Pub Date : 2026-04-15Epub 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":"2026-04-15","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 : 2026-04-15Epub Date: 2026-01-14DOI: 10.1016/j.saa.2026.127469
Yingte Wang, Yijun Shen, Jiandang Xue, Lele Liu, Yawei Li
Nitrogen-doped carbon dots (N-CDs) exhibiting long-wavelength fluorescence were successfully synthesized via a one-step hydrothermal method using neutral red and thiosemicarbazide as precursors, specifically to address limitations in pyrophosphate (P2O74−, PPi) detection. As an essential adenosine triphosphate (ATP) hydrolysis byproduct and disease biomarker, PPi quantification remains challenged by costly instrumentation and complex procedures in conventional methods. The synthesized N-CDs demonstrated optimal excitation/emission at 520/600 nm with a quantum yield (QY) of 4.8%, enabling rapid (1 min response time), selective PPi detection through fluorescence quenching. Quantitative analysis revealed a linear detection range of 1.38–85.60 μmol/L (R2 = 0.9983) and low detection limit of 0.42 μmol/L. Practical validation in milk samples yielded excellent recovery rates of 95.26–105.74% with ≤1.86% relative standard deviation, confirming reliability in complex matrices. Critically, the N-CDs' deep-tissue penetration capability facilitated real-time monitoring of intracellular PPi dynamics in HeLa cells, while maintaining high biocompatibility. This work establishes multi-element doped carbon dots as both a cost-effective analytical alternative and a versatile platform for biomedical imaging applications.
{"title":"Long-wavelength emissive N-doped carbon dots as a fluorescent probe for sensitive detection of pyrophosphate and cellular imaging","authors":"Yingte Wang, Yijun Shen, Jiandang Xue, Lele Liu, Yawei Li","doi":"10.1016/j.saa.2026.127469","DOIUrl":"10.1016/j.saa.2026.127469","url":null,"abstract":"<div><div>Nitrogen-doped carbon dots (N-CDs) exhibiting long-wavelength fluorescence were successfully synthesized via a one-step hydrothermal method using neutral red and thiosemicarbazide as precursors, specifically to address limitations in pyrophosphate (P<sub>2</sub>O<sub>7</sub><sup>4−</sup>, PPi) detection. As an essential adenosine triphosphate (ATP) hydrolysis byproduct and disease biomarker, PPi quantification remains challenged by costly instrumentation and complex procedures in conventional methods. The synthesized N-CDs demonstrated optimal excitation/emission at 520/600 nm with a quantum yield (QY) of 4.8%, enabling rapid (1 min response time), selective PPi detection through fluorescence quenching. Quantitative analysis revealed a linear detection range of 1.38–85.60 μmol/L (R<sup>2</sup> = 0.9983) and low detection limit of 0.42 μmol/L. Practical validation in milk samples yielded excellent recovery rates of 95.26–105.74% with ≤1.86% relative standard deviation, confirming reliability in complex matrices. Critically, the N-CDs' deep-tissue penetration capability facilitated real-time monitoring of intracellular PPi dynamics in HeLa cells, while maintaining high biocompatibility. This work establishes multi-element doped carbon dots as both a cost-effective analytical alternative and a versatile platform for biomedical imaging applications.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127469"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146004691","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 : 2026-04-15Epub Date: 2026-01-12DOI: 10.1016/j.saa.2026.127471
Jian-Hua Tang , Jing-Jing Yu , Jun-Tao Niu , Tong Han , Jia-Le Cui , Yi-Ran Di , Ting Liang , Yan-Fei Kang , Hao-Jun Fan
The endoplasmic reticulum (ER), a central organelle, play critical roles in protein synthesis, folding and detoxification. Viscosity within the ER lumen is recognized as an essential physical property for maintaining its normal functions, and its dysregulation has been associated with numerous diseases and aging processes. Thus, detecting change of viscosity was meaningful in ER. In this work, a near-infrared (NIR) fluorescent probe (BEQ-ER) with a classic D-π-A structure is designed to measure viscosity fluctuation in ER relying on twisted intramolecular charge transfer (TICT) mechanism. BEQ-ER exhibited strong fluorescence at 682 nm under conditions of high viscosity due to suppressed intramolecular rotation. Moreover, the image results showed BEQ-ER can precisely target ER with a colocalization coefficient of 0.964, and high viscosity was detected in cancer cells. Importantly, BEQ-ER was shown to selectively illuminate tumor tissues in 4 T1 tumor-bearing mice. Therefore, this work provided a valuable tool for investigating disease mechanisms and progression through real-time monitoring of ER viscosity.
{"title":"An endoplasmic reticulum-targeting NIR fluorescent probe for viscosity imaging in vitro and vivo","authors":"Jian-Hua Tang , Jing-Jing Yu , Jun-Tao Niu , Tong Han , Jia-Le Cui , Yi-Ran Di , Ting Liang , Yan-Fei Kang , Hao-Jun Fan","doi":"10.1016/j.saa.2026.127471","DOIUrl":"10.1016/j.saa.2026.127471","url":null,"abstract":"<div><div>The endoplasmic reticulum <strong>(</strong>ER), a central organelle, play critical roles in protein synthesis, folding and detoxification. Viscosity within the ER lumen is recognized as an essential physical property for maintaining its normal functions, and its dysregulation has been associated with numerous diseases and aging processes. Thus, detecting change of viscosity was meaningful in ER. In this work, a near-infrared (NIR) fluorescent probe (<strong>BEQ-ER</strong>) with a classic D-π-A structure is designed to measure viscosity fluctuation in ER relying on twisted intramolecular charge transfer (TICT) mechanism. <strong>BEQ-ER</strong> exhibited strong fluorescence at 682 nm under conditions of high viscosity due to suppressed intramolecular rotation. Moreover, the image results showed <strong>BEQ-ER</strong> can precisely target ER with a colocalization coefficient of 0.964, and high viscosity was detected in cancer cells. Importantly, <strong>BEQ-ER</strong> was shown to selectively illuminate tumor tissues in 4 T1 tumor-bearing mice. Therefore, this work provided a valuable tool for investigating disease mechanisms and progression through real-time monitoring of ER viscosity.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127471"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146032248","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 : 2026-04-15Epub Date: 2026-01-12DOI: 10.1016/j.saa.2026.127467
Yu Liu, Jing-Yan Li, Yu-Peng Xu, Pu Chen, Dan Liu, Xiao-Li Chu
To evaluate mid-infrared sampling geometries for rapid coal analysis, attenuated total reflectance (ATR) and diffuse reflectance FTIR (DRF) were systematically compared, and multimodal fusion was explored. A total of 200 coal samples were analyzed for six key quality indices: ash, calorific value, volatile matter, fixed carbon, moisture, and sulfur. During data preprocessing, extended multiplicative scatter correction (EMSC) was applied to improve spectral stability, followed by correlation-based wavelength selection and cross-validated optimization of latent variables to construct partial least squares (PLS) regression models for each property. Notably, this study establishes a unified and reproducible benchmarking framework to disentangle sampling-geometry effects (surface-sensitive ATR and bulk-sensitive DRF) under strictly identical preprocessing, variable-selection, and cross-validation rules, and interprets the observed performance differences via chemically meaningful spectral contribution. In addition, we systematically benchmark three fusion levels (low/mid/high) within the same framework to clarify when multimodal integration is beneficial and when it is not. DRF achieved the most accurate ash prediction, whereas ATR performed better for volatile matter and moisture; calorific value and fixed carbon were comparable. Sulfur prediction remained challenging for both modalities. Low- and mid-level fusion showed no consistent synergistic gain, while high-level fusion improved prediction for five properties. Overall, the study provides actionable guidance for selecting FTIR modality and fusion strategy for practical coal quality assessment.
