Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon
The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/ne...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2018/7450695 |
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| author | Ting Wu Nan Zhong Ling Yang |
| author_facet | Ting Wu Nan Zhong Ling Yang |
| author_sort | Ting Wu |
| collection | DOAJ |
| description | The cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products. |
| format | Article |
| id | doaj-art-8ca9afaad1384938a7247ec2e0fcc881 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-8ca9afaad1384938a7247ec2e0fcc8812025-02-03T07:25:20ZengWileyJournal of Spectroscopy2314-49202314-49392018-01-01201810.1155/2018/74506957450695Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of SalmonTing Wu0Nan Zhong1Ling Yang2College of Engineering, South China Agricultural University, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaSchool of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, ChinaThe cold storage time of salmon has a significant impact on its freshness, which is an important factor for consumers to evaluate the quality of salmon. The efficient, accurate, and convenient protocol is urgent to appraise the freshness for quality checking. In this paper, the ability of visible/near-infrared (VIS/NIR) spectroscopy was evaluated to predict the cold storage time of salmon meat and skin, which were stored at low-temperature box for 0~12 days. Meanwhile, a double-layer stacked denoising autoencoder neural network (SDAE-NN) algorithm was introduced to establish the prediction model without spectral pre-preprocessing. The results showed that, compared with the common methods such as partial least squares regression (PLSR) and back propagation neural network (BP-NN), the SDAE-NN method had a better performance due to its high efficiency in decreasing noise and optimizing the initial weights. The determination coefficient of test sets (R2test) and root mean square error of test sets (RMSEP) have been calculated based on SDAE-NN, for the salmon meat (skin), the R2test can reach 0.98 (0.92), and the RMSEP can reach 0.93 (1.75), respectively. It is highlighted that the algorithm is efficient and accurate and that the salmon meat would be more suitable for predicting freshness than the salmon skin. VIS/NIR spectroscopy combined with the SDAE-NN algorithm can be widely used to predict the freshness of various agricultural products.http://dx.doi.org/10.1155/2018/7450695 |
| spellingShingle | Ting Wu Nan Zhong Ling Yang Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon Journal of Spectroscopy |
| title | Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon |
| title_full | Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon |
| title_fullStr | Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon |
| title_full_unstemmed | Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon |
| title_short | Application of VIS/NIR Spectroscopy and SDAE-NN Algorithm for Predicting the Cold Storage Time of Salmon |
| title_sort | application of vis nir spectroscopy and sdae nn algorithm for predicting the cold storage time of salmon |
| url | http://dx.doi.org/10.1155/2018/7450695 |
| work_keys_str_mv | AT tingwu applicationofvisnirspectroscopyandsdaennalgorithmforpredictingthecoldstoragetimeofsalmon AT nanzhong applicationofvisnirspectroscopyandsdaennalgorithmforpredictingthecoldstoragetimeofsalmon AT lingyang applicationofvisnirspectroscopyandsdaennalgorithmforpredictingthecoldstoragetimeofsalmon |