biothiols, three near-infrared (NIR) probes 1a-1c built upon a tetrahydroacridinium scaffold have been developed. These probes exhibited rapid responsiveness, high sensitivity, and excellent selectivity toward Cys/Hcy, accompanied by significant fluorescence enhancement and large Stokes shifts. Among them, 1a demonstrated superior mitochondria-targeting capability and low cytotoxicity, enabling real-time monitoring of Cys/Hcy from both internal cellular processes and external sources in live cells. Furthermore, 1a was utilized to monitor the depletion of mitochondrial Cys/Hcy during Erastin-induced ferroptosis and to evaluate the restorative effects of inhibitors ferrostatin-1 (Fer-1) and N-acetyl-l-cysteine (NAC), providing visual evidence of their distinct regulatory roles. Additionally, the probe could track the consumption of mitochondrial biothiols in an inflammatory model, underscoring its potential for studying redox imbalances in inflammatory processes.
{"title":"A rapid-response NIR fluorescent probe for imaging Cys/Hcy in ferroptosis and inflammation models","authors":"Jie Gong , Xu-rong Liu , Xuan Liu , Ru Sun , Jian-Feng Ge","doi":"10.1016/j.saa.2026.127519","DOIUrl":"10.1016/j.saa.2026.127519","url":null,"abstract":"<div><div>Ferroptosis constitutes a type of regulated cell death that is iron-dependent and orchestrated by lipid peroxidation. Central to the maintenance of redox homeostasis in this pathway are cysteine (Cys) and homocysteine (Hcy). To visualize the dynamics of these biothiols, three near-infrared (NIR) probes <strong>1a-1c</strong> built upon a tetrahydroacridinium scaffold have been developed. These probes exhibited rapid responsiveness, high sensitivity, and excellent selectivity toward Cys/Hcy, accompanied by significant fluorescence enhancement and large Stokes shifts. Among them, <strong>1a</strong> demonstrated superior mitochondria-targeting capability and low cytotoxicity, enabling real-time monitoring of Cys/Hcy from both internal cellular processes and external sources in live cells. Furthermore, <strong>1a</strong> was utilized to monitor the depletion of mitochondrial Cys/Hcy during Erastin-induced ferroptosis and to evaluate the restorative effects of inhibitors ferrostatin-1 (Fer-1) and <em>N</em>-acetyl-l-cysteine (NAC), providing visual evidence of their distinct regulatory roles. Additionally, the probe could track the consumption of mitochondrial biothiols in an inflammatory model, underscoring its potential for studying redox imbalances in inflammatory processes.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"352 ","pages":"Article 127519"},"PeriodicalIF":4.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076348","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}
Pub Date : 2026-01-23DOI: 10.1016/j.saa.2026.127504
Lele Yan , Xiaoyuan Sun , Zhuobin Shang , Zhenming Dong , Xiaoqing Yan , Yu Wang , Shaomin Shuang
As the principal organ for the metabolism and detoxification of both exogenous and endogenous substances, the liver demonstrates heightened vulnerability to the onset of hepatic pathologies. Aberrant viscosity, acting as a potential biomarker, exhibits a significant correlation with liver diseases such as fatty liver, hepatic fibrosis, and liver injury. Therefore, real-time monitoring of viscosity fluctuations in animal models of liver disease is essential for related pathological investigations. Here, we report a novel viscosity-sensitive fluorescent probe (DJXP) with NIR excitation at 736 nm and emission at 809 nm, characterized by a large Stokes shift (67 nm), a broad operational pH range, high selectivity and excellent biocompatibility. DJXP enabled the visualization of viscosity changes in ICR mice induced by the antifungal drug nystatin, as determined by fluorescence imaging analysis. Furthermore, DJXP was employed to monitor elevated viscosity in mouse models of both lipopolysaccharide (LPS)-induced inflammation and rotenone-induced acute hepatic injury, demonstrating promising potential for the non-invasive detection and diagnosis of hepatic pathologies associated with altered viscosity.