{"title":"Comparative and exploratory study of ATR and diffuse reflectance mid-infrared spectroscopy for coal property analysis","authors":"Yu Liu, Jing-Yan Li, Yu-Peng Xu, Pu Chen, Dan Liu, Xiao-Li Chu","doi":"10.1016/j.saa.2026.127467","DOIUrl":"10.1016/j.saa.2026.127467","url":null,"abstract":"<div><div>To evaluate mid-infrared sampling geometries for rapid coal analysis, attenuated total reflectance (ATR) and diffuse reflectance FTIR (DRF) were systematically compared, and multimodal fusion was explored. A total of 200 coal samples were analyzed for six key quality indices: ash, calorific value, volatile matter, fixed carbon, moisture, and sulfur. During data preprocessing, extended multiplicative scatter correction (EMSC) was applied to improve spectral stability, followed by correlation-based wavelength selection and cross-validated optimization of latent variables to construct partial least squares (PLS) regression models for each property. Notably, this study establishes a unified and reproducible benchmarking framework to disentangle sampling-geometry effects (surface-sensitive ATR and bulk-sensitive DRF) under strictly identical preprocessing, variable-selection, and cross-validation rules, and interprets the observed performance differences via chemically meaningful spectral contribution. In addition, we systematically benchmark three fusion levels (low/mid/high) within the same framework to clarify when multimodal integration is beneficial and when it is not. DRF achieved the most accurate ash prediction, whereas ATR performed better for volatile matter and moisture; calorific value and fixed carbon were comparable. Sulfur prediction remained challenging for both modalities. Low- and mid-level fusion showed no consistent synergistic gain, while high-level fusion improved prediction for five properties. Overall, the study provides actionable guidance for selecting FTIR modality and fusion strategy for practical coal quality assessment.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127467"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976115","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 : 2026-04-15Epub Date: 2026-01-13DOI: 10.1016/j.saa.2026.127475
Petr Larionov , Nikolay Maslov , Natalia Pogorelova , Ilya Rozhin , Natalya Sarnitskaya , Vyacheslav Stupak , Irina Kirilova , Andrey Korytkin , Ilya Digel
Bacterial cellulose (BC) is a promising biomaterial for medical and biotechnological applications. However, microbial contaminants and their metabolic residues remain a critical limitation for its clinical use. Many of the BC purity tests are labor-intensive and time-consuming. This study investigates the feasibility of using laser-induced fluorescence (LIF) spectroscopy for monitoring microbial contamination in BC.
BC samples were obtained from a Medusomyces gisevii consortium and subjected to various purification protocols (alkaline, detergent and oxidative treatments). LIF spectra were recorded across 220–290 nm excitation wavelengths and analyzed chemometrically. For better interpretation of the results, the same samples were examined by laser scanning confocal microscopy (LSM).
The results reveal that both native and treated BC samples exhibit fluorescence features associated with tryptophan and tyrosine, indicative of microbial residues. Treatment with NaOH effectively reduced tryptophan-associated signals, while hydrogen peroxide diminished tyrosine-related fluorescence. None of the purification strategies completely eliminated these signals. A good correlation between the LIF and the more labor-consuming LSM data was observed. LIF showed the capability of rapid and reliable differentiation between treatment variants and provided spectral fingerprints linked to residual contamination. Future work may focus on standardizing LIF-based diagnostic protocols and integrating them into biotechnological workflows for contamination monitoring.
