Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy最新文献

{"title":"Machine learning assisted raman spectroscopy for the classification of ovarian cancer cells","authors":"Yong-Jiang Li ,&nbsp;Ye-Cheng Sun ,&nbsp;Hao-Ran Li ,&nbsp;Meng-Zhen Li ,&nbsp;Ya-Ruo Gao ,&nbsp;Tao Zhu ,&nbsp;Hao Wen ,&nbsp;Chun-Dong Xue ,&nbsp;Xu-Qu Hu ,&nbsp;Zhuo Yang ,&nbsp;Kai-Rong Qin","doi":"10.1016/j.saa.2026.127465","DOIUrl":"10.1016/j.saa.2026.127465","url":null,"abstract":"<div><div>Ovarian cancer is one of the most lethal gynecological malignancies, asymptomatic early progression, ineffective screening, and high histological heterogeneity. Accurate subtype classification and detection of chemotherapy resistance are critical for guiding personalized treatment strategies. Raman spectroscopy offers a label-free, non-destructive means of capturing biochemical fingerprints of cells, but its clinical potential is hindered by high spectral complexity and subtle inter-class variations. This study presents a machine learning–assisted Raman spectroscopy framework for the classification of ovarian cancer cell subtypes and their cisplatin resistance phenotypes. Raman spectra were acquired from normal ovarian epithelial cells (IOSE-80), four ovarian cancer cell lines (A2780, SKOV3, OVCAR-3, ES-2), and cisplatin-resistant variants (A2780-DDP, SKOV3-DDP). Three computational models were developed and systematically compared: a principal component analysis–support vector machine (PCA–SVM) algorithm and two convolutional neural network (CNN-Enhance and CNN-BiLSTM). Classification performance was assessed across three tasks: (i) discrimination of normal versus malignant cells, (ii) differentiation of cancer cells from their cisplatin-resistant variants, and (iii) classification of distinct cancer subtypes. Results show that Raman spectra reveal distinctive biochemical differences between normal and malignant cells, particularly in protein-, lipid-, and nucleic acid–related peaks. Both PCA–SVM and CNN achieved high classification accuracy (&gt;90%) in most tasks, with PCA–SVM demonstrating greater stability and superior performance in subtype classification, while CNN showed advantages in specific cell-type detection. Notably, PCA–SVM achieved up to 100% accuracy in differentiating cisplatin-resistant phenotypes. These findings demonstrate that integrating Raman spectroscopy with machine learning enables label-free, and accurate classification of ovarian cancer subtypes and drug resistance, offering a promising pathway toward minimally invasive precision diagnostics and personalized cancer treatment planning.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127465"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976049","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}

{"title":"Detection of hazelnut rancidity inside chocolate tablets using near-infrared hyperspectral imaging","authors":"Jokin Ezenarro ,&nbsp;Núria Vera-i-Valls ,&nbsp;Ángel García-Pizarro ,&nbsp;Daniel Schorn-García","doi":"10.1016/j.saa.2026.127505","DOIUrl":"10.1016/j.saa.2026.127505","url":null,"abstract":"<div><div>Near-infrared hyperspectral imaging (NIR-HSI) was evaluated as a non-destructive tool to detect hazelnut rancidity directly inside commercial chocolate bars, without separating the nuts from the chocolate matrix. Chocolate bars containing fresh or deliberately oxidized hazelnuts were imaged and analysed using a spectral unmixing strategy to separate chocolate and hazelnut contributions, yielding a residual component dominated by the nut signal where rancidity-related differences became more evident. ASCA applied to the original preprocessed spectra showed that rancidity explained 13.09% of the total variance (<em>p</em> = 0.001), confirming a significant and interpretable spectral effect despite the dominant chocolate background. A PLS-DA model built on the residual spectra successfully discriminated fresh from rancid hazelnuts and correctly classified all chocolate bars in an independent validation set, using fewer latent variables than a model based on the original data. These results demonstrate the feasibility of in situ detection of hazelnut rancidity within chocolate tablets and support NIR-HSI, combined with chemometric modelling, as a promising approach for non-destructive quality control in heterogeneous confectionery products.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127505"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036076","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}

{"title":"SERS-based quantitative detection of furfural in transformer oil using an improved residual network and transfer learning","authors":"Xia Jie","doi":"10.1016/j.saa.2025.127423","DOIUrl":"10.1016/j.saa.2025.127423","url":null,"abstract":"<div><div>Accurate quantification of furfural concentration in transformer oil is crucial for assessing the aging status of insulating paper; however, conventional quantitative models face significant challenges in generalizing due to interference from complex oil matrices. To address this, we propose a quantitative analytical framework integrating an improved residual network (ResNet) with transfer learning. Initially, a deep convolutional generative adversarial network (DCGAN) was introduced to augment the Raman spectral dataset of furfural, generating high-fidelity spectra (peak signal-to-noise ratio, PSNR = 46.02; structural similarity index, SSIM = 0.98) to mitigate the risk of overfitting due to small sample sizes. Subsequently, an improved ResNet model was developed by simplifying network structure and embedding Dropout regularization, significantly enhancing feature extraction capability and robustness (correlation coefficient, R² = 0.9921). To accommodate spectral distribution differences among multi-source oil samples, a transfer learning framework (Transfer-ResNet) was constructed by freezing lower network layers and fine-tuning deeper residual blocks, thus achieving cross-domain generalization (R² = 0.9914). Furthermore, Grad-CAM++ interpretability analysis revealed the model's multi-frequency response mechanisms, identifying key molecular features such as C-H/C-C vibrations and C<img>N stretching. This proposed method provides a novel strategy for accurate, interpretable quantitative SERS detection of furfural in transformer oil, demonstrating significant potential for transformer condition assessment.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127423"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036095","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}

