Study on detection of soybean components in edible oil with ladder-shape melting temperature isothermal amplification (LMTIA) assay

Gu, Menglin; Xiao, Fugang; Wang, Borui; Zhang, Yaoxuan; Ding, Chen; Zhang, Guozhi; Wang, Deguo

doi:10.1039/d2ay01719a

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Traditional methods for determining soybean crop year include empirical identification and chemical analysis (Lin et al, 2021). Chemical analysis methods can accurately detect the chemical composition and content of soybeans (Menglin et al, 2023). However, these methods are cumbersome to operate, can damage samples, pollute the environment, are costly, and cannot be used for large-scale detection.…”

Section: Introductionmentioning

confidence: 99%

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

He,

Kang,

Yin

et al. 2023

Eng. Agríc.

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In soybeans from different years, the infrared spectroscopy waveforms exhibit similarities, yet variations in aging time lead to significant differences in their fatty acid content. To rapidly discern the year of soybean production, this study employed ten feature extraction algorithms in conjunction with Convolutional Neural Networks (CNN) to establish models for classifying soybeans from different years (2019, 2020, and 2021). The research findings reveal the following: (1) The CNN models, after feature extraction, all demonstrated improved accuracy. Notably, the optimal models for both powdered and granulated states were the Kpca-CNN models, achieving an accuracy of 100%. The corresponding loss function values were 0.0002 and 0.0007, with processing times of 0.19 seconds and 0.20 seconds, respectively. (2) The modeling results of all models suggest that the classification accuracy of soybean powder spectral data is higher compared to soybean particle spectral data. (3) Validation of the optimal Kpca-CNN model confirmed its consistent accuracy of 100% when introducing new data or reducing the size of the training dataset. In conclusion, the fusion of near-infrared spectroscopy analysis and CNN technology is considered an effective method for classifying soybeans from different years. This method provides a practical solution for the rapid and precise determination of seed production years.

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Section: Introductionmentioning

confidence: 99%

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

He,

Kang,

Yin

et al. 2023

Eng. Agríc.

View full text Add to dashboard Cite

show abstract

Comparative study on the target sequences of thaumatin-like protein (TLP) and internal transcribed spacer (ITS) in the detection of corn component in edible oil by ladder-shape melting temperature isothermal amplification (LMTIA)

Xiao,

Zhang,

et al. 2024

LWT

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Rapid and duplex detection of sweetpotato and maize components in starch products using Proofman-LMTIA method

Cui,

Hu,

Guo

et al. 2024

Microchemical Journal

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Study on detection of soybean components in edible oil with ladder-shape melting temperature isothermal amplification (LMTIA) assay

Abstract: A ladder-shape melting temperature isothermal amplification (LMTIA) assay was established and used to detect soy components in edible oils. The LMTIA primers were designed with the sequence of the internal...

Cited by 6 publications

References 27 publications

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

CNN Classification of Soybeans With Storage Time Based on Near Infrared Spectroscopy

Comparative study on the target sequences of thaumatin-like protein (TLP) and internal transcribed spacer (ITS) in the detection of corn component in edible oil by ladder-shape melting temperature isothermal amplification (LMTIA)

Rapid and duplex detection of sweetpotato and maize components in starch products using Proofman-LMTIA method

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