UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality
Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky f...
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MDPI AG
2025-01-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/13/1/21 |
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| author | Mohammad Al Ktash Mona Knoblich Frank Wackenhut Marc Brecht |
| author_facet | Mohammad Al Ktash Mona Knoblich Frank Wackenhut Marc Brecht |
| author_sort | Mohammad Al Ktash |
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| description | Cotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination <i>R</i><sup>2</sup> = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications. |
| format | Article |
| id | doaj-art-379484d728f24e8cb4f42b8e42a56438 |
| institution | Kabale University |
| issn | 2227-9040 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Chemosensors |
| spelling | doaj-art-379484d728f24e8cb4f42b8e42a564382025-01-24T13:26:55ZengMDPI AGChemosensors2227-90402025-01-011312110.3390/chemosensors13010021UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber QualityMohammad Al Ktash0Mona Knoblich1Frank Wackenhut2Marc Brecht3Center of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, GermanyCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, GermanyCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, GermanyCenter of Process Analysis and Technology (PA&T), School of Life Sciences, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, GermanyCotton, the most widely produced natural fiber, is integral to the textile industry and sustains the livelihoods of millions worldwide. However, its quality is frequently compromised by contamination, particularly from honeydew, a substance secreted by insects that leads to the formation of sticky fibers, thereby impeding textile processing. This study investigates ultraviolet (UV) hyperspectral imaging (230–380 nm) combined with multivariate data analysis to detect and quantify honeydew contaminations in real cotton samples. Reference cotton samples were sprayed multiple times with honey solutions to replicate the natural composition of honeydew. Comparisons were made with an alternative method where samples were soaked in sugar solutions of varying concentrations. Principal component analysis (PCA) and quadratic discriminant analysis (QDA) effectively differentiated and classified samples based on honey spraying times. Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination <i>R</i><sup>2</sup> = 0.75 and root mean square error of RMSE = 0.8 for the honey model. By employing a realistic spraying method that closely mimics natural contamination, this study refines sample preparation techniques for improved evaluation of honeydew levels. In conclusion, the integration of hyperspectral imaging with multivariate analysis represents a robust, non-destructive, and rapid approach for real-time detection of honeydew contamination in cotton, offering significant potential for industrial applications.https://www.mdpi.com/2227-9040/13/1/21hyperspectral imagingpushbroomUV spectroscopyprinciple component analysispartial least squares regressionhoneydew |
| spellingShingle | Mohammad Al Ktash Mona Knoblich Frank Wackenhut Marc Brecht UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality Chemosensors hyperspectral imaging pushbroom UV spectroscopy principle component analysis partial least squares regression honeydew |
| title | UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality |
| title_full | UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality |
| title_fullStr | UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality |
| title_full_unstemmed | UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality |
| title_short | UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality |
| title_sort | uv hyperspectral imaging and chemometrics for honeydew detection enhancing cotton fiber quality |
| topic | hyperspectral imaging pushbroom UV spectroscopy principle component analysis partial least squares regression honeydew |
| url | https://www.mdpi.com/2227-9040/13/1/21 |
| work_keys_str_mv | AT mohammadalktash uvhyperspectralimagingandchemometricsforhoneydewdetectionenhancingcottonfiberquality AT monaknoblich uvhyperspectralimagingandchemometricsforhoneydewdetectionenhancingcottonfiberquality AT frankwackenhut uvhyperspectralimagingandchemometricsforhoneydewdetectionenhancingcottonfiberquality AT marcbrecht uvhyperspectralimagingandchemometricsforhoneydewdetectionenhancingcottonfiberquality |