Detection of pest infestation in stored grain using an electronic nose system optimized for sensor arrays
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| Publicado en: | Journal of Food Measurement & Characterization vol. 19, no. 1 (Jan 2025), p. 439 |
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| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2193-4126 | ||
| 022 | |a 2193-4134 | ||
| 022 | |a 1932-7587 | ||
| 022 | |a 1932-9954 | ||
| 024 | 7 | |a 10.1007/s11694-024-02980-2 |2 doi | |
| 035 | |a 3154284507 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 245 | 1 | |a Detection of pest infestation in stored grain using an electronic nose system optimized for sensor arrays | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Pest infestation during grain storage reduces the weight and quality of the grain, which poses a risk to food safety. It’s important to have a reliable, quick, and intelligent approach for spotting pests in grain storage. In this study, an electronic nose (e-nose) was designed to detect the different densities of Tribolium castaneum (Herbst) in stored wheat. To avoid the phenomenon of “dimensional disaster” caused by the large amount of data in the e-nose data processing, the eigenvalues of the e-nose response curve were extracted to form the original feature matrix for data analysis. Then, to obtain the optimal feature matrix, the initial feature matrix was gradually refined using multivariate statistical methods such as response strength analysis, analysis of variance, coefficient of variation analysis, and correlation analysis. Finally, the feature matrix was regressed using partial least squares regression (PLSR), principal component regression (PCR), support vector machine regression (SVR), and Gaussian process regression (GPR) to establish various prediction models. The GPR model presented the best prediction effect among the four regression models, and its correlation coefficient (R), root mean square error (RMSE), and relative analysis error (RPD) were 0.96, 9.08, and 2.24, respectively. This work provides a feasible optimization method by which the e-nose can be used to detect stored grain pest density within a very small error margin and promotes the development of intelligent agriculture. | |
| 653 | |a Grain | ||
| 653 | |a Humidity | ||
| 653 | |a Data processing | ||
| 653 | |a Matrices (mathematics) | ||
| 653 | |a Regression analysis | ||
| 653 | |a Regression models | ||
| 653 | |a Electronic noses | ||
| 653 | |a Correlation analysis | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Least squares method | ||
| 653 | |a Food quality | ||
| 653 | |a Statistical methods | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Prediction models | ||
| 653 | |a Error detection | ||
| 653 | |a Correlation coefficients | ||
| 653 | |a Correlation coefficient | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Grain storage | ||
| 653 | |a Eigenvalues | ||
| 653 | |a Data analysis | ||
| 653 | |a Coefficient of variation | ||
| 653 | |a Variance analysis | ||
| 653 | |a Pests | ||
| 653 | |a Support vector machines | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Sensors | ||
| 653 | |a Optimization | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Rice | ||
| 653 | |a Sensor arrays | ||
| 653 | |a Gaussian process | ||
| 653 | |a Infestation | ||
| 653 | |a Food safety | ||
| 653 | |a Methods | ||
| 653 | |a Mathematical models | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Journal of Food Measurement & Characterization |g vol. 19, no. 1 (Jan 2025), p. 439 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3154284507/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3154284507/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |