Bromophenol red, known as chemical indicator, was found to act as a novel potent signal enhancer of the peroxidase-catalyzed luminol–H 2 O 2 chemiluminescent (CL) reaction. It was found interestingly that bovine serum albumin (BSA) played a role in the enhanced chemiluminescent reaction (ECR). The addition of 2.5 mg mL −1 BSA into bromophenol red-enhance CL system showed 36 times stronger CL signal than that without addition of BSA. Mechanism study showed that the luminophors in the ECR were still 3-aminophthalate ion in an excited state (3-APA*). In addition, singlet oxygen ( 1 O 2 ) and hydroxyl radical ( ∙ OH) played a role in the ECR. The possible mechanism was discussed in the present study. The effect of pH, reaction time, and concentration of bromophenol red, BSA, luminol, and H 2 O 2 on CL intensity of the peroxidase-catalyzed CL reaction was studied. The detection limit value (LOD) of HRP and streptavidin-modified HRP in the proposed ECR with bromophenol red and BSA was 0.20 ng mL −1 and 0.05 ng mL −1 , respectively. This novel luminol–H 2 O 2 –HRP–bromophenol red-BSA CL system was applied to the CL detection of sequence-specific DNA based on a magnetic separation process. As low as 0.4 fmol of target DNA could be sensitively detected using the proposed CL system without any amplification process. The obtained results demonstrate very promising perspectives for using bromophenol red and BSA to improve the sensitivity of CL detection of sequence-specific DNA. In addition, this novel ECR system can also be generalized for CL immunoassay , CL western blotting, and so on.
Early detection of Bactrocera dorsalis ( B. dorsalis ) infestation in citrus is challenging due to the asymptomatic nature of initial larval stages. Colorimetric sensor arrays (CSAs) are promising for non-destructive detection of B. dorsalis infestations in citrus, yet they still face limitations in reliably identifying the key volatile organic compounds (VOCs) released by infested citrus. In this study, we developed a MOF-enhanced colorimetric sensor array integrating pH-sensitive dyes into ZIF-7 nanoparticles for non-invasive detection. The array detected VOCs associated with early infestation, including D -limonene, decanal, and linalool, identified via GC-MS. The dye/ZIF-7 composite CSA exhibited enhanced sensitivity compared to the pure dye sensor array. Furthermore, across the 4-day test period, infested samples registered a progressive rise in total Euclidean distance (ED) of color change, reaching a 171.7-unit increase over healthy controls on day 4. To enhance the accuracy and efficiency of infestation detection, we implemented a machine-learning pipeline using Dung Beetle Optimizer (DBO) for feature selection and Tree-structured Parzen Estimator (TPE) for hyperparameter tuning. Consequently, the XGBoost classifier achieved a peak test accuracy of 97.15 %, enabling reliable detection during the early stage of infestation. Overall, this study offers a non-destructive and cost-effective approach for batch-mode early detection of B. dorsalis infestation in citrus, advancing the potential for enhanced postharvest monitoring and pest management strategies.
American ginseng and Asian ginseng products have similar appearance, however, the huge price differences for various species and grades of ginseng lead to the widespread adulteration in the commercial market. We herein reported an efficient colorimetric sensor array for ginseng discrimination through analysis of diols composition (e.g. monosaccharide and ginsenoside). The sensor array was rationally designed based on two mechanisms, changes in pH and indicators displacement assay (IDA), with phenylboronic acid as the receptor to diols. The excellent discrimination capability of the sensor array was validated upon analysis of different species or concentrations of monosaccharides in ginseng hydrolysate. 40 ginseng samples within three categories (cultivated American ginseng, wild American ginseng and Asian ginseng) were successfully classified using the sensor array coupled with a statistical analysis such as hierarchy cluster analysis (HCA) and principal component analysis (PCA). Our work provided a simple and effective method for the fast discrimination and quality control of ginseng products.
危险品化学品经营许可证（带存储）