{"title":"A robust xanthylium-based near-infrared fluorescent probe for viscosity imaging in inflammatory and hepatic injury mice models","authors":"Lele Yan , Xiaoyuan Sun , Zhuobin Shang , Zhenming Dong , Xiaoqing Yan , Yu Wang , Shaomin Shuang","doi":"10.1016/j.saa.2026.127504","DOIUrl":"10.1016/j.saa.2026.127504","url":null,"abstract":"<div><div>As the principal organ for the metabolism and detoxification of both exogenous and endogenous substances, the liver demonstrates heightened vulnerability to the onset of hepatic pathologies. Aberrant viscosity, acting as a potential biomarker, exhibits a significant correlation with liver diseases such as fatty liver, hepatic fibrosis, and liver injury. Therefore, real-time monitoring of viscosity fluctuations in animal models of liver disease is essential for related pathological investigations. Here, we report a novel viscosity-sensitive fluorescent probe (<strong>DJXP</strong>) with NIR excitation at 736 nm and emission at 809 nm, characterized by a large Stokes shift (67 nm), a broad operational pH range, high selectivity and excellent biocompatibility. <strong>DJXP</strong> enabled the visualization of viscosity changes in ICR mice induced by the antifungal drug nystatin, as determined by fluorescence imaging analysis. Furthermore, DJXP was employed to monitor elevated viscosity in mouse models of both lipopolysaccharide (LPS)-induced inflammation and rotenone-induced acute hepatic injury, demonstrating promising potential for the non-invasive detection and diagnosis of hepatic pathologies associated with altered viscosity.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"352 ","pages":"Article 127504"},"PeriodicalIF":4.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076363","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}
Pub Date : 2026-01-23DOI: 10.1016/j.saa.2026.127513
Bo Peng , Haiwang Liu , Shuai Li , Yong Zhou , Mi Zhu , Bingsheng Du , Ningsheng Liao
In the 200–230 nm ultraviolet wavelength range, there is a significant overlap in the absorption spectra of sulfur dioxide (SO2), nitric oxide (NO), and ammonia (NH3). This overlap significantly complicates the detection of SO₂ and NO concentrations. This study proposes a hybrid concentration detection model that combines frequency-domain physical priors with deep learning. Firstly, by utilizing characteristic differences among the three gases in the frequency domain, a band-pass filtering layer separates the frequency band of each target gas from the mixed spectrum. Secondly, we design a parallel dual-output network structure incorporating an efficient channel attention mechanism. Multi-branch feature extraction and an attention-weighting mechanism enhance the model's ability to extract key features. Latin hypercube sampling is employed to generate diverse concentration combinations, and fine-tuning strategies effectively bridge the distribution gap between synthesized and real data. Under the interference of NH3, the detection limits of SO2 and NO are 0.25 ppm and 0.26 ppm, respectively, with corresponding uncertainties of 1.25% and 1.30%. This study provides a novel and effective technical path for solving the problem of spectral interference in multi-component gas analysis.
{"title":"A concentration detection model combining frequency-domain physical priors and deep learning: For SO2 and NO mixed gas under NH3 interference","authors":"Bo Peng , Haiwang Liu , Shuai Li , Yong Zhou , Mi Zhu , Bingsheng Du , Ningsheng Liao","doi":"10.1016/j.saa.2026.127513","DOIUrl":"10.1016/j.saa.2026.127513","url":null,"abstract":"<div><div>In the 200–230 nm ultraviolet wavelength range, there is a significant overlap in the absorption spectra of sulfur dioxide (SO<sub>2</sub>), nitric oxide (NO), and ammonia (NH<sub>3</sub>). This overlap significantly complicates the detection of SO₂ and NO concentrations. This study proposes a hybrid concentration detection model that combines frequency-domain physical priors with deep learning. Firstly, by utilizing characteristic differences among the three gases in the frequency domain, a band-pass filtering layer separates the frequency band of each target gas from the mixed spectrum. Secondly, we design a parallel dual-output network structure incorporating an efficient channel attention mechanism. Multi-branch feature extraction and an attention-weighting mechanism enhance the model's ability to extract key features. Latin hypercube sampling is employed to generate diverse concentration combinations, and fine-tuning strategies effectively bridge the distribution gap between synthesized and real data. Under the interference of NH<sub>3</sub>, the detection limits of SO<sub>2</sub> and NO are 0.25 ppm and 0.26 ppm, respectively, with corresponding uncertainties of 1.25% and 1.30%. This study provides a novel and effective technical path for solving the problem of spectral interference in multi-component gas analysis.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"352 ","pages":"Article 127513"},"PeriodicalIF":4.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045223","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}