{"title":"Detection of residual microbial biomarkers in bacterial cellulose using laser-induced fluorescence spectroscopy","authors":"Petr Larionov , Nikolay Maslov , Natalia Pogorelova , Ilya Rozhin , Natalya Sarnitskaya , Vyacheslav Stupak , Irina Kirilova , Andrey Korytkin , Ilya Digel","doi":"10.1016/j.saa.2026.127475","DOIUrl":"10.1016/j.saa.2026.127475","url":null,"abstract":"<div><div>Bacterial cellulose (BC) is a promising biomaterial for medical and biotechnological applications. However, microbial contaminants and their metabolic residues remain a critical limitation for its clinical use. Many of the BC purity tests are labor-intensive and time-consuming. This study investigates the feasibility of using laser-induced fluorescence (LIF) spectroscopy for monitoring microbial contamination in BC.</div><div>BC samples were obtained from a <em>Medusomyces gisevii</em> consortium and subjected to various purification protocols (alkaline, detergent and oxidative treatments). LIF spectra were recorded across 220–290 nm excitation wavelengths and analyzed chemometrically. For better interpretation of the results, the same samples were examined by laser scanning confocal microscopy (LSM).</div><div>The results reveal that both native and treated BC samples exhibit fluorescence features associated with tryptophan and tyrosine, indicative of microbial residues. Treatment with NaOH effectively reduced tryptophan-associated signals, while hydrogen peroxide diminished tyrosine-related fluorescence. None of the purification strategies completely eliminated these signals. A good correlation between the LIF and the more labor-consuming LSM data was observed. LIF showed the capability of rapid and reliable differentiation between treatment variants and provided spectral fingerprints linked to residual contamination. Future work may focus on standardizing LIF-based diagnostic protocols and integrating them into biotechnological workflows for contamination monitoring.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127475"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976051","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 : 2026-04-15Epub Date: 2026-01-17DOI: 10.1016/j.saa.2026.127474
Xing Hu, Dezhi Chen, Shihao Zhou, Jun Xu, Kai Xu, Long Jiang, Yi Wang, Sheng Su, Song Hu, Jun Xiang
The precise detection of biochar characteristics serves as a critical determinant in both production process optimization and targeted application selection. In this study, interpretable machine learning prediction models based on Raman spectroscopy, including extreme gradient boosting, support vector regression, feedforward neural network, random forest, and ridge regression, were developed for accurately predicting the characteristics of biochar derived from six different biomass, across a pyrolysis temperature range of 350–1000 °C. Results demonstrated that the feedforward neural network achieved superior overall predictive performance for key biochar characteristics (R2 = 0.89–0.95), including fixed carbon, volatile, H, O, atomic ratio of H/C and O/C. Highly accurate prediction of ash (R2 = 0.95) was achieved by integrating the results of multibasic prediction of volatile matter and fixed carbon and establishing a quantitative relationship with ash. A tripartite analytical framework was developed to improve model interpretability by integrating CARS for spectral feature selection, SHAP analysis to quantify feature importance, and mechanistic correlation analysis of model predictions linking selected bands to biochar structure. The robustness of the models was evaluated through tests on various enhanced datasets, confirming their resilience under different perturbations. This approach, combining Raman spectroscopy with machine learning, offers a rapid and reliable means for predicting biochar characteristics, facilitating more efficient control of biomass pyrolysis processes, and supporting the development of online monitoring techniques.
{"title":"Interpretable machine learning prediction of biochar characteristics based on laser-Raman spectroscopy","authors":"Xing Hu, Dezhi Chen, Shihao Zhou, Jun Xu, Kai Xu, Long Jiang, Yi Wang, Sheng Su, Song Hu, Jun Xiang","doi":"10.1016/j.saa.2026.127474","DOIUrl":"10.1016/j.saa.2026.127474","url":null,"abstract":"<div><div>The precise detection of biochar characteristics serves as a critical determinant in both production process optimization and targeted application selection. In this study, interpretable machine learning prediction models based on Raman spectroscopy, including extreme gradient boosting, support vector regression, feedforward neural network, random forest, and ridge regression, were developed for accurately predicting the characteristics of biochar derived from six different biomass, across a pyrolysis temperature range of 350–1000 °C. Results demonstrated that the feedforward neural network achieved superior overall predictive performance for key biochar characteristics (R<sup>2</sup> = 0.89–0.95), including fixed carbon, volatile, H, O, atomic ratio of H/C and O/C. Highly accurate prediction of ash (R<sup>2</sup> = 0.95) was achieved by integrating the results of multibasic prediction of volatile matter and fixed carbon and establishing a quantitative relationship with ash. A tripartite analytical framework was developed to improve model interpretability by integrating CARS for spectral feature selection, SHAP analysis to quantify feature importance, and mechanistic correlation analysis of model predictions linking selected bands to biochar structure. The robustness of the models was evaluated through tests on various enhanced datasets, confirming their resilience under different perturbations. This approach, combining Raman spectroscopy with machine learning, offers a rapid and reliable means for predicting biochar characteristics, facilitating more efficient control of biomass pyrolysis processes, and supporting the development of online monitoring techniques.