{"title":"Exploring the molecular interaction between tryptophan and β-cyclodextrin modified ZnS quantum dots using spectroscopic analysis","authors":"Ritu K. Shah ,&nbsp;Sahaj A. Gandhi ,&nbsp;Pinkesh G. Sutariya ,&nbsp;Krushna A. Baraiya","doi":"10.1016/j.saa.2026.127497","DOIUrl":"10.1016/j.saa.2026.127497","url":null,"abstract":"<div><div>The luminescent properties of water-soluble β-cyclodextrin surface-modified capped ZnS QDs (β-CD/ZnS QDs) were investigated for host–guest interactions. The optical properties of the β-CD/ZnS QDs complex revealed selectivity for L-tryptophan (L-Trp) through aggregation-induced emission. Fluorescence titration and Dynamic Light Scattering analysis revealed the fluorescence enhancement effect and aggregation state of β-CD/ZnS QDs in the presence of L-Trp, respectively. The apparent binding constant was determined <span><math><mn>1.75</mn><mspace></mspace><mi>x</mi><mspace></mspace><msup><mn>10</mn><mrow><mo>−</mo><mn>8</mn></mrow></msup><mspace></mspace><msup><mi>M</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></math></span> determine, along an LOD of 420 nM for L-Trp. UV–Visible, Raman, Fourier transform infrared, and Matrix-Assisted Laser Desorption/Ionization (MALDI) - mass spectrometry were used to investigate the interaction of β-CD/ZnS QDs with L-Trp. Moreover, the β-CD/ZnS QDs probe was used for L-Trp detection in soybeans, yielding 91–97% recovery using fluorescence titration.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127497"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036098","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}

{"title":"Real-time concentration prediction in column chromatography purification using NIR optical sensing and evolutionary attention-LSTM modeling","authors":"Dandan Zhai ,&nbsp;Jinfeng Liu ,&nbsp;Jingyun Liu ,&nbsp;Mingxing Li ,&nbsp;Anqi Liu ,&nbsp;Yu Yang ,&nbsp;Zhenwei Zhang ,&nbsp;Na Liu ,&nbsp;Ming Hui ,&nbsp;Peng Li","doi":"10.1016/j.saa.2026.127463","DOIUrl":"10.1016/j.saa.2026.127463","url":null,"abstract":"<div><div>Ginsenosides, the principal bioactive constituents of American ginseng (<em>Panax quinquefolius</em>), require precise monitoring during column chromatography to ensure product quality. Conventional assays are time-consuming and unsuited for real-time process control, particularly for low-abundance compounds. Here, we present a rapid, non-destructive near-infrared (NIR) optical sensing system coupled with an Evolutionary Attention–based Long Short-Term Memory (EA-LSTM) model to predict ginsenoside concentrations dynamically. The approach exploits the high-speed acquisition and reproducibility of NIR spectra while leveraging EA-LSTM's temporal feature extraction and adaptive attention to focus on chemically informative elution segments. Offline spectra from eight elution batches, referenced by UPLC, enabled model training and validation, with high-frequency sampling simulated to mimic industrial deployment. The system demonstrated high accuracy (R² = 0.9841, RPD = 7.94) and robustness under dynamic conditions (R² = 0.9694, RPD = 5.72, response time = 0.34 ms), highlighting its potential for real-time, non-destructive monitoring and intelligent control in natural product purification.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127463"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000206","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}

{"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 ,&nbsp;Pere Gou ,&nbsp;Elena Fulladosa ,&nbsp;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":"2026-04-15","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}

{"title":"A novel amide-quinoline salt-based mitochondrial-targeted fluorescent probe for detecting pH in cells and water samples","authors":"Feng-Ting Liu ,&nbsp;Bang-Zhao Zhou ,&nbsp;Jun-Ying Miao ,&nbsp;Bao-Xiang Zhao ,&nbsp;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":"2026-04-15","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}

