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红提糖度和硬度的高光谱成像无损检测

Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging

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摘要

红提被誉为世界四大水果之首,因果肉质地坚实、香甜可口、富含多种维生素而受到人们的喜爱[1]。糖作为红提果实中最主要的营养物质,决定着果实的风味,是果实成熟度的衡量标准和重要指标。采后红提的果实质地会不断发生变化,内部组织变软,风味变差,特别是在长距离运输过程中,挤压易造成果粒发生损伤,严重影响红提的品质和价格[2]。而且,损伤的果粒更容易腐烂。水果的硬度是判断果蔬成熟度和贮运品质的一个重要指标,决定了水果的耐贮性和成熟度[3],因此检测红提的糖度和硬度具有重要意义。

Abstract

The sugar content and firmness of red globe grapes are important indicators for evaluating their quality. This study explores nondestructive detection methods and best prediction models for determining the sugar content and firmness of red globe grapes based on hyperspectral imaging technology. The hyperspectral images of 213 samples, in the wavelength range of 400-1000 nm, are collected in three placement orientations (horizontal, fruit stalk-side down, and fruit stalk-side up). The optimal orientation for spectral imaging is compared and analyzed, and subsequently the spectrum is preprocessed in the optimal orientation. Several preprocessing methods, i.e., genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighed sampling (CARS) algorithm, and uninformative variable elimination algorithm (UVE), are applied to the images to extract characteristic wavelengths from the original spectra. Using chemometrics methods, combined with either partial least squares regression (PLSR), least squares support vector machine (LSSVM), and random forest (RF) analysis based on full spectra and characteristic wavelengths, several protocols are established to mathematically predict the sugar content and firmness of red globe grapes from the images. Results show that the sugar and firmness model based on RF performs the best. The optimal model for predicting sugar content proves to be RF optimized by GA (GA-RF), with corrected-set correlation coefficient (Rc) and predicted-set correlation coefficient (Rp) values of 0.969 and 0.928, respectively, and corrected-set root-mean-square error (RMSEC) and predicted-set root-mean-square error (RMSEP) values of 0.266 and 0.254, respectively. The optimal model for predicting firmness proves to be RF optimized by moving-average method and SPA (MA-SPA-RF), with Rc and Rp values of 0.961 and 0.932, respectively, and RMSEC and RMSEP values of 2.119 and 1.634, respectively. These results prove the sugar content and firmness of red globe grapes can be nondestructively predicted via hyperspectral imaging.

Newport宣传-MKS新实验室计划
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DOI:10.3788/AOS201939.1030004

所属栏目:光谱学

基金项目:国家自然科学基金、湖北省自然科学基金、湖北省研究与开发计划项目;

收稿日期:2019-03-26

修改稿日期:2019-07-08

网络出版日期:2019-10-01

作者单位    点击查看

高升:华中农业大学工学院, 湖北 武汉 430070
王巧华:华中农业大学工学院, 湖北 武汉 430070农业部长江中下游农业装备重点实验室, 湖北 武汉 430070
付丹丹:华中农业大学工学院, 湖北 武汉 430070
李庆旭:华中农业大学工学院, 湖北 武汉 430070

联系人作者:王巧华(wqh@mail.hzau.edu.cn)

备注:国家自然科学基金、湖北省自然科学基金、湖北省研究与开发计划项目;

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引用该论文

Sheng Gao,Qiaohua Wang,Dandan Fu,Qingxu Li. Nondestructive Detection of Sugar Content and Firmness of Red Globe Grape by Hyperspectral Imaging[J]. Acta Optica Sinica, 2019, 39(10): 1030004

高升,王巧华,付丹丹,李庆旭. 红提糖度和硬度的高光谱成像无损检测[J]. 光学学报, 2019, 39(10): 1030004

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