Pub Date : 2026-01-22DOI: 10.1016/j.saa.2026.127500
Feng-Ting Liu , Bang-Zhao Zhou , Jun-Ying Miao , Bao-Xiang Zhao , Zhao-Min Lin
Mitochondria is the primary organelle responsible for energy production, and their weakly alkaline microenvironment (pH ≈ 8) is closely linked to cellular metabolism and disease states. Mitochondrial acidification is closely associated with the pathogenesis of a spectrum of disorders, encompassing neurodegenerative syndromes, cardiovascular disorders and cancer. Therefore, monitoring mitochondrial pH fluctuations is crucial for deciphering cellular physiological processes. In this research, we constructed a novel pH fluorescent probe QAA based on amide-quinoline salt, featuring piperazine as the pH recognition site and quinoline salt as the mitochondrial targeting group. QAA exhibited excellent water solubility (PBS buffer) which could be well matched with the cell imaging conditions, appropriate pKa (8.0) and mitochondrial targeting. Fluorescence spectroscopy results indicated that QAA possessed high selectivity and sensitivity, with a linear response in pH range of 6.6 to 9.5. The recognition mechanism was confirmed by density functional theory (DFT) calculations and HNMR spectral. Crucially, QAA not only exhibited excellent photostability, low cytotoxicity and the ability to detect pH in cellular mitochondria, but also could be used for real water sample detection, achieving recovery rates ranging from 98.96% to 104.84%. QAA held practical potential as a mitochondrial pH indicator for studying physiology-related processes involving mitochondria.
{"title":"A novel amide-quinoline salt-based mitochondrial-targeted fluorescent probe for detecting pH in cells and water samples","authors":"Feng-Ting Liu , Bang-Zhao Zhou , Jun-Ying Miao , Bao-Xiang Zhao , Zhao-Min Lin","doi":"10.1016/j.saa.2026.127500","DOIUrl":"10.1016/j.saa.2026.127500","url":null,"abstract":"<div><div>Mitochondria is the primary organelle responsible for energy production, and their weakly alkaline microenvironment (pH ≈ 8) is closely linked to cellular metabolism and disease states. Mitochondrial acidification is closely associated with the pathogenesis of a spectrum of disorders, encompassing neurodegenerative syndromes, cardiovascular disorders and cancer. Therefore, monitoring mitochondrial pH fluctuations is crucial for deciphering cellular physiological processes. In this research, we constructed a novel pH fluorescent probe <strong>QAA</strong> based on amide-quinoline salt, featuring piperazine as the pH recognition site and quinoline salt as the mitochondrial targeting group. <strong>QAA</strong> exhibited excellent water solubility (PBS buffer) which could be well matched with the cell imaging conditions, appropriate p<em>K</em>a (8.0) and mitochondrial targeting. Fluorescence spectroscopy results indicated that <strong>QAA</strong> possessed high selectivity and sensitivity, with a linear response in pH range of 6.6 to 9.5. The recognition mechanism was confirmed by density functional theory (DFT) calculations and HNMR spectral. Crucially, <strong>QAA</strong> not only exhibited excellent photostability, low cytotoxicity and the ability to detect pH in cellular mitochondria, but also could be used for real water sample detection, achieving recovery rates ranging from 98.96% to 104.84%. <strong>QAA</strong> held practical potential as a mitochondrial pH indicator for studying physiology-related processes involving mitochondria.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127500"},"PeriodicalIF":4.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036111","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}
Pub Date : 2026-01-22DOI: 10.1016/j.saa.2026.127506
Sughra Sarwar, Tahir Mehmood, Mudassir Iqbal
The natural characteristics of the infrared spectroscopic data are that it tends to distort the baseline, there is high-dimensionality and non-linear correlation that hinder reliable prediction of biochemical properties. To overcome these obstacles, this study introduces an integrated MARS–PLS2–Lasso framework that incorporates the effective baseline correction, non-linear regression, latent variable extraction, and sparse variable selection to promote the chemometric modeling accuracy and interpretability. Out of four baseline correction methods, viz. Asymmetric Least Squares (ALS), AirPLS, Polynomial fitting, and Wavelet baseline correction, the Wavelet method (sym8, Level 5) was found to be the most successful, in that it was able to represent local spectral