{"title":"A near-infrared viscosity probe for dynamic imaging of multilevel drug-induced liver injury","authors":"Ziyi Wang ,&nbsp;Keying Yang ,&nbsp;Chen Yang ,&nbsp;Bing Xue ,&nbsp;Kaifu Ma ,&nbsp;Zuonian Jin ,&nbsp;Cailing Fan ,&nbsp;Libo Jiang ,&nbsp;Wei Shu","doi":"10.1016/j.saa.2026.127470","DOIUrl":"10.1016/j.saa.2026.127470","url":null,"abstract":"<div><div>Drug-induced liver injury (DILI) disrupts hepatocellular homeostasis during the early phase. However, conventional detection methods often only become apparent after the injury has advanced. Cellular viscosity is a characteristic of the intracellular microenvironment, which regulates diffusion, membrane fluidity, organelle transport, and signaling, and can change rapidly under stress. As DILI can elicit mitochondrial dysfunction, endoplasmic reticulum stress, and lipid droplet remodeling, localized viscosity changes emerge early in the injury process. Here we report a near-infrared viscosity probe named <strong>WZY-1</strong> based on a pyridinium-aryl molecular rotor. In high-viscosity media, the restriction of intramolecular rotation suppresses the twisted intramolecular charge transfer (TICT) and enhances the emission intensity. <strong>WZY-1</strong> provides long-wavelength near-infrared emission with a large Stokes shift, maintains stable signals, shows tolerance to common ions and reactive species, and displays good biocompatibility. At the cellular level, <strong>WZY-1</strong> can distinguish hepatocellular carcinoma cells from normal hepatic cells, and detect endogenous viscosity changes. In DILI models, it visualizes viscosity changes in cells, tissue, and live mice. Taken together, <strong>WZY-1</strong> enables noninvasive, <em>in situ</em>, real-time readout of viscosity and supports early assessment of DILI across cellular, tissue, and animal levels.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127470"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021229","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}

{"title":"Characterization of Seals from the Santi Quattro Coronati complex (Rome, Italy) with a focus on FTIR and chemometric analysis","authors":"Martina Franchi ,&nbsp;Alessandro Ciccola ,&nbsp;Matteo Taroni ,&nbsp;Anna Candida Felici ,&nbsp;Alessandro Nucara ,&nbsp;Naurang Lal Saini ,&nbsp;Luca Businaro ,&nbsp;Lia Barelli ,&nbsp;Simona Morretta ,&nbsp;Alessia Cedola","doi":"10.1016/j.saa.2025.127413","DOIUrl":"10.1016/j.saa.2025.127413","url":null,"abstract":"<div><div>Red wax seals were discovered at the Santi Quattro Coronati complex in Rome, within a staircase utilized as a domestic disposal pit in the late 15th or early 16th century. The four seals, recovered from three distinct stratigraphic units, were in a poor state of preservation. Over time, they underwent morphological and chemical transformations, resulting in the loss of the impressions they originally bore, along with the documents to which they were once affixed.</div><div>The composition and manufacturing techniques of wax seals changed over time, resulting in complex, multi-component materials. A multi-analytical approach was employed to investigate their composition and conservation state, incorporating Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS), X-ray Fluorescence (XRF) spectroscopy, Fiber Optic Reflectance Spectroscopy (FORS), Fourier-Transform Infrared spectroscopy (FTIR) in both micro-reflectance and Attenuated Total Reflectance (ATR) modes, and chemometric analysis. The study was exclusively performed on the archaeological samples recovered from the site under investigation. Consequently, the limited sample size precludes material classification as a primary objective. This study instead aims to advance knowledge of historical wax seal production materials and degradation processes, identifying key aging markers and assessing potential correlations between their conservation state and their respective stratigraphic contexts.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127413"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035685","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}

{"title":"Interval retention optimization (IRO): An efficient feature selection method for expanding spectral datasets","authors":"Yifan Cheng ,&nbsp;Mengsheng Zhang ,&nbsp;Chen Niu ,&nbsp;Harse Sattar ,&nbsp;Lianbo Guo","doi":"10.1016/j.saa.2026.127445","DOIUrl":"10.1016/j.saa.2026.127445","url":null,"abstract":"<div><div>Effective feature selection is crucial for large-scale near-infrared (NIR) spectroscopy, yet existing algorithms face a trade-off between accuracy and efficiency. This trade-off arises from the search strategy: sequential methods are efficient but often lack generalization, while global methods capture feature interactions but incur high computational costs due to repeated retraining. To address these limitations, we propose Interval Retention Optimization (IRO), a framework that reformulates feature selection as a continuous allocation of retention rates across wavelength intervals. Guided by global importance measures and optimized with Bayesian search, IRO leverages a mask-based perturbation strategy to evaluate candidate subsets directly on a pre-trained model, thereby eliminating retraining and significantly boosting efficiency. Experimental results demonstrate that IRO can achieve improved prediction accuracy and computational efficiency, reducing RMSEP by up to 9.10 %, improving RMSECV and <em>R</em>² by up to 5.51 % and 15.20 %, respectively, and accelerating computation by up to 87.54 %. These results highlight IRO as a scalable and practical solution for spectral feature selection in complex NIR applications.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"351 ","pages":"Article 127445"},"PeriodicalIF":4.6,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013987","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}