将高光谱成像技术和可见光-近红外光谱技术与数据融合策略相结合,检测苹果中的可溶性固形物含量

IF 4 2区 农林科学 Q2 CHEMISTRY, APPLIED
Yi Lin , Rongsheng Fan , Youli Wu , Chunyi Zhan , Rui Qing , Kunyu Li , Zhiliang Kang
{"title":"将高光谱成像技术和可见光-近红外光谱技术与数据融合策略相结合,检测苹果中的可溶性固形物含量","authors":"Yi Lin ,&nbsp;Rongsheng Fan ,&nbsp;Youli Wu ,&nbsp;Chunyi Zhan ,&nbsp;Rui Qing ,&nbsp;Kunyu Li ,&nbsp;Zhiliang Kang","doi":"10.1016/j.jfca.2024.106996","DOIUrl":null,"url":null,"abstract":"<div><div>Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.</div></div>","PeriodicalId":15867,"journal":{"name":"Journal of Food Composition and Analysis","volume":"137 ","pages":"Article 106996"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining hyperspectral imaging technology and visible-near infrared spectroscopy with a data fusion strategy for the detection of soluble solids content in apples\",\"authors\":\"Yi Lin ,&nbsp;Rongsheng Fan ,&nbsp;Youli Wu ,&nbsp;Chunyi Zhan ,&nbsp;Rui Qing ,&nbsp;Kunyu Li ,&nbsp;Zhiliang Kang\",\"doi\":\"10.1016/j.jfca.2024.106996\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an <span><math><msubsup><mrow><mi>R</mi></mrow><mrow><mi>p</mi></mrow><mrow><mn>2</mn></mrow></msubsup></math></span> of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.</div></div>\",\"PeriodicalId\":15867,\"journal\":{\"name\":\"Journal of Food Composition and Analysis\",\"volume\":\"137 \",\"pages\":\"Article 106996\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-11-19\",\"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/S0889157524010305\",\"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/S0889157524010305","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}
引用次数: 0

摘要

可溶性固形物含量(SSC)是评估苹果质量的一项重要指标。本研究旨在评估两种光谱技术(高光谱成像(HSI)和可见光-近红外(Vis-NIR))与低级和中级融合策略(LLF 和 MLF)相结合用于检测苹果中的 SSC。首先,采用基线校正(BC)、去趋势(DT)、乘散校正(MSC)、萨维茨基-戈莱(SG)和标准正态变量(SNV)对光谱数据进行预处理。其次,遗传算法(GA)、竞争性自适应加权采样(CARS)及其组合被用于特征变量提取。最后,使用偏最小二乘回归(PLSR)为光谱数据建立模型。结果表明,如果只考虑单一的光谱技术,Vis-NIR 在预测苹果 SSC 方面比 HSI 更有优势。与单个光谱数据相比,数据融合策略在预测 SSC 指标方面表现更好。其中,LLF 策略在预测 SSC 方面表现最佳,Rp2 为 0.927,RMSEP 为 0.529 °Brix,AIC 为 332.96。此外,还使用了基于 HSI 数据的最佳模型来绘制 SSC 可视图。结果表明,融合 HSI 和可见光-近红外数据为检测苹果中的 SSC 提供了一种可行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining hyperspectral imaging technology and visible-near infrared spectroscopy with a data fusion strategy for the detection of soluble solids content in apples
Soluble solids content (SSC) is an important indicator for evaluating apple quality. This study aimed to assess two spectral techniques (hyperspectral imaging (HSI) and visible-near infrared (Vis-NIR)) combined with low-level and mid-level fusion strategies (LLF and MLF) for the detection of SSC in apples. Firstly, baseline correction (BC), detrending (DT), multiplicative scatter correction (MSC), savitzky-golay (SG), and standard normal variables (SNV) were used for the preprocessing of spectral data. Secondly, genetic algorithm (GA), competitive adaptive reweighted sampling (CARS), and their combinations were used for feature variable extraction. Finally, models were developed using partial least squares regression (PLSR) for spectral data. The results showed that Vis-NIR has an advantage over HSI in predicting apple SSC if only a single spectral technique was considered. The data fusion strategy showed better performance in predicting SSC metrics compared to individual spectral data. Among them, the LLF strategy showed the best performance in predicting SSC, with an Rp2 of 0.927, an RMSEP of 0.529 °Brix, and an Akike information criterion (AIC) of 332.96. In addition, an optimal model based on HSI data was used to achieve visual maps of SSC. It was demonstrated that the fusion of HSI and Vis-NIR data provided a promising method for detecting the SSC in apples.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Food Composition and Analysis
Journal of Food Composition and Analysis 工程技术-食品科技
CiteScore
6.20
自引率
11.60%
发文量
601
审稿时长
53 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信