variation with low-frequency noise. This technique achieved high predictive accuracy with RMSE = 0.2846–0.6857, MAE = 0.2371–0.5445 and MSE = 0.0810–0.4705 specifying both high model fit and minimal residual error across bacterial spectra. The Wavelet-corrected spectra revealed six key functional regions that contributed most significantly to bacterial differentiation: 720 cm−1 to 750 cm−1 (C–Cl stretching, CH bending), 1000 cm−1 to 1300 cm−1 (C–O stretching, esters, carboxylic acids), 1500 cm−1 to 1650 cm−1 (CC stretching), 1687 cm−1 to 1793 cm−1 (CO stretching, conjugated carbonyls), 2771 cm−1 to 3143 cm−1 (CH stretching, alkanes, alkenes), 3290 cm−1 to 3595 cm−1 (O–H and NH stretching ). Vibrational domains of interest are biochemical components of lipids, proteins, amides and polysaccharides that determine the structural integrity and metabolic activity of bacteria. The proposed MARS–PLS2–Lasso model leverages Multivariate Adaptive Regression Splines (MARS) to capture nonlinear relationships through adaptive basis functions, while Partial Least Squares (PLS2) extracts latent components that maximize covariance between spectral predictors and multiple bacterial responses. Lasso regularization adds sparsity to the model and reduces the complexity of the model, as well as penalizes less interesting basis functions, which overfit the model. Such a combination is used to provide a reasonable approximation of the parameter even in high-dimensional spectral data. In general, MARS–PLS2–Lasso provides a sound, interpretable, and chemically consistent way of high dimensional infrared spectral modeling. It is highly predictive, less noisy and has a more adequate manner of interpreting spectral–biochemical interactions, and thus, a bright way of bacteria modeling, spectral diagnostics and further use in bio-analytical spectroscopy.
{"title":"Advanced spectral modeling for bacterial strains: A MARS–PLS2 approach with Lasso regularization and baseline optimization","authors":"Sughra Sarwar, Tahir Mehmood, Mudassir Iqbal","doi":"10.1016/j.saa.2026.127506","DOIUrl":"10.1016/j.saa.2026.127506","url":null,"abstract":"<div><div>The natural characteristics of the infrared spectroscopic data are that it tends to distort the baseline, there is high-dimensionality and non-linear correlation that hinder reliable prediction of biochemical properties. To overcome these obstacles, this study introduces an integrated MARS–PLS2–Lasso framework that incorporates the effective baseline correction, non-linear regression, latent variable extraction, and sparse variable selection to promote the chemometric modeling accuracy and interpretability. Out of four baseline correction methods, viz. Asymmetric Least Squares (ALS), AirPLS, Polynomial fitting, and Wavelet baseline correction, the Wavelet method (sym8, Level 5) was found to be the most successful, in that it was able to represent local spectral variation with low-frequency noise. This technique achieved high predictive accuracy with RMSE = 0.2846–0.6857, MAE = 0.2371–0.5445 and MSE = 0.0810–0.4705 specifying both high model fit and minimal residual error across bacterial spectra. The Wavelet-corrected spectra revealed six key functional regions that contributed most significantly to bacterial differentiation: 720<!--> <!-->cm<sup>−1</sup> to 750<!--> <!-->cm<sup>−1</sup> (C–Cl stretching, C<img>H bending), 1000<!--> <!-->cm<sup>−1</sup> to 1300<!--> <!-->cm<sup>−1</sup> (C–O stretching, esters, carboxylic acids), 1500<!--> <!-->cm<sup>−1</sup> to 1650<!--> <!-->cm<sup>−1</sup> (C<img>C stretching), 1687<!--> <!-->cm<sup>−1</sup> to 1793<!--> <!-->cm<sup>−1</sup> (C<img>O stretching, conjugated carbonyls), 2771<!--> <!-->cm<sup>−1</sup> to 3143<!--> <!-->cm<sup>−1</sup> (C<img>H stretching, alkanes, alkenes), 3290<!--> <!-->cm<sup>−1</sup> to 3595<!--> <!-->cm<sup>−1</sup> (O–H and N<img>H stretching ). Vibrational domains of interest are biochemical components of lipids, proteins, amides and polysaccharides that determine the structural integrity and metabolic activity of bacteria. The proposed MARS–PLS2–Lasso model leverages Multivariate Adaptive Regression Splines (MARS) to capture nonlinear relationships through adaptive basis functions, while Partial Least Squares (PLS2) extracts latent components that maximize covariance between spectral predictors and multiple bacterial responses. Lasso regularization adds sparsity to the model and reduces the complexity of the model, as well as penalizes less interesting basis functions, which overfit the model. Such a combination is used to provide a reasonable approximation of the parameter even in high-dimensional spectral data. In general, MARS–PLS2–Lasso provides a sound, interpretable, and chemically consistent way of high dimensional infrared spectral modeling. It is highly predictive, less noisy and has a more adequate manner of interpreting spectral–biochemical interactions, and thus, a bright way of bacteria modeling, spectral diagnostics and further use in bio-analytical spectroscopy.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127506"},"PeriodicalIF":4.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036096","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}
Pub Date : 2026-01-22DOI: 10.1016/j.saa.2026.127515
Lian-Lian Duan , Wen-Jun Liu , Rui Zhai , Zhen-Guang Wang , Hong-Yuan Yan , Yun-Kai Lv PhD (Leading)
This study employed a pore-confined synthesis strategy to achieve the in situ growth of bovine serum albumin-capped copper nanoclusters (BSA-CuNCs) within the UiO-66 framework (BSA-CuNCs@UiO-66). This nanocomposite enables highly sensitive and specific detection of creatinine (CR). Results demonstrated that the spatial confinement imposed by UiO-66 induced aggregation of the BSA-CuNCs and suppressed non-radiative transitions, leading to an approximately 10-fold enhancement in fluorescence intensity and a 11-fold increase in quantum yield. Leveraging the specific adsorption and enrichment capability of the UiO-66 framework toward CR, the BSA-CuNCs@UiO-66 fluorescence probe exhibited significant fluorescence quenching upon exposure to CR, achieving a detection range of 50–1000 nM and a detection limit of 30.81 nM. This work presented a novel confinement engineering strategy utilizing metal-organic frameworks (MOFs), establishing a new design paradigm for high-performance fluorescence probes with significant potential in bioanalytical applications.
{"title":"Construction of BSA-CuNCs@UiO-66 nanoprobe based on MOF confinement effect and its ultrasensitive fluorescence sensing for creatinine","authors":"Lian-Lian Duan , Wen-Jun Liu , Rui Zhai , Zhen-Guang Wang , Hong-Yuan Yan , Yun-Kai Lv PhD (Leading)","doi":"10.1016/j.saa.2026.127515","DOIUrl":"10.1016/j.saa.2026.127515","url":null,"abstract":"<div><div>This study employed a pore-confined synthesis strategy to achieve the in situ growth of bovine serum albumin-capped copper nanoclusters (BSA-CuNCs) within the UiO-66 framework (BSA-CuNCs@UiO-66). This nanocomposite enables highly sensitive and specific detection of creatinine (CR). Results demonstrated that the spatial confinement imposed by UiO-66 induced aggregation of the BSA-CuNCs and suppressed non-radiative transitions, leading to an approximately 10-fold enhancement in fluorescence intensity and a 11-fold increase in quantum yield. Leveraging the specific adsorption and enrichment capability of the UiO-66 framework toward CR, the BSA-CuNCs@UiO-66 fluorescence probe exhibited significant fluorescence quenching upon exposure to CR, achieving a detection range of 50–1000 nM and a detection limit of 30.81 nM. This work presented a novel confinement engineering strategy utilizing metal-organic frameworks (MOFs), establishing a new design paradigm for high-performance fluorescence probes with significant potential in bioanalytical applications.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127515"},"PeriodicalIF":4.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055571","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}
Pub Date : 2026-01-22DOI: 10.1016/j.saa.2026.127507
Mintong Zhao , Zhilong Tang , Mingyang Zhou , Xiaohan Zhang , Xinyu Wang , Xingchu Gong
The manufacturing of Chinese medicines often faces challenges such as poor product consistency, high solvent consumption, and long processing times. The percolation process is a commonly used technique for extracting medicinal herbs. Significant variation in percolate concentration and low concentration near the endpoint make it difficult for existing online detection technologies to accurately determine target component concentrations. To address this, the study developed an online monitoring system integrating multi-modal sensors for physical quantity, image, and spectral data. Using Xiaochaihu capsules, real-time multimodal data were collected, including over 20,000 physical quantity points, 14,000 spectra, and 14,000 images. A Transformer-based framework, PMFormer, was proposed, with interpolation-based data augmentation to alleviate the “data-rich but label-scarce” problem. PMFormer achieved R2 values of 0.96, 0.94, and 0.91 for 6-gingerol, 8-gingerol, and adenine, with RMSEs below 2.4, 0.4, and 1.8 μg/mL, respectively. A quantitative extraction control strategy was developed, determining the percolation endpoint when the accumulated total mass of collection (ATMC) met quality control limits. Validation showed improved consistency, reduced solvent use, and enhanced efficiency, aligning with Lean Six Sigma concepts. This study provides a reference for online monitoring of TCM percolation processes and demonstrates the potential of multimodal data fusion in pharmaceutical manufacturing.
