{"title":"利用多目标特征选择和多任务模型的高光谱成像技术,同时检测不同贮藏条件下黄桃的贮藏条件和贮藏时间","authors":"","doi":"10.1016/j.jfca.2024.106647","DOIUrl":null,"url":null,"abstract":"<div><p>The detection for storage condition and storage time can ensure quality and safety of yellow peaches, and optimize storage management. This study explored the feasibility of simultaneously detecting the storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging technology combined with multi-target characteristic selection and multi-task model. Firstly, a total of 1080 hyperspectral images of 120 yellow peach samples under different storage conditions and different storage time were acquired using visible near-infrared hyperspectral imaging system. Subsequently, Savitzky-Golay first derivative was used to preprocess the raw spectra to improve the signal-to-noise ratio of the spectra. Distinguished index modified competitive adaptive reweighted sampling (DI-CARS) was proposed to select characteristic wavelengths that simultaneously characterize information of storage condition and storage time. Wild horse optimizer optimized support vector machine (WHO-SVM) was proposed for the multi-task modeling analysis. The results showed that multi-target characteristic selection and multi-task model not only reduced 66.67 % in computational cost, but also improved the accuracy, and multi-task WHO-SVM model based on multi-target DI-CARS achieved the optimal result with the overall accuracy reached 98.06 % and the number of characteristic wavelengths being 99. In conclusion, hyperspectral imaging combined with multi-target DI-CARS and multi-task WHO-SVM is feasible to simultaneously detect storage condition and storage time of yellow peach under different storage conditions.</p></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simultaneous detection for storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging with multi-target characteristic selection and multi-task model\",\"authors\":\"\",\"doi\":\"10.1016/j.jfca.2024.106647\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The detection for storage condition and storage time can ensure quality and safety of yellow peaches, and optimize storage management. This study explored the feasibility of simultaneously detecting the storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging technology combined with multi-target characteristic selection and multi-task model. Firstly, a total of 1080 hyperspectral images of 120 yellow peach samples under different storage conditions and different storage time were acquired using visible near-infrared hyperspectral imaging system. Subsequently, Savitzky-Golay first derivative was used to preprocess the raw spectra to improve the signal-to-noise ratio of the spectra. Distinguished index modified competitive adaptive reweighted sampling (DI-CARS) was proposed to select characteristic wavelengths that simultaneously characterize information of storage condition and storage time. Wild horse optimizer optimized support vector machine (WHO-SVM) was proposed for the multi-task modeling analysis. The results showed that multi-target characteristic selection and multi-task model not only reduced 66.67 % in computational cost, but also improved the accuracy, and multi-task WHO-SVM model based on multi-target DI-CARS achieved the optimal result with the overall accuracy reached 98.06 % and the number of characteristic wavelengths being 99. In conclusion, hyperspectral imaging combined with multi-target DI-CARS and multi-task WHO-SVM is feasible to simultaneously detect storage condition and storage time of yellow peach under different storage conditions.</p></div>\",\"PeriodicalId\":15867,\"journal\":{\"name\":\"Journal of Food Composition and Analysis\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Composition and Analysis\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0889157524006811\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Composition and Analysis","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0889157524006811","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Simultaneous detection for storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging with multi-target characteristic selection and multi-task model
The detection for storage condition and storage time can ensure quality and safety of yellow peaches, and optimize storage management. This study explored the feasibility of simultaneously detecting the storage condition and storage time of yellow peach under different storage conditions using hyperspectral imaging technology combined with multi-target characteristic selection and multi-task model. Firstly, a total of 1080 hyperspectral images of 120 yellow peach samples under different storage conditions and different storage time were acquired using visible near-infrared hyperspectral imaging system. Subsequently, Savitzky-Golay first derivative was used to preprocess the raw spectra to improve the signal-to-noise ratio of the spectra. Distinguished index modified competitive adaptive reweighted sampling (DI-CARS) was proposed to select characteristic wavelengths that simultaneously characterize information of storage condition and storage time. Wild horse optimizer optimized support vector machine (WHO-SVM) was proposed for the multi-task modeling analysis. The results showed that multi-target characteristic selection and multi-task model not only reduced 66.67 % in computational cost, but also improved the accuracy, and multi-task WHO-SVM model based on multi-target DI-CARS achieved the optimal result with the overall accuracy reached 98.06 % and the number of characteristic wavelengths being 99. In conclusion, hyperspectral imaging combined with multi-target DI-CARS and multi-task WHO-SVM is feasible to simultaneously detect storage condition and storage time of yellow peach under different storage conditions.
期刊介绍:
The Journal of Food Composition and Analysis publishes manuscripts on scientific aspects of data on the chemical composition of human foods, with particular emphasis on actual data on composition of foods; analytical methods; studies on the manipulation, storage, distribution and use of food composition data; and studies on the statistics, use and distribution of such data and data systems. The Journal''s basis is nutrient composition, with increasing emphasis on bioactive non-nutrient and anti-nutrient components. Papers must provide sufficient description of the food samples, analytical methods, quality control procedures and statistical treatments of the data to permit the end users of the food composition data to evaluate the appropriateness of such data in their projects.
The Journal does not publish papers on: microbiological compounds; sensory quality; aromatics/volatiles in food and wine; essential oils; organoleptic characteristics of food; physical properties; or clinical papers and pharmacology-related papers.