Non-destructive detection of adulteration of weight-loss drugs in the field of spectral feature fusion

Jie, Zhaowei; Hou, Xiaolong; Wang, Jifen; Zhang, Wenfang; Zhang, Aolin

doi:10.1016/j.infrared.2023.104591

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…8 Jie et al used terahertz (THz) combined with a least squares support vector machine and an extreme learning machine model to classify and identify weight loss medications. 9 However, these methods may have disadvantages, such as expensive instruments, being limited to certain elements or weak spectral signals that are susceptible to interference from background light.…”

Section: Introductionmentioning

confidence: 99%

Traceability of cold medications with similar ingredients based on laser-induced breakdown spectroscopy

Yao,

Li,

Liu

et al. 2024

J. Anal. At. Spectrom.

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Section: Introductionmentioning

confidence: 99%

Traceability of cold medications with similar ingredients based on laser-induced breakdown spectroscopy

Yao,

Li,

Liu

et al. 2024

J. Anal. At. Spectrom.

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Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Pu,

Yu,

Luo

et al. 2024

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

Zhao,

Xu,

et al. 2024

Agronomy

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To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

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Non-destructive detection of adulteration of weight-loss drugs in the field of spectral feature fusion

Cited by 3 publications

References 20 publications

Traceability of cold medications with similar ingredients based on laser-induced breakdown spectroscopy

Traceability of cold medications with similar ingredients based on laser-induced breakdown spectroscopy

Terahertz spectra reconstructed using convolutional denoising autoencoder for identification of rice grains infested with Sitophilus oryzae at different growth stages

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

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