Application of hyperspectral imaging technology for rapid identification of <i>Ruditapes philippinarum</i> contaminated by heavy metals

Liu, Yao; Qiao, Fu; Wang, Shuwen; Wang, Runtao; Xu, Lele

doi:10.1039/d1ra03664e

RSC Adv.

2021

DOI: 10.1039/d1ra03664e

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Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals

Yao Liu

Fu Qiao

Shuwen Wang

et al.

Abstract: Combined with pattern recognition analysis hyperspectral imaging technology can be used to identify heavy metal contamination in Ruditapes philippinarum rapidly and non-destructively, even with only a small number of training samples.

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“…Hyperspectral imaging technology has attracted much attention due to its non-destructive detection characteristics (Garcia-Martin et al 2020;Liu et al 2021). Selenium-enriched millet and ordinary millet were distinguished based on the hyperspectral imaging technology in the study, which will provide a reference for the efficient identification of selenium-enriched millets.…”

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confidence: 99%

Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology

Zhang¹,

Cui²,

Zhang³

et al. 2022

Czech J. Food Sci.

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To meet rapid and non-destructive identification of selenium-enriched agricultural products selenium-enriched millet and ordinary millet were taken as objects. Image regions of interest (ROI) were selected to extract the spectral average value based on hyperspectral imaging technology. Reducing noise by the Savitzky-Golay (SG) smoothing algorithm, variables were used as inputs that were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), CARS-SPA, UVE-SPA, and UVE-CARS, while sample variables were used as outputs to build support vector machine (SVM) models. The results showed that the accuracy of CARS-SPA-SVM was 100% in the training set and 99.58% in the test set equivalent to that of CARS-SVM and UVE-CARS-SVM, which was higher than that of SPA-SVM, UVE-SPA-SVM, and UVE-SVM. Therefore, the method of CARS-SPA had superiority, and CARS-SPA-SVM was suitable to identify selenium-enriched millet. Finally, 454.57 nm, 484.98 nm, 885.34 nm, and 937.1 nm, which were obtained by wavelength extraction algorithms, were considered as the sensitive wavelengths of selenium information. This study provided a reference for the identification of selenium-enriched agricultural products.

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mentioning

confidence: 99%

Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology

Zhang¹,

Cui²,

Zhang³

et al. 2022

Czech J. Food Sci.

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Detection of mussels contaminated with cadmium by near‐infrared reflectance spectroscopy based on RELS‐TSVM

Liu,

Qiao,

2024

Journal of Food Science

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Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non‐destructive and rapid detection method for Cd‐contaminated mussels based on near‐infrared reflectance spectroscopy was studied. The spectral data of Cd‐contaminated and non‐contaminated mussels were collected in the range of 950–1700 nm. The model based on a robust energy‐based least squares twin support vector machine (RELS‐TSVM) was established to detect Cd‐contaminated mussels. The influence of parameters on the RELS‐TSVM model was analyzed, and the most suitable parameters were determined. The average accuracy of the proposed RELS‐TSVM model in detecting Cd‐contaminated mussels reached 99.92%, which was better than other twin support vector machine‐derived models. For test datasets with different kinds of spectral noises (Gaussian noise, baseline shift, stray light, and wavelength shift), the RELS‐TSVM model had a high robustness for noise disturbance. The results show that near‐infrared spectroscopy combined with the RELS‐TSVM model can realize the detection of Cd‐contaminated mussels, which can provide technical support for the monitoring of heavy metals in shellfish.Practical ApplicationThe method of detecting Cd‐contaminated mussels by the NIRS has important practical significance for ensuring the safety of consumers. It provides a new way for the quality assessment and safety detection of shellfish and provides a technical basis for the marine environment assessment and management.

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Application of hyperspectral imaging technology for rapid identification of Ruditapes philippinarum contaminated by heavy metals

Abstract: Combined with pattern recognition analysis hyperspectral imaging technology can be used to identify heavy metal contamination in Ruditapes philippinarum rapidly and non-destructively, even with only a small number of training samples.

Cited by 2 publications

References 28 publications

Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology

Rapid non-destructive identification of selenium-enriched millet based on hyperspectral imaging technology

Detection of mussels contaminated with cadmium by near‐infrared reflectance spectroscopy based on RELS‐TSVM

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