Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy最新文献

A rapid method using FTIR-ATR spectroscopy combined with PCA and a classification model was applied to distinguish between natural, synthetic, and adulterated Bergamot essential oil (BEO). Synthetic BEOs are often composed of specific alcohols such as ethanol and dipropylene glycol (DPG), which are used to dilute synthetic metabolites like limonene, linalyl acetate, and linalool. Synthetic BEOs exhibited a distinct peak at 1340 cm^-1, linked to CH bending of alcohols or methyl group deformation in artificial esters like linalyl acetate, a peak that is absent in natural BEOs. Additionally, an absorption band between 3600 and 3100 cm^-1 indicated the presence of DPG and synthetic ethanol, a byproduct of synthetic linalyl acetate. These findings were validated by comparison with NMR spectroscopy for metabolite recognition. A logistic regression (LR) model using PCA was applied to 369 samples, achieving an overall accuracy of 0.976 ± 0.016 through five-fold cross-validation (CV).

Early diagnosis of liver cancer is crucial for developing clinical treatment strategies and improving patient survival rates. However, current diagnostic methods are often invasive, complex, and time-consuming, making them unsuitable for early screening in practical settings. Therefore, there is an urgent need to develop efficient and convenient non-invasive diagnostic techniques. This study presents a non-invasive optical diagnostic approach based on surface-enhanced Raman spectroscopy (SERS) and a deep learning algorithm for liver cancer staging identification and auxiliary screening. We systematically collected high-quality SERS spectral data from serum samples of patients with different stages of liver cancer (T1, T2, T3), hepatitis B (HBV), and healthy controls (Normal). Recursive feature elimination (RFE) was employed for feature selection, eliminating redundant spectral bands and retaining features highly relevant to classification, which significantly enhanced the model's discriminative ability. The selected features were input then into a gradient boosting decision tree (GBDT) model. Through residual iterative optimization, the model effectively captured nonlinear feature interactions, and key spectral bands were interpreted using the local interpretable model-agnostic explanations (LIME) algorithm. Compared to other commonly used classifiers such as logistic regression (LR) and random forest (RF), the RFE-GBDT model demonstrated superior performance in liver cancer staging tasks, achieving an accuracy of 92.68% in the five-class classification. The results indicate that the integration of SERS technology with the RFE-GBDT algorithm holds promise as an efficient and non-invasive auxiliary tool for the early diagnosis of liver cancer.

In this study, lysine protected silver nanoclusters (Lys-AgNCs) were prepared and the mimetic property was systematically investigated. The nanoclusters displayed oxidase-like and peroxidase-like activity for the catalytic oxidation of 3, 3', 5, 5'-tetramethylbenzidine (TMB). Interestingly, the nanozyme property could be tuned by adjusting the pH or temperature. In the low pH (3.6-4.2), oxidase-like activity is predominant. In the high pH(4.2-5.4), peroxidase- like activity is the main activity. In the lower temperature (20-40 °C), oxidase-like activity is predominant. Importantly, the Lys-AgNCs showed stronger affinity for TMB. The activation energy of Lys-AgNCs was lower than bare Ag nanoclusters, indicating a faster reaction rate. In addition, a detection method for Hg²⁺ was designed based on the aggregation enhanced peroxidase-like activity of lysine-Ag nanocluster with limit of detection (LOD) of 0.004 μM. The sensing assay exhibited excellent selectivity owing to the specific interaction between lysine and Hg²⁺ and can be used in tap water with an acceptable recovery rate. Considering the cost-effective of this method, it is proposed to be used in practical application.

The quality of red blood cells (RBCs) is crucial in transfusion efficacy and safety, particularly in high-risk patients. In this study, age-related biochemical alterations in stored pRBCs were investigated using a systematic, paired comparison of samples collected from pilot tubes and the main storage bags. The analyses were based on spectroscopic measurements of the isolated supernatant mixture containing RBC-derived metabolites and hemolysis products, instead of intact red blood cells. This proof-of-concept demonstrates that pilot tube samples may not reliably reflect the biochemical state of pRBCs stored in the main bag. Our findings revealed that RBCs stored in pilot tubes undergo accelerated degradation, as indicated by elevated hemoglobin concentration, increased lactate levels, reduced glucose content, and a higher lipid-to-protein ratio. Semiquantitative analysis showed that these markers were elevated by approximately 20-100% by the seventh week of storage compared to those observed in the main blood bags. These consistent trends underscore that pilot tube samples do not reliably reflect the true biochemical quality of pRBCs intended for transfusion. Notably, the study highlights the high diagnostic potential of FTIR and Raman spectroscopies in assessing blood quality in a rapid, non-destructive manner. These techniques offer a promising tool for point-of-care evaluation of RBC integrity directly through the storage bag, enabling improved transfusion decision-making, especially in critical care settings. By directly comparing pilot tube and main bag samples, this study reveals systematic differences in their biochemical profiles and proposes a spectroscopic framework for representative, non-invasive evaluation of pRBC quality.