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127474"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036099","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 : 2026-04-15Epub 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":"2026-04-15","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 : 2026-04-15Epub Date: 2026-01-10DOI: 10.1016/j.saa.2026.127460
Zhixuan Feng , Wenjing Liu , Xiaojie Zhang , Ping Li , Libo Du , Yan Cui
Protein sulfenic acids (PSA) are crucial reactive species in oxidative stress, yet their transient nature and the complex cellular environment demand detection tools with high selectivity, sensitivity, and organelle-targeting capability. To address this, we report a novel near-infrared (NIR) turn-on fluorescent probe, HCA-CHD. This probe is rationally constructed with a cationic hemicyanine (HCA) dye as the NIR fluorophore and a 1,3-cyclohexanedione (CHD) moiety as the specific reaction site for PSA. The reaction with PSA forms a thioether linkage, which triggers a significant fluorescence enhancement. HCA-CHD exhibits a maximum absorption at 640 nm and, upon reaction, shows a strong turn-on fluorescence emission at 710 nm. Comprehensive characterization confirms its excellent reactivity, high selectivity, good stability, and inherent mitochondria-targeting ability. We successfully demonstrate the application of HCA-CHD for the highly sensitive and selective imaging of endogenous PSA in the mitochondria of live HeLa and MCF-7 cells, thus providing a powerful tool for investigating redox biology.
{"title":"A mitochondria-targeted “turn-on” near-infrared fluorescent probe for imaging protein Sulfenic acids in live cells under oxidative stress","authors":"Zhixuan Feng , Wenjing Liu , Xiaojie Zhang , Ping Li , Libo Du , Yan Cui","doi":"10.1016/j.saa.2026.127460","DOIUrl":"10.1016/j.saa.2026.127460","url":null,"abstract":"<div><div>Protein sulfenic acids (PSA) are crucial reactive species in oxidative stress, yet their transient nature and the complex cellular environment demand detection tools with high selectivity, sensitivity, and organelle-targeting capability. To address this, we report a novel near-infrared (NIR) turn-on fluorescent probe, HCA-CHD. This probe is rationally constructed with a cationic hemicyanine (HCA) dye as the NIR fluorophore and a 1,3-cyclohexanedione (CHD) moiety as the specific reaction site for PSA. The reaction with PSA forms a thioether linkage, which triggers a significant fluorescence enhancement. HCA-CHD exhibits a maximum absorption at 640 nm and, upon reaction, shows a strong turn-on fluorescence emission at 710 nm. Comprehensive characterization confirms its excellent reactivity, high selectivity, good stability, and inherent mitochondria-targeting ability. We successfully demonstrate the application of HCA-CHD for the highly sensitive and selective imaging of endogenous PSA in the mitochondria of live HeLa and MCF-7 cells, thus providing a powerful tool for investigating redox biology.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127460"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957741","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 : 2026-04-15Epub Date: 2026-01-19DOI: 10.1016/j.saa.2026.127501
Yulei Zhang , Xingzhu Tang , Lei Wang , Ye Wang , Chaofan Sun
Focusing theoretically on the Excited State Intramolecular Proton Transfer (ESIPT) process, this study evaluates how external electric fields (EEFs) modulate both ESIPT dynamics and the photophysical behavior of a benzothiadiazole derivative, 2-(Benzo[c] Weller (1955), Huang et al. (2024), Lu and He (2021) [1, 2, 5] thiadiazol-4-yl)-N, N-diethylpyridin-4-amine (BZ-4, Chem. Commun., 2024, 60, 9105) using density functional theory (DFT) and time-dependent DFT (TD-DFT) methods. Analyses of dihedral angle variations in molecular structures, including infrared (IR) vibrational spectra related to bond lengths, bond angles, and intramolecular hydrogen bond (IHB) parameters, demonstrate that hydrogen bond strength varies under different EEFs. Furthermore, applying EEFs in different directions differentially impacts the molecular potential energy curves (PECs). Notably, while the direction of the EEFs determines the high or low barrier regime of the PECs, the variation of field intensity causes only minimal fluctuations in the energy barrier height. Moreover, the enhancement of IHB induced by the application of a negative electric field will inhibit the proton transfer. Specifically, distortion of the dihedral angle θ hinders the completion of ESIPT. Moreover, the applied electric field suppresses the twisted intramolecular charge transfer (TICT) process, thus enhancing the fluorescence intensity. This theoretical investigation offers valuable guidance on modulating molecular photophysical behaviors through external electric field regulation.