{"title":"Online monitoring of Chinese herbal medicine production process toward lean six sigma: multimodal data fusion based on transformer architecture","authors":"Mintong Zhao , Zhilong Tang , Mingyang Zhou , Xiaohan Zhang , Xinyu Wang , Xingchu Gong","doi":"10.1016/j.saa.2026.127507","DOIUrl":"10.1016/j.saa.2026.127507","url":null,"abstract":"<div><div>The manufacturing of Chinese medicines often faces challenges such as poor product consistency, high solvent consumption, and long processing times. The percolation process is a commonly used technique for extracting medicinal herbs. Significant variation in percolate concentration and low concentration near the endpoint make it difficult for existing online detection technologies to accurately determine target component concentrations. To address this, the study developed an online monitoring system integrating multi-modal sensors for physical quantity, image, and spectral data. Using Xiaochaihu capsules, real-time multimodal data were collected, including over 20,000 physical quantity points, 14,000 spectra, and 14,000 images. A Transformer-based framework, PMFormer, was proposed, with interpolation-based data augmentation to alleviate the “data-rich but label-scarce” problem. PMFormer achieved R<sup>2</sup> values of 0.96, 0.94, and 0.91 for 6-gingerol, 8-gingerol, and adenine, with RMSEs below 2.4, 0.4, and 1.8 μg/mL, respectively. A quantitative extraction control strategy was developed, determining the percolation endpoint when the accumulated total mass of collection (ATMC) met quality control limits. Validation showed improved consistency, reduced solvent use, and enhanced efficiency, aligning with Lean Six Sigma concepts. This study provides a reference for online monitoring of TCM percolation processes and demonstrates the potential of multimodal data fusion in pharmaceutical manufacturing.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127507"},"PeriodicalIF":4.6,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146088592","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}
Pub Date : 2026-01-22DOI: 10.1016/j.saa.2026.127512
Yaxuan Han , Kazuya Shimooka , Jiacheng Gao , Meng-Wan Yeh , Harumi Sato , Yukihiro Ozaki , Motohiro Tsuboi
Raman imaging offers powerful capabilities for geoscience research; however, its quantitative application to mineral–organic interactions remains underdeveloped. Building upon the work of Kitanaka et al. (2024), who combined Raman imaging with chemometric analysis for concretion studies, this research advances the approach by coupling Raman imaging with the classical least squares (CLS) method to visualize the compositional distributions within a pyrite concretion from Taiwan. Standard Raman spectroscopic analysis identified quartz, anatase, pyrite, and well-preserved organic matter as the principal constituents. By applying the CLS algorithm to hyperspectral Raman datasets, the method enables semi-quantitative determination and spatial mapping of both mineral and organic components with high precision. The resulting CLS-based Raman images reveal distinct co-localization of pyrite and kerogen within microstructures resembling biogenetic textures. These spatial patterns provide direct visual evidence that supports bacterial sulfate reduction (BSR) as a key microbial process mediating concretion growth. This study demonstrates that integrating Raman imaging with CLS modeling not only enhances quantitative interpretation of complex mineral–organic assemblages but also provides new insights into the microbially influenced mineralization processes in sedimentary environments. The proposed approach establishes a robust framework for non-destructive, semi-quantitative, and spatially resolved characterization of geobiological materials.