To address the limitations of conventional and remote-sensing-based water quality monitoring, this study developed a proximal spectral sensing system incorporating 12 configurable spectral bands and coupled it with an adaptive ensemble regression framework to achieve continuous, non-contact retrieval of turbidity (Tur), chlorophyll-a (Chla), chemical oxygen demand (COD), and dissolved oxygen (DO). The proposed approach integrated an abnormal spectral curves removal framework, feature augmentation, and Recursive Feature Elimination with Cross-Validation (RFECV), and constructed a dynamic model pool consisting of heterogeneous base learners, including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Gaussian Process Regression (GPR), and k-Nearest Neighbor Regression (KNNR), together with two ensemble strategies, namely weighted averaging and stacking. The results demonstrated that the optimal models achieve high predictive accuracy on the test set, with coefficients of determination (R²) of 0.988 for Tur, 0.814 for Chla, 0.882 for COD, and 0.833 for DO. Except for COD, the mean absolute percentage errors (MAPE) of the remaining three parameters were all below 10%. Ensemble strategies showed clear advantages for non-optically active parameters, whereas well-tuned tree-based base learners performed better for optically active parameters. In addition, the models maintained strong robustness against Gaussian noise and performed stably with limited training data, effectively tracking both sudden fluctuations and long-term trends in water quality. Overall, this study provides a stable and low-cost solution for high-frequency, continuous surface water quality monitoring.

Total polar materials (TPM) in edible oils consist primarily of degradation products formed through oxidation, hydrolysis, and polymerization during processing or storage. These polar components adversely affect the oil's functional performance, storage stability, and nutritional quality. Leveraging the dual positive correlation between TPM content and the microenvironmental polarity and viscosity of frying oil, a fluorescent probe was designed that exhibited a synergistic enhancement in fluorescence in response to increases in both polarity and viscosity. As the viscosity and polarity increased, the probe exhibited a distinct "off-on" fluorescence response, demonstrating a strong correlation with both factors. This probe was successfully employed to determine the TPM content in frying oils. In experiments using soybean oil (TPM range: 1.1-29.3%) and salad oil (TPM range: 0.1-27.5%), the fluorescence intensity demonstrated excellent linear correlations with TPM content, achieving correlation coefficients of 0.9967 and 0.9940, respectively. These results confirm the probe's capability for quantitative detection. Furthermore, the probe was validated with commercially available oil samples, accurately determining TPM levels even in complex unknown matrices. This demonstrates its potential as a tool for rapid preliminary screening of edible oil quality. This study provides valuable insights and lays the foundation for the development of highly sensitive and efficient dual-response fluorescent probes that target polarity and viscosity.

Carboxylesterase (CES) is a key Phase I metabolic enzyme, and the accurate assessment of its activity is essential for studies in drug metabolism, toxicology, and disease diagnosis. However, many existing fluorescent probes suffer from cross-reactivity with homologous enzymes such as acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). In this study, we designed carbamate as a CES-specific hydrolyzable moiety and conjugated it to the highly photostable naphthalimide fluorophore NIOH via either chlorinated or non-chlorinated self-immolative linkers, thereby developing two ratiometric fluorescent probes, NI-Cl and NI-W. Both probes exhibited negligible responses to AChE and BChE while enabling selective and sensitive ratiometric detection of CES. NI-Cl demonstrated an exceptionally low detection limit of 0.038 U/mL, and NI-W also showed strong performance with a detection limit of 0.048 U/mL. Importantly, NI-Cl successfully visualized endogenous CES activity in different live cells, providing a robust tool for elucidating the physiological and pathological roles of CES.

The individual's physiological system needs to function effectively for a person to maintain life in good health. Deficiency of vitamins and minerals and a structured approach to life tend to trigger variations in the quantity of biological substances, which include hormonal substances, proteins, and neurotransmitters, and threaten the well-being of humans. The most common and the most fundamental catecholamine neurotransmitter present throughout the brain and nervous system is dopamine. The primary nervous system, kidneys, the hormone-sensitive, cardiac, and metabolic functions of humans have each been controlled by how much it is in the body's tissues. Consequently, it would be desirable to come up with an exceptionally sensitive, precise and productive approach that allows for a quick determination of Dopamine levels. Here, we have defined a novel design of Radial Petal-Loop Pattern Refractive Index Biosensor (RPLPRIB) for measurements of dopamine components like Dn1, Dn2, Dn3, Dn4, and Dn5, with refractive index values of 1.256, 1.267, 1.289, 1.291, and 1.309. This structure's petal rings possess a greater efficient area of surface for substrate binding, which enabled greater numbers of dopamine receptors for interaction at the same time, therefore reducing the limit of detection. The outcomes of the RPLPRIB show substantial sensitivity of 1666.66 nm/RIU, and substantial limits of detection 0.000190 RIU, for the concentrations of Dn5 and Dn3, respectively. The substantial quality factor values of 2077.66 and the sensor range value of 2108.78 RIU have been achieved for the concentration of Dn4. The machine learning approach also shows a substantial R-square value of 0.991156 with a low mean-square error. The defined idea represents suitable results that can perfectly help to determine dopamine.