本研究从理论上关注激发态分子内质子转移（ESIPT）过程，评估了外电场（EEFs）如何调节苯并噻唑衍生物2-(Benzo[c] Weller (1955), Huang et al. （2024), Lu and He(2021)[1,2,5]噻二唑-4-基）- n， n-二乙基吡啶-4-胺（BZ-4, Chem. 4）的ESIPT动力学和光物理行为。Commun。应用密度泛函理论（DFT）和时变DFT （TD-DFT）方法进行分析。分析分子结构的二面角变化，包括与键长、键角和分子内氢键（IHB）参数相关的红外（IR）振动光谱，表明在不同的电场作用下，氢键强度是不同的。此外，不同方向的电场作用对分子势能曲线的影响也不同。值得注意的是，虽然电场的方向决定了PECs的高或低势垒状态，但场强的变化只会引起能量势垒高度的最小波动。此外，施加负电场诱导的IHB增强会抑制质子转移。具体来说，二面角θ的畸变阻碍了ESIPT的完成。外加电场抑制了分子内扭曲电荷转移（TICT）过程，从而增强了荧光强度。这一理论研究为通过外电场调控分子光物理行为提供了有价值的指导。
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Pub Date : 2026-04-15Epub Date: 2026-01-13DOI: 10.1016/j.saa.2026.127472
Huizhen Lin , Jiawang Chen , Yiming Chen , Dechan Lu
Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.
{"title":"SERS-based deep learning approach for early detection of gestational diabetes mellitus","authors":"Huizhen Lin , Jiawang Chen , Yiming Chen , Dechan Lu","doi":"10.1016/j.saa.2026.127472","DOIUrl":"10.1016/j.saa.2026.127472","url":null,"abstract":"<div><div>Early and precise diagnosis of gestational diabetes mellitus (GDM) is crucial for improving maternal and neonatal outcomes and reducing the risk of adverse pregnancy events. However, current clinical screening methods for GDM still exhibit limitations in detection speed, sensitivity and convenience, making it difficult to meet the clinical demand for rapid early-pregnancy screening. To address this, we propose a novel strategy for early GDM diagnosis based on surface-enhanced Raman spectroscopy (SERS) combined with deep learning, aiming to achieve rapid and accurate early screening. Characteristic SERS spectra of serum were obtained using a substrate based on silver nanoparticles (Ag NPs). A fused PCA-CNN model integrating principal component analysis (PCA) for dimensionality reduction and a one-dimensional convolutional neural network (1D-CNN) for feature learning was developed. The PCA-CNN model effectively extracts potential biomarker features from serum SERS spectra, achieving a diagnostic accuracy of 93.7%, with sensitivity and specificity of 0.95 and 0.93, respectively. Moreover, the entire detection process can be completed within 30 min, requires about 2.5 μL of serum per sample, and involves minimal preprocessing, highlighting both efficiency and practicality. This study provides a novel method for early GDM screening that combines high diagnostic performance with clinical applicability, offering promising technical support for early intervention and clinical management of GDM.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127472"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976114","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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