Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit

Wu, Di; Meng, Liuwei; Yang, Liang; Wang, Jingyu; Fu, Xiaping; Du, Xiaoqiang; Li, Shaojia; He, Yong; Huang, Lingxia

doi:10.3390/ijms20082017

Cited by 23 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Decision tree (DT), support vector regression (SVR) and radial basis function neural network (RBFNN) have a strong ability to deal with nonlinear issues. These methods combined with spectral technology have achieved good results in detection of crop diseases [37], determination of crop origins [38,39] and prediction of physiological indexes [40,41]. It confirms the superiority of machine learning in mining the relationship between variables.…”

Section: Introductionmentioning

confidence: 61%

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

et al. 2022

Water

Self Cite

View full text Add to dashboard Cite

Spray droplet size is the main factor affecting the deposition uniformity on a target crop. Studying the influence of multiple factors on the droplet size distribution as well as the evaluation method is of great significance for improving the utilization of pesticides. In this paper, volume median diameter (VMD) and relative span (RS) were selected to evaluate the droplet size distribution under different hollow cone nozzles, flow rates and spatial positions, and the quantitative models of VMD and RS were established based on machine learning methods. The results showed that support vector regression (SVR) had excellent results for VMD (Rc = 0.9974, Rp = 0.9929), while multi-layer perceptron (MLP) had the best effect for RS (Rc = 0.9504, Rp = 0.9537). The correlation coefficient of the prediction set is higher than 0.95, showing the excellent ability of machine learning on predicting the droplet size distribution. In addition, the visualization images of the droplet size distribution were obtained based on the optimal models, which provided intuitive guidance for realizing the uniform distribution of pesticide deposition. In conclusion, this study provides a novel and feasible method for quantitative evaluation of droplet size distribution and offers a theoretical basis for further determining appropriate operation parameters according to the optimal droplet size.

show abstract

Section: Introductionmentioning

confidence: 61%

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

et al. 2022

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…The regression models were established using partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) algorithms, respectively. PLSR is a multivariate statistics-based linear regression method that is widely used to establish regression models [34,35]. The main principle of PLSR is to extract orthogonal factors of latent variables (LVs) and to establish regression relationships between the data set and the corresponding reference values.…”

Section: Multivariate Data Analysis and Model Evaluationmentioning

confidence: 99%

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Cao

Zhang

Lin

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Strawberries are susceptible to mechanical damage. The detection of damaged strawberries by their volatile organic compounds (VOCs) can avoid the deficiencies of manual observation and spectral imaging technologies that cannot detect packaged fruits. In the present study, the detection of strawberries with impact damage is investigated using electronic nose (e-nose) technology. The results show that the e-nose technology can be used to detect strawberries that have suffered impact damage. The best model for detecting the extent of impact damage had a residual predictive deviation (RPD) value of 2.730, and the correct rate of the best model for identifying the damaged strawberries was 97.5%. However, the accuracy of the prediction of the occurrence time of impact was poor, and the RPD value of the best model was only 1.969. In addition, the gas chromatography–mass spectrophotometry analysis further shows that the VOCs of the strawberries changed after suffering impact damage, which was the reason why the e-nose technology could detect the damaged fruit. The above results show that the mechanical force of impact caused changes in the VOCs of strawberries and that it is possible to detect strawberries that have suffered impact damage using e-nose technology.

show abstract

“…In comparison with conventional chemical procedures, such as ICP-OES, LIBS performed rapid analysis, much faster that ICP-OES, thus being an attractive method for real-time analysis of crops. In the same direction, Wu et al [ 40 ] used LIBS aided by Hyperspectral Imaging (HSI) in order to perform fast detection of thiophanate-methyl on the surface of some mulberry fruits, employing PCA and PLSR analysis. Thiophanate-methyl is a fungicide, i.e., a biological chemical compound, often used for exterminating several parasites in a large variety of crops.…”

Section: Libs Applications In Food Analysismentioning

confidence: 99%

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

et al. 2021

View full text Add to dashboard Cite

Laser-Induced Breakdown Spectroscopy (LIBS), having reached a level of maturity during the last few years, is generally considered as a very powerful and efficient analytical tool, and it has been proposed for a broad range of applications, extending from space exploration down to terrestrial applications, from cultural heritage to food science and security. Over the last decade, there has been a rapidly growing sub-field concerning the application of LIBS for food analysis, safety, and security, which along with the implementation of machine learning and chemometric algorithms opens new perspectives and possibilities. The present review intends to provide a short overview of the current state-of-the-art research activities concerning the application of LIBS for the analysis of foodstuffs, with the emphasis given to olive oil, honey, and milk.

show abstract

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit

Cited by 23 publications

References 36 publications

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

Quantitative Analysis of Droplet Size Distribution in Plant Protection Spray Based on Machine Learning Method

Non-Destructive Detection of Damaged Strawberries after Impact Based on Analyzing Volatile Organic Compounds

Laser-Induced Breakdown Spectroscopy: An Efficient Tool for Food Science and Technology (from the Analysis of Martian Rocks to the Analysis of Olive Oil, Honey, Milk, and Other Natural Earth Products)

Contact Info

Product

Resources

About