Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xiaoxin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma - Tanaka, Hajime ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:145 ; year:2023 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.foodcont.2022.109416 |
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ELV059641452 |
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| 245 | 1 | 0 | |a Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken |
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| 520 | |a Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. | ||
| 520 | |a Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. | ||
| 650 | 7 | |a Visualization |2 Elsevier | |
| 650 | 7 | |a Chicken fillets |2 Elsevier | |
| 650 | 7 | |a Visible and near-infrared spectroscopy |2 Elsevier | |
| 650 | 7 | |a Data fusion |2 Elsevier | |
| 650 | 7 | |a Near-infrared spectroscopy |2 Elsevier | |
| 650 | 7 | |a Multiple qualities |2 Elsevier | |
| 700 | 1 | |a Cai, Mingrui |4 oth | |
| 700 | 1 | |a Li, Mengshuang |4 oth | |
| 700 | 1 | |a Wei, Xiaoqun |4 oth | |
| 700 | 1 | |a Liu, Zhen |4 oth | |
| 700 | 1 | |a Wang, Junshu |4 oth | |
| 700 | 1 | |a Jia, Kaiyuan |4 oth | |
| 700 | 1 | |a Han, Yuxing |4 oth | |
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10.1016/j.foodcont.2022.109416 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001990.pica (DE-627)ELV059641452 (ELSEVIER)S0956-7135(22)00609-0 DE-627 ger DE-627 rakwb eng 610 VZ Li, Xiaoxin verfasserin aut Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Visualization Elsevier Chicken fillets Elsevier Visible and near-infrared spectroscopy Elsevier Data fusion Elsevier Near-infrared spectroscopy Elsevier Multiple qualities Elsevier Cai, Mingrui oth Li, Mengshuang oth Wei, Xiaoqun oth Liu, Zhen oth Wang, Junshu oth Jia, Kaiyuan oth Han, Yuxing oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:145 year:2023 pages:0 https://doi.org/10.1016/j.foodcont.2022.109416 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 145 2023 0 |
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10.1016/j.foodcont.2022.109416 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001990.pica (DE-627)ELV059641452 (ELSEVIER)S0956-7135(22)00609-0 DE-627 ger DE-627 rakwb eng 610 VZ Li, Xiaoxin verfasserin aut Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Visualization Elsevier Chicken fillets Elsevier Visible and near-infrared spectroscopy Elsevier Data fusion Elsevier Near-infrared spectroscopy Elsevier Multiple qualities Elsevier Cai, Mingrui oth Li, Mengshuang oth Wei, Xiaoqun oth Liu, Zhen oth Wang, Junshu oth Jia, Kaiyuan oth Han, Yuxing oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:145 year:2023 pages:0 https://doi.org/10.1016/j.foodcont.2022.109416 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 145 2023 0 |
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10.1016/j.foodcont.2022.109416 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001990.pica (DE-627)ELV059641452 (ELSEVIER)S0956-7135(22)00609-0 DE-627 ger DE-627 rakwb eng 610 VZ Li, Xiaoxin verfasserin aut Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Visualization Elsevier Chicken fillets Elsevier Visible and near-infrared spectroscopy Elsevier Data fusion Elsevier Near-infrared spectroscopy Elsevier Multiple qualities Elsevier Cai, Mingrui oth Li, Mengshuang oth Wei, Xiaoqun oth Liu, Zhen oth Wang, Junshu oth Jia, Kaiyuan oth Han, Yuxing oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:145 year:2023 pages:0 https://doi.org/10.1016/j.foodcont.2022.109416 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 145 2023 0 |
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10.1016/j.foodcont.2022.109416 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001990.pica (DE-627)ELV059641452 (ELSEVIER)S0956-7135(22)00609-0 DE-627 ger DE-627 rakwb eng 610 VZ Li, Xiaoxin verfasserin aut Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Visualization Elsevier Chicken fillets Elsevier Visible and near-infrared spectroscopy Elsevier Data fusion Elsevier Near-infrared spectroscopy Elsevier Multiple qualities Elsevier Cai, Mingrui oth Li, Mengshuang oth Wei, Xiaoqun oth Liu, Zhen oth Wang, Junshu oth Jia, Kaiyuan oth Han, Yuxing oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:145 year:2023 pages:0 https://doi.org/10.1016/j.foodcont.2022.109416 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 145 2023 0 |
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10.1016/j.foodcont.2022.109416 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001990.pica (DE-627)ELV059641452 (ELSEVIER)S0956-7135(22)00609-0 DE-627 ger DE-627 rakwb eng 610 VZ Li, Xiaoxin verfasserin aut Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken 2023transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. Visualization Elsevier Chicken fillets Elsevier Visible and near-infrared spectroscopy Elsevier Data fusion Elsevier Near-infrared spectroscopy Elsevier Multiple qualities Elsevier Cai, Mingrui oth Li, Mengshuang oth Wei, Xiaoqun oth Liu, Zhen oth Wang, Junshu oth Jia, Kaiyuan oth Han, Yuxing oth Enthalten in Elsevier Science Tanaka, Hajime ELSEVIER Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma 2022 Amsterdam [u.a.] (DE-627)ELV009139680 volume:145 year:2023 pages:0 https://doi.org/10.1016/j.foodcont.2022.109416 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 145 2023 0 |
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Defining Tumour Shape Irregularity for Preoperative Risk Stratification of Clinically Localised Renal Cell Carcinoma |
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Combining Vis-NIR and NIR hyperspectral imaging techniques with a data fusion strategy for the rapid qualitative evaluation of multiple qualities in chicken |
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Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. |
| abstractGer |
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. |
| abstract_unstemmed |
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are two important indicators for evaluating and controlling the quality and safety of chicken fillets. The aim of this study is to evaluate two hyperspectral imaging techniques (visible near-infrared (Vis-NIR) and NIR) in combination with low-level and intermediate-level fusion strategies (LLF and ILF) for the prediction of multiple quality indicators of chicken fillets stored at 4 °C. Quantitative predictions using partial least squares regression (PLSR) were established after preprocessing and feature wavelength selection. The results showed that the data fusion strategy exhibited better performance in both indicators compared to individual data. The LLF strategy showed optimal performance in predicting the TVC content with an R P 2 of 0.9275 and an RMSEP of 0.1889, while the ILF strategy was best in predicting the TVB-N content with an R P 2 of 0.8652 and an RMSEP of 2.6094. Moreover, the optimal models based on selected bands were used to achieve visual maps of the TVB-N and TVC contents. Although validation with an independent batch of samples was not used, it was a feasibility and valuable study. The experiment results demonstrated that the fusion of two types of hyperspectral data can be successfully used to evaluation of chicken multiple qualities, and provided a potential method for monitoring and detection of meat qualities. |
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