Analytical Methods最新文献

Chlorogenic acid (CGA) is an important and abundant bioactive compound, exhibiting various pharmacological properties including antifungal, anti-inflammatory, antioxidant, antiviral, neuroprotective and antispasmodic activities. CGA is available in many types of pharmaceutical products in the form of tablets, capsules, and injections derived from plants. The CGA content is typically regarded as an important indicator for the quality control of these pharmaceutical products. Therefore, it is particularly important to develop a reliable and accurate method for the determination of CGA. In this study, CGA was coupled to bovine serum albumin (BSA) and ovalbumin (OVA) using an active ester method to synthesise artificial antigens. A sensitive and specific monoclonal antibody (mAb) against CGA was obtained. To analyse CGA efficiently, an indirect competitive chemiluminescent enzyme immunoassay (ic-CLEIA) was developed on the basis of the generated mAb. Under optimal conditions, the limit of detection (LOD) and half-maximal inhibitory concentration (IC₅₀) were 1.76 ng mL⁻¹ and 18 ng mL⁻¹, respectively. The linear range was from 2.5–100 ng mL⁻¹, with an R² value of 0.9963. The recoveries of CGA in spiked pharmaceutical products ranged from 77.2% to 118.3%, with coefficients of variation (CVs) ranging from 1.7% to 11.2%. The samples were validated by high-performance liquid chromatography (HPLC) coupled to a UV detector at 327 nm. The ic-CLEIA results showed a high correlation coefficient of 0.9718 when compared with those obtained by HPLC, demonstrating that the proposed ic-CLEIA would be a credible method for the quantification of CGA in pharmaceutical products.

Phthalates (PAEs), which are pollutants that most easily migrate from plastic packages to edible oils, have received increasing attention. In this work, a sample preconditioning method was proposed and explored, involving a single-step extraction using a methanol and ethyl hexanoate mixed solvent, followed by clean-up with a mixed absorbent of graphitic carbon nitride (g-C₃N₄) and N-propyl ethylenediamine (PSA), for the determination of 18 PAEs in oil samples through GC-MS/MS analysis. The triazine ring structure and conjugated aromatic heterocyclic accumulation structure of g-C₃N₄ and the amino-rich structure of PSA can provide hydrogen bonds, conjugated π–π interactions, and hydrophobic effects for the removal of interferences such as organic acids, aliphatic acids, and pigments. To achieve optimal analytical performance, key extraction and purification parameters that may affect the pretreatment efficiency—such as the extraction solution, its volume ratio, extraction temperature, extraction time, and the amount of the clean-up absorbent—were systematically explored and optimized. When applied to edible oil analysis, an excellent linear range from 0.2 to 8.0 mg kg⁻¹ with a preferable coefficient of determination (R² > 0.9989) and satisfactory sensitivity (LOD and LOQ ranging from 0.0002 mg kg⁻¹ to 0.08 mg kg⁻¹ and 0.0004 mg kg⁻¹ to 0.2 mg kg⁻¹, respectively) were obtained. Moreover, a relatively satisfactory recovery varying from 71.4% to 107.9% and the corresponding repeatability ranging from 0.3% to 6.8% were acquired. Furthermore, this work offers a new idea for exploring the application of novel nanocomposites in the pretreatment process.

We developed a novel enzyme cycling method using hypoxanthine-guanine phosphoribosyltransferase (HGPRT) (EC 2.4.2.8) from Hungateiclostridium thermocellum. This method exploits the reversible nature of the HGPRT reaction, catalyzing both forward and reverse reactions in the presence of excess inosine 5′-monophosphate (IMP) and guanine (Gua), similar to the established purine nucleoside phosphorylase (PNP)-based method. Real-time detection was achieved by coupling the reaction with commercially available xanthine dehydrogenase (XDH) (EC 1.17.1.4) in the presence of nicotinamide adenine dinucleotide (NAD⁺). The reaction exhibited high efficiency, with a cycling rate constant of approximately 60 min⁻¹ or higher at an HGPRT concentration of 1 U mL⁻¹. Additionally, we demonstrated a preliminary application of this XDH-coupled enzyme cycling reaction for the determination of pyrophosphate (PPi).

The performance of Raman spectroscopy (RS) has improved in recent years due to the significant advantages of its use in various fields, such as archaeology, medicine, foods, and forensics. An aspect of great relevance in forensic investigation is preserving criminal evidence, and nondestructive techniques for analysis are needed. RS has great potential for characterizing pen inks in questioned documents, and it has even been considered promising for trace intersection and dating investigations. This article reviews the evolution of techniques based on Raman scattering for the identification of pen inks and for their use in the characterization of frauds in documents, compares RS with traditional methods of analysis, and provides data for choosing the most appropriate methodology for ink analysis.

Analyzing forensically relevant body fluids contributes to proving criminal acts, and saliva is often left on the scene, especially in sexual assault cases. Currently, saliva is presumptively identified using its salivary α-amylase activity as an indicator. However, the specificity of saliva presumptive tests is low, and therefore, they cannot confidently prove the presence of saliva. This study aimed to develop and validate an indirect ELISA-based saliva confirmatory test using the novel human salivary protein markers histatin 3 and cystatin D. Histatin 3 and cystatin D are known to be uniquely expressed in human salivary glands, and the molecular evolution of both genes occurred after the divergence of hominoids; thus, the high specificity of human saliva for both markers was expected. As a result of the validation experiments, both histatin 3 and cystatin D markers were able to distinguish human saliva from other human body fluids and animal saliva using appropriate thresholds of the quantitative absorbance values. Our results indicate that both histatin 3 and cystatin D markers are human saliva-specific; thus, we consider that this method will be useful and practical in identifying human saliva.

Quantification of exopolysaccharide (EPS) production in fermentation broth requires solvent precipitation of the polymer, followed by acid or enzymatic hydrolysis, and colorimetric or chromatographic analysis. This lengthy multistep sample preparation and analysis is a major bottleneck in bioprocess monitoring. The development of a nondestructive analytical method requiring minimal sample preparation is warranted. In this study, partial least squares (PLS) regression models were developed to quantify pullulan in cell-free supernatant (PCS) and precipitated pullulan redissolved in distilled water (PDW) from spectral data (204–400 nm). Genetic algorithm, particle swarm optimization, competitive adaptive reweighted sampling, and adaptive bottom-up space exploration strategies were employed to select optimal spectral regions. The full-spectrum model on the PCS (5 latent variables, RMSE_CV: 0.020 g l⁻¹, R_CV²: 0.997) outperformed the PDW (3 latent variables, R_CV²: 0.990). Adaptive bottom-up space exploration achieved the lowest RMSE_CV (0.009 g l⁻¹ for the PCS, 0.027 g l⁻¹ for the PDW), retaining just 16 and 21 spectral variables, respectively. The residual predictive deviation (RPD) for all PLS model variants remains satisfactory (>6.559). The method's limit of detection (0.021 g l⁻¹) was suitable for quantifying pullulan in fermentation broth. The proposed method can be extended to other structurally similar biopolymers where PLS-based soft sensor integration would enable real-time monitoring and bioprocess control.

