Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage
S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. T...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/11/14/2024 |
| _version_ | 1797433583390949376 |
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| author | Kunshan Yao Jun Sun Jiehong Cheng Min Xu Chen Chen Xin Zhou Chunxia Dai |
| author_facet | Kunshan Yao Jun Sun Jiehong Cheng Min Xu Chen Chen Xin Zhou Chunxia Dai |
| author_sort | Kunshan Yao |
| collection | DOAJ |
| description | S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R<sup>2</sup><sub>P</sub>) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs. |
| first_indexed | 2024-03-09T10:19:03Z |
| format | Article |
| id | doaj.art-29389e3d3a374d30a495bf9212fe4c86 |
| institution | Directory Open Access Journal |
| issn | 2304-8158 |
| language | English |
| last_indexed | 2024-03-09T10:19:03Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj.art-29389e3d3a374d30a495bf9212fe4c862023-12-01T22:08:48ZengMDPI AGFoods2304-81582022-07-011114202410.3390/foods11142024Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during StorageKunshan Yao0Jun Sun1Jiehong Cheng2Min Xu3Chen Chen4Xin Zhou5Chunxia Dai6School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212100, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaS-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R<sup>2</sup><sub>P</sub>) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.https://www.mdpi.com/2304-8158/11/14/2024hyperspectral imagingeggS-ovalbumin contentvisualizationmultivariate analysis |
| spellingShingle | Kunshan Yao Jun Sun Jiehong Cheng Min Xu Chen Chen Xin Zhou Chunxia Dai Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage Foods hyperspectral imaging egg S-ovalbumin content visualization multivariate analysis |
| title | Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage |
| title_full | Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage |
| title_fullStr | Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage |
| title_full_unstemmed | Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage |
| title_short | Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage |
| title_sort | development of simplified models for non destructive hyperspectral imaging monitoring of s ovalbumin content in eggs during storage |
| topic | hyperspectral imaging egg S-ovalbumin content visualization multivariate analysis |
| url | https://www.mdpi.com/2304-8158/11/14/2024 |
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