MICROCHEMICAL JOURNAL
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2025
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IF 5.1
Yuling Deng,Meifang Jiang,Qing Liu,Jun Wang,Yi Tao
Ginkgo ester (GBE50) is a well-known botanical drug derived from crude material Ginkgo Folium . Nonetheless, a swift and nondestructive method for full quality inspection on Ginkgo Folium is still lacking. Herein, an interpretable deep learning embedded hyperspectral imaging methodology was established to control the quality of Ginkgo Folium . Hyperspectral images were collected from 102 batches of Ginkgo Folium ranging from 389.8 nm to 1048.2 nm. The contents of three flavonol glycosides and four terpene lactones in Ginkgo Folium were unambiguously determined by well-validated high-performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography-triple quadrupole-mass spectrometry (UHPLC-TQ-MS) methods, whilst the moisture content measured through a moisture meter. In order to realize the real-time full quality inspection, competitive adapative reweighted sampling-partial least squares regression (CARS-PLSR) and seven deep learning algorithms (ResNet50, AlexNet, GoogLeNet, DenseNet121, ResNeXt50, Inception-V4 and Inception-ResNet-V2) were attempted to establish a robust quantitative calibration model embedded hyperspectral imager. As a result, DenseNet121 algorithm showed the most excellent predictive performance. The R p 2 values of quercetin, kaempferol, isorhamnetin, bilobalide, ginkgolide A, ginkgolide B, ginkgolide C, and moisture were determined to be 0.9969, 0.9913, 0.9965, 0.9988, 0.9951, 0.9952, 0.9988 and 0.9981, respectively, and the RMSEP values were 0.0084, 0.0094, 0.0103, 0.0053, 0.0056, 0.0099, 0.0084 and 0.0050, respectively. Grad-CAM was used to unlock the blackbox of DenseNet121model and interpret the model. It’s believed that the established DenseNet121 embedded hyperspectral imager can rapidly and nondestructive determine the eight quality attributes and provide a tool for full quality inspection on Ginkgo Folium .
研究领域:
植物学
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药物分析
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质量控制
✓ 已验证
产品关联
DOI
高影响因子
ACS Sensors
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2025
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IF 9.1
Xiaopeng She,Mengqing Wang,Xinke Jiang,Yi Chen,Cheng Zou,Hongping Liang,Yong Zhou
Ethylene (C2H4) serves as a gaseous biomarker of fruit ripeness during the stages of growth and storage, thus necessitating swift and sensitive detection to assess the evolution of maturity and flavor for target fruits. However, C2H4 vapor is difficult to discern due to its nonpolar inertness and robust molecular structure at room temperature (RT). To this end, a new chemiresistive sensor based on bismuth selenide (Bi2Se3) decorated with zinc oxide (ZnO) nanorods was prepared in this work. Contrary to the high sensor resistance and lack of signal toward C2H4 under dark environments at RT, UV illumination endowed the composite film sensor with low resistance and a noticeable signal. Furthermore, the recovery speed was intriguingly reduced from 10 min to less than 2 min when water vapor was simultaneously introduced. In particular, the optimal 1.0-Bi2Se3/ZnO sensor delivered a response of 38.7% and a response/recovery time of 27/87 s toward 5 ppm of C2H4. Also, the long-term stability was remarkably improved with respect to the pure ZnO counterpart. In addition, excellent linearity across the concentration range from 1 to 20 ppm, good repeatability, and high selectivity over C2H4 were displayed. The effective on-site freshness detection of bananas via the as-prepared 1.0-Bi2Se3/ZnO sensor, by combining electrical signal and visual information demonstrated a huge application potential for fruit ripening monitoring under the synergistic effect of UV illumination and humidity at RT, well catering to the demanding requirements of low power consumption and high miniaturization in the field of future smart sensors.
研究领域:
传感器学
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食品科学
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纳米材料学
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生物医学工程
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环境监测
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智能农业
✓ 已验证
产品关联
DOI
PubMed
高影响因子
FOOD CHEMISTRY
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2023
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IF 8.8
Zhiyang Chen,Yue Sun,Jiyong Shi,Wen Zhang,Xinai Zhang,Xiaowei Hang,Zhihua Li,Xiaobo Zou
The goal of food safety supervision is to directly identify the pesticide residues on the surface of fruits and vegetables. This study proposed to develop a facile, non-destructive, and sensitive method based on surface-enhanced Raman scattering (SERS) to detect non-systemic pesticides on the surface of fruits and vegetables. The composite material was prepared by loading CTAB guided Au@Ag NRs with positive charge onto filter paper which was modified with PDADMAC(+) and PSS(−) using electrostatic adsorption. Au@Ag NRs with bimetallic synergies were effectively adsorbed in the fiber grid to generate 3D SERS hotspots within a few microns of depth. The results showed that the 3D composite flexible substrate had a high SERS activity, great repeatability, and sensitivity when the method was utilized to detect 4-MBA, methyl-parathion, thiram and chlorpyrifos. Three kinds of non-systemic pesticides on the peel could be detected directly and quickly owing to the arbitrary bending of the substrate, demonstrating the efficiency of the SERS “paste-reading” method. The acquired findings demonstrated that PDADMAC/PSS/Au@Ag NRs composite filter paper had the potential to provide rapid feedback for in- situ analysis of pesticide residues on the surface of fruit and vegetable.
研究领域:
食品安全
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分析化学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）