Metal–organic framework (MOF-818) and nanocomposite (Fe₃O₄@PB-Au) dual nanozymes for enhanced cascade signal amplification were designed. MOF-818 has excellent catechol oxidase mimetic activity and catalyzes the production of color and in situ generation of hydrogen peroxide from the substrate 3,5-di-tert-butylcatechol (3,5-DTBC). Subsequently, Fe₃O₄@PB-Au with peroxidase-like activity catalyzes the generation of reactive oxygen species from hydrogen peroxide, which oxidizes 3,3′,5,5′-tetramethylbenzidine (TMB) to generate the oxidized state of oxTMB, resulting in a signal-enhancing effect. The prepared dual nanozymes can be combined with aptamers with specific recognition ability, thus developing a colorimetric aptamer sensor with high sensitivity and selectivity for detecting Golgi protein 73, which provides a feasible assay for clinical detection of GP73 protein. Detection of GP73 was accomplished by measuring the UV absorption peak at 415 nm. Under the optimal conditions, the concentration of GP73 was linearly correlated with the absorbance in the range of 10.0–100.0 ng mL⁻¹ with a detection limit of 1.83 ng mL⁻¹. The proposed colorimetric biosensor was successfully applied to the determination of GP73 in spiked human serum samples with recoveries of 96.15–100.95% and RSDs of 1.52–6.85%, which demonstrated the great potential of the highly sensitive GP73 assay in clinical detection.

Angelica sinensis is a medicine food homology plant. Its dried root (radix Angelicae sinensis, RAS) is rich in nutrients and bioactive ingredients. This study involved a quantitative analysis of 401 RAS material batches harvested from different geographical regions over a time span of four years. Quantitative statistical distributions of 24 nutrients of interest were determined using ultrahigh performance liquid chromatography-tandem mass spectrometry. The distribution features of 15 target metal elements were specified using inductively coupled plasma-mass spectrometry and atomic absorption spectroscopy methods. A new integrated bioaccessibility response method was introduced to address the statistical analysis of content responses of multiple nutrients through one multivariate compact area. This work not only provides new insights into the compositional ingredients of RAS material to assist in quality control but also helps in understanding the potential benefits of RAS material to human health and increasing its applications.

Pesticides such as cartap play a crucial role in agriculture, as they are extensively used in controlling pests in crops like sugarcane, rice and vegetables. However, their excessive usage poses major threats to human health, causing neurotoxicity and respiratory disorders. In addition, they harm aquatic ecosystems by disrupting biodiversity. Consequently, effective detection and remediation are essential to minimize their adverse effects on human health and the ecosystem. For this purpose, an electrochemical method was used for the simultaneous detection and degradation of the under-reported insecticide cartap. In this work, vertically aligned carbon nanotube arrays were grown and converted into fibers via simple mechanical spinning and used as a flexible electrode material for cartap electro-oxidation. The fabricated electrode exhibited excellent performance towards sensing and remediation without any surface modification. The fiber-based electrode showed a wide linear range, a low detection limit (LOD) of 0.575 mM, high sensitivity (7.98 μA cm⁻² mM⁻¹), reproducibility, and excellent electrocatalytic activity. The multi-functionality of this electrode was attributed to its unique properties like flexibility, compatibility with various media (aqueous and organic), cost-effectiveness, and seamless integration with membranes, making these fibers promising candidates for on-site facile detection and long-term degradation of cartap.

Laser-Induced Breakdown Spectroscopy (LIBS) combined with Artificial Intelligence (AI) offers a powerful method for analyzing and comparing spectral data. This study presents a comparative analysis of conventional and AI-developed methods for processing and interpreting LIBS data, especially in forensic applications, focusing on toner sample discrimination. We propose a novel AI-developed approach that combines normalization, interpolation, and peak detection techniques to simplify LIBS spectral analysis without user preprocessing and easily identify unique spectral features. This method was compared with conventional principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), which are commonly used for LIBS data analysis. The AI-developed method demonstrated superior performance in discriminating between toner samples from various brands and models of printers and photocopiers. The quantitative evaluation of the performance of the AI-developed approach was performed using statistical analysis, including accuracy difference percentage, component-wise variance analysis, paired t-test, and cross-validation test. The results confirmed a significant improvement in accuracy with the AI-developed method compared to conventional approaches. This proposed work highlights the potential of AI in enhancing spectroscopic analysis for forensic applications, offering increased efficiency and accuracy in sample discrimination and classification. Additionally, it accelerates the analysis of LIBS data with no need for user preprocessing.