Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue Data Article Swipe
Jingjing Liu
,
Simeng Liu
,
Sze Shin
,
Fulong Liu
,
Tie Lin Shi
,
Chuang Lv
,
Qiao Qi
,
Hairui Fang
,
Wenjuan Jiang
,
Hong Men
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18494/sam.2020.2715
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.18494/sam.2020.2715
Taste is one of the most important criteria for evaluating the quality of apples.In this study, a correlation model using hyperspectral images and quantitative taste information was built for the nondestructive detection of apple taste information.Firstly, the images of 90 sets of Aksu apples were collected by a hyperspectral image system and quantitative values of taste information (sourness and sweetness) were measured using an SA-402B electronic tongue.Secondly, to overcome the difficulties in obtaining the most representative wavelengths, a competitive adaptive reweighted sampling (CARS) algorithm was proposed to remove redundant information in the hyperspectral data.Then, 43 characteristic wavelengths corresponding to sourness and 22 characteristic wavelengths corresponding to sweetness were selected.Finally, particle swarm optimization (PSO) was used to dynamically optimize the kernel parameters and penalty factors of support vector regression (SVR).A PSO-SVR prediction model based on characteristic wavelengths was established.Upon comparing the performance of the prescreening and postscreening models, results showed that the CARS-PSO-SVR model achieved better prediction for apple taste information, for which the correlation coefficients (R 2 ) of sourness and sweetness were 0.81 and 0.887, and the root mean square errors of the prediction set (RMSEP) were 0.03 and 0.018, respectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18494/sam.2020.2715
- https://myukk.org/SM2017/sm_pdf/SM2216.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3027466764
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3027466764Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18494/sam.2020.2715Digital Object Identifier
- Title
-
Detection of Apple Taste Information Using Model Based on Hyperspectral Imaging and Electronic Tongue DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-05-19Full publication date if available
- Authors
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Jingjing Liu, Simeng Liu, Sze Shin, Fulong Liu, Tie Lin Shi, Chuang Lv, Qiao Qi, Hairui Fang, Wenjuan Jiang, Hong MenList of authors in order
- Landing page
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https://doi.org/10.18494/sam.2020.2715Publisher landing page
- PDF URL
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https://myukk.org/SM2017/sm_pdf/SM2216.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://myukk.org/SM2017/sm_pdf/SM2216.pdfDirect OA link when available
- Concepts
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Hyperspectral imaging, Electronic tongue, Taste, Tongue, Computer science, Computer vision, Artificial intelligence, Remote sensing, Medicine, Neuroscience, Psychology, Geography, PathologyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 1, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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