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Pub Date : 2026-01-21DOI: 10.1016/j.saa.2026.127509
Clara Barnés-Calle , Pere Gou , Elena Fulladosa , Frans W.J. van den Berg
Fourier transform infrared spectroscopy (FTIR) combined with Amide I band deconvolution has been used to investigate protein structural changes occurring during high moisture extrusion processing (HMEP). However, it is a sensitive, user-dependent technique that has sparked debate over its appropriate analytical approach. This paper aims to assess the suitability of FTIR Amide I band deconvolution to investigate protein structural changes in fava bean protein concentrate (FBPC) caused by temperature treatment and/or HMEP at different temperatures (110 °C, 135 °C and 165 °C), and to explore its relationship with the texturisation level of the obtained products. Influence of sample preparation and parameter selection during FTIR deconvolution procedure was also explored. To do so, FBPC was heated in a convection oven or subjected to HMEP at different temperatures (110, 135 or 165 °C), and Fourier self-deconvolution (FSD) and second derivative (SD) were explored as band-narrowing methods to analyse protein conformation from FTIR spectra. FTIR Amide I band deconvolution showed high sensitivity to sample preparation and parameter selection during FSD and SD analytical procedure. Results suggested that HMEP caused the denaturation of β-sheet forms present initially in FBPC, and an increase of other structures including intermolecular β-sheet and/or aggregates—probably due to the formation of new intermolecular bonds. Moreover, although higher temperature during HMEP enhanced fibre-like structure formation, texturisation level could not be directly related to the protein conformation of the final high moisture extrudates (HME), since no significant differences were observed between protein secondary structure of HME under the studied conditions.
傅里叶变换红外光谱（FTIR）结合酰胺I波段反褶积（Amide I band deconvolution）研究了高水分挤压加工（HMEP）过程中蛋白质结构的变化。然而，它是一种敏感的、依赖于用户的技术，引发了对其适当分析方法的争论。本文旨在评估FTIR酰胺I波段反卷积在不同温度（110°C、135°C和165°C）下温度处理和/或HMEP对蚕豆蛋白浓缩物（FBPC）蛋白质结构变化的适用性，并探讨其与所得产品织构水平的关系。探讨了样品制备和参数选择对反褶积过程的影响。为此，将FBPC在对流烤箱中加热或在不同温度（110、135或165℃）下进行HMEP，并探索傅里叶自反卷积（FSD）和二阶导数（SD）作为窄带方法来分析FTIR光谱中的蛋白质构象。FTIR酰胺I波段反褶积对FSD和SD分析过程中的样品制备和参数选择具有很高的敏感性。结果表明，HMEP引起了FBPC中最初存在的β-薄片形式的变性，并增加了其他结构，包括分子间β-薄片和/或聚集体，这可能是由于形成了新的分子间键。此外，尽管高温增强了纤维状结构的形成，但纹理化水平与最终高水分挤出物（HME）的蛋白质构象没有直接关系，因为在研究条件下，HME的蛋白质二级结构之间没有显著差异。
{"title":"Exploring the use of FTIR Amide I band deconvolution to investigate protein secondary structure and texturisation during high moisture extrusion","authors":"Clara Barnés-Calle , Pere Gou , Elena Fulladosa , Frans W.J. van den Berg","doi":"10.1016/j.saa.2026.127509","DOIUrl":"10.1016/j.saa.2026.127509","url":null,"abstract":"<div><div>Fourier transform infrared spectroscopy (FTIR) combined with Amide I band deconvolution has been used to investigate protein structural changes occurring during high moisture extrusion processing (HMEP). However, it is a sensitive, user-dependent technique that has sparked debate over its appropriate analytical approach. This paper aims to assess the suitability of FTIR Amide <em>I</em> band deconvolution to investigate protein structural changes in fava bean protein concentrate (FBPC) caused by temperature treatment and/or HMEP at different temperatures (110 °C, 135 °C and 165 °C), and to explore its relationship with the texturisation level of the obtained products. Influence of sample preparation and parameter selection during FTIR deconvolution procedure was also explored. To do so, FBPC was heated in a convection oven or subjected to HMEP at different temperatures (110, 135 or 165 °C), and Fourier self-deconvolution (FSD) and second derivative (SD) were explored as band-narrowing methods to analyse protein conformation from FTIR spectra. FTIR Amide I band deconvolution showed high sensitivity to sample preparation and parameter selection during FSD and SD analytical procedure. Results suggested that HMEP caused the denaturation of β-sheet forms present initially in FBPC, and an increase of other structures including intermolecular β-sheet and/or aggregates—probably due to the formation of new intermolecular bonds. Moreover, although higher temperature during HMEP enhanced fibre-like structure formation, texturisation level could not be directly related to the protein conformation of the final high moisture extrudates (HME), since no significant differences were observed between protein secondary structure of HME under the studied conditions.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127509"},"PeriodicalIF":4.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047562","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}
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