Quantitative detection of mixed pesticide residue of lettuce leaves based on hyperspectral technique

Yang

et al. 2018

Self Cite

To explore the best method for quantitative detection of moisture content in rice seeds, the total of 120 samples of rice seeds with different moisture content were studied by hyperspectral technique in the experiment. Sensitive wavelengths of moisture were firstly selected by calculating the migration rate, after that successive projections algorithm (SPA) was used to select characteristic wavelengths. The clustering method was proposed to increase the ability of prediction model by increasing the discrimination of hyperspectral eigenvalues of each sample group. Firstly, fuzzy C‐mean clustering (FCM) algorithm was applied to cluster the characteristic wavelengths selected by SPA. Then the prediction model was established by support vector regression (SVR). Due to the unsatisfied clustering effect, simulated annealing genetic algorithm (SAGA) was introduced for clustering. By comparing the results based on original eigenvalues, FCM and SAGA clustering, respectively, it was found that the best method was SAGA. The SAGA‐SVR mode achieved the value with Rp2 of .8892 and RMSEP of 0.0296. The relaxation variable was introduced to reduce interval threshold because the Rp2 was not ideal, and the final value achieved with Rp2 of .9318 and RMSEP of 0.0264. It was proved that the SAGA‐SVR model can be used for moisture detection of rice seeds. Practical applications Rice is widely cultivated around the world, the moisture content of rice seed is an important index for judging the quality of rice seeds. The moisture detection of single rice seed is absolutely feasible, but the moisture content of single rice seed is not representative. Traditional methods cannot make batch detection of moisture content in rice seeds, which are also easy to cause secondary pollution in the process of detection, so they cannot meet the requirements of fine management in modern agricultural. Hyperspectral technology, a safe and effective technique, has been widely used in moisture detection of foods. This study showed that hyperspectral technology can accurately predict moisture content in rice seeds after proper data processing.

Section: Methodsmentioning

confidence: 99%

Quantitative detection of moisture content in rice seeds based on hyperspectral technique

Yang

et al. 2018

Self Cite

“…Take the average value as the reflectance spectral value of the sample. The spectral data had large noises at the beginning and end of the spectra (Sun, Cong, Mao, Wu, & Yang, ). Therefore, data in the first and the last 50 bands were eliminated, so that the bands eventually adopted for analysis ranged from 950 to 1,650 nm.…”

Section: Methodsmentioning

confidence: 99%

SSC prediction of cherry tomatoes based on IRIV‐CS‐SVR model and near infrared reflectance spectroscopy

et al. 2018

As one of the most important indexes of internal quality testing of fruit, soluble solids content (SSC) is significant for its rapid and efficient nondestructive testing by using near infrared reflectance spectroscopy (NIRS). In this article, 126 cherry tomatoes were selected as the research object. Reflectance spectra data of 228 bands in cherry tomatoes were acquired by the near infrared spectrometer and SSC was measured by the hand‐held refractometer. Savitzky–Golay (SG) combined with multiplicative scatter correction (MSC) was used to preprocess the spectral data to reduce the effects of light scattering and other noise. Then, the dimensions of spectral data were reduced by iteratively retaining informative variables (IRIV) algorithm and 10 characteristic wavelengths were obtained, which were 1,080.37, 1,113.62, 1,117.3, 1,297.57, 1,301.02, 1,538.32, 1,540.40, 1,590.72, 1,615.94, and 1,636.89 nm, respectively. Subsequently, support vector regression (SVR) and its two optimization models, PSO‐SVR and CS‐SVR, were respectively used to establish SSC prediction models based on full spectra and characteristic spectra. The experimental results showed the IRIV‐CS‐SVR model for SSC prediction achieved the accuracy with RP2 of 0.9718 and RC2 of 0.9845. Thus, it is feasible to use NIRS with IRIV‐CS‐SVR to make a rapid and efficient nondestructive SSC prediction of cherry tomatoes. Practical applications As one of the important testing standards of fruit internal quality, SSC is of great significance for the rapid and efficient nondestructive testing. In this article, an iteratively retaining information variables (IRIV) algorithm is proposed to extract the characteristic wavelengths, and a regression model CS‐SVR is established by combining the optimization algorithm cuckoo search (CS). This study shows that the model IRIV‐CS‐SVR has a certain effect on SSC prediction of cherry tomatoes.

“…According to GB 2763‐2016, the maximum residue limit of fenvalerate and triazoline in lettuce is 1 and 0.1 mg/kg. However, in the growth of vegetables, pesticide is irrationally configured and excessively used by some farmers, which not only debases the quality of vegetables but also causes great threat to the human body (Sun et al, ; Sun, Cong, Mao, Wu, & Yang, ; Sun, Jin, Mao, & Wu, ). Detecting kinds of pesticide residues is the precondition to determine whether the pesticide concentration exceeds the standard.…”

Section: Introductionmentioning

confidence: 99%

Application of deep brief network in transmission spectroscopy detection of pesticide residues in lettuce leaves

et al. 2019

Self Cite

To realize the quick, nondestructive detection of pesticide residues in lettuce leaves, a new method based on deep brief network (DBN) combined with near‐infrared transmission spectroscopy, was studied in this article. Two kinds of pesticide residues (fenvalerate and triazoline) and distilled water were sprayed on the surface of lettuce leaves, respectively. In addition, near infrared transmission spectroscopy was used for collecting spectral data of 240 lettuce samples. Furthermore, Savitzky–Golay combined with multiplicative scatter correction was used to denoise the raw spectral data. Then, after preprocessing spectral data, DBN was used to extract features and identify kinds of pesticide residues in lettuce leaves. Moreover，successive projection algorithm (SPA) was used to select characteristic wavelengths. After all, support vector machine (SVM), PLS‐DA, and k‐nearest neighbor were carried out to establish classification models based on full spectral data, data extracted by DBN and data extracted by SPA. Consequently, DBN–SVM performed best and the accuracy of training set and test set reached 98.89 and 95.00%, respectively. Hence, the method of near infrared transmission spectroscopy combined with DBN–SVM is practical for the qualitative analysis of pesticide residues in lettuce leaves. Practical applications Using near infrared spectroscopy could detect the kinds of pesticide residues in lettuce leaves quickly and effectively. A new method involving deep brief network (DBN) was proposed to extract the features of spectral data. Results in this study showed that the DBN is feasible and effective for building classification models of different pesticide residues.