<scp>D</scp>iscrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra

Sun, Jun; Zhou, Xin; Mao, Hanping; Wu, Xiaohong; Zhang, Xiaodong; Li, Qinglin

doi:10.1111/jfpe.12509

Cited by 34 publications

(26 citation statements)

References 17 publications

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“…A total of 450 samples of lettuce were stored in different plastic bags with different labels and carried to laboratory for polarization spectroscopy collection, after being picked off. In addition, quantitative chemical related experiments was carried out and the results showed that the detection of concentration in each group of pesticide residues in lettuce corresponding to the configuration of each spraying pesticide residues in lettuce (Sun et al, ).…”

Section: Methodsmentioning

confidence: 99%

Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy

Zhou

Sun

et al. 2018

J Food Process Engineering

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In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. There were 90 pieces of lettuce leaves from five different groups, as well as a total of 450 samples of lettuce were used to collect polarization spectral information. CARS, IRIV, and SPA were used to obtain optimal wavelengths. BP neural network, KNN, and SVM were used to establish classification models. The results showed that the significance of these parameters from primary factor to secondary factor were incident zenith angle A, detector polarization angle D, detecting zenith angle B, and sample stage azimuth E, respectively. Besides, optimal level of A, B, D, and E were 60°, 45°, 30°, and 270°, respectively. Furthermore, the best classification model different kinds of pesticide residues in lettuce leaves was CARS‐SVM model, with calibration identification rate of 100%, prediction identification rate of 97.78%. It confirms that polarization spectral detection technology is a feasible and effective method for classifying different pesticide residues in lettuce leaves. Practical applications It is of great significance to understand the effects of pesticide residues on the biological structure and to reveal new biological functions and mechanisms of action. In order to effectively implement the rapid and nondestructive testing of pesticide residues in lettuce leaves, polarization spectral detection technology was used in this article. In addition, the order and superior level of polarization spectral factor were obtained through the calculation of the range using orthogonal test. It confirms that the polarization spectra is a feasible and effective method for discriminating different pesticide residues in lettuce leaves.

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Section: Methodsmentioning

confidence: 99%

Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy

Zhou

Sun

et al. 2018

J Food Process Engineering

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“…It can also be used online in processing lines as a method that requires little or no sample preparation. Some research studies are available in the literature regarding the detection of pesticide residues on a variety of foodstuffs by NIRS, including pesticide residue determination in tomato, green pepper, longan fruit, orange, olive, cucumber, lettuce and propolis samples . These studies demonstrate the utility of NIRS to detect pesticide residues.…”

Section: Introductionmentioning

confidence: 92%

Determination of pesticide residual levels in strawberry (Fragaria) by near‐infrared spectroscopy

2020

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BACKGROUND In this study, an infrared‐based prediction method was developed for easy, fast and non‐destructive detection of pesticide residue levels measured by reference analysis in strawberry (Fragaria × ananassa Duch, cv. Albion) samples using near‐infrared spectroscopy and demonstrating its potential alternative or complementary use instead of traditional pesticide determination methods. Strawberries of Albion variety, which were supplied directly from greenhouses, were used as the study material. A total of 60 batch sample groups, each consisting of eight strawberries, was formed, and each group was treated with a commercial pesticide at different concentrations (26.7% boscalid + 6.7% pyraclostrobin) and varying residual levels were obtained in strawberry batches. The strawberry samples with pesticide residuals were used both to collect near‐infrared spectra and to determine reference pesticide levels, applying QuEChERS (quick, easy, cheap, rugged, safe) extraction, followed by liquid chromatographic–mass spectrometric analysis. RESULTS AND CONCLUSION Partial least squares regression (PLSR) models were developed for boscalid and pyraclostrobin active substances. During model development, the samples were randomly divided into two groups as calibration (n = 48) and validation (n = 12) sets. A calibration model was developed for each active substance, and then the models were validated using cross‐validation and external sets. Performance evaluation of the PLSR models was evaluated based on the residual predictive deviation (RPD) of each model. An RPD of 2.28 was obtained for boscalid, while it was 2.31 for pyraclostrobin. These results indicate that the developed models have reasonable predictive power. © 2019 Society of Chemical Industry

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“…A review of hyperspectral imaging studies for safety and quality of fruits and vegetables has also been recently carried out (Crichton et al, ; Washburn, Stormo, Skjelvareid, & Heia, ). Moreover, due to the wide spectrum range of hyperspectral image technology (Sun et al, ), it is necessary to find a suitable feature selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

Visualization research of moisture content in leaf lettuce leaves based on WT‐PLSR and hyperspectral imaging technology

Zhou

Sun

Mao

et al. 2017

J Food Process Engineering

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Fast and effective visualization research of leaf lettuce leaves was particularly important in modern fine agriculture irrigation. Therefore, hyperspectral imaging technology was used to test the moisture content in leaf lettuce sample. A method involving wavelet transform coupled with partial least squares regression (WT‐PLSR) was proposed to extract characteristic wavelengths, build models, and evaluate characteristic wavelengths. Hyperspectral imaging data of 200 leaf lettuce leaves of five moisture gradients were obtained using hyperspectral imaging instrument. The whole region of leaf lettuce sample was selected as region of interest (ROI) to extract the hyperspectral data using sheffield index, image segment, and masking. To reduce noise interference, Savitzky–Golay (SG) algorithm was used to manage spectral data. Besides, WT‐PLSR algorithm was used to select effective feature wavelengths, build detection model, and evaluate characteristic wavelengths. The best prediction model of moisture content in leaf lettuce sample was PLSR model (RMSEP = 0.1688, Rp2=0.8307) building by characteristic wavelength extracted from fourth layers of wavelet decomposition, and it was applied to achieve the distribution of moisture content in leaf lettuce leaves. Hyperspectral imaging technology coupled with WT‐PLSR algorithm can effectively realize the quantitative detection of moisture content in leaf lettuce sample, and visualizing distribution map of moisture content in leaf lettuce leaves offered a more intuitive and comprehensive assessment of moisture contents. Practical applications Well understanding moisture content in leaf lettuce leaves is very important for revelation of novel biological function and calcium (Ca) content. To facilitate more intuitively and comprehensively detect the moisture content in leaf lettuce leaves, a method involving wavelet transform coupled with partial least squares regression (WT‐PLSR) was proposed to extract characteristic wavelengths, build models, and evaluate characteristic wavelengths. It confirms that the WT‐PLSR algorithm is feasible and effective for building models of different moisture contents in leaf lettuce leaves, and visualizing distribution map of leaf lettuce leaves offered a more intuitive and comprehensive assessment of moisture contents.

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Discrimination of pesticide residues in lettuce based on chemical molecular structure coupled with wavelet transform and near infrared hyperspectra

Cited by 34 publications

References 17 publications

Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy

Study on pesticide residues classification of lettuce leaves based on polarization spectroscopy

Determination of pesticide residual levels in strawberry (Fragaria) by near‐infrared spectroscopy

Visualization research of moisture content in leaf lettuce leaves based on WT‐PLSR and hyperspectral imaging technology

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