Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice
Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Deng, Fei [verfasserIn] Lu, Hui [verfasserIn] Yuan, Yujie [verfasserIn] Chen, Hong [verfasserIn] Li, Qiuping [verfasserIn] Wang, Li [verfasserIn] Tao, Youfeng [verfasserIn] Zhou, Wei [verfasserIn] Cheng, Hong [verfasserIn] Chen, Yong [verfasserIn] Lei, Xiaolong [verfasserIn] Li, Guiyong [verfasserIn] Li, Min [verfasserIn] Ren, Wanjun [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Food chemistry - New York, NY [u.a.] : Elsevier, 1976, 407 |
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| Übergeordnetes Werk: |
volume:407 |
| DOI / URN: |
10.1016/j.foodchem.2022.135176 |
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| Katalog-ID: |
ELV009008748 |
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| 245 | 1 | 0 | |a Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice |
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| 520 | |a Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. | ||
| 650 | 4 | |a Artificial neural networks | |
| 650 | 4 | |a Eating and cooking quality | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a Rice | |
| 650 | 4 | |a Texture properties | |
| 700 | 1 | |a Lu, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Yujie |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Qiuping |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Youfeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Lei, Xiaolong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Guiyong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Wanjun |e verfasserin |0 (orcid)0000-0003-0985-8399 |4 aut | |
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10.1016/j.foodchem.2022.135176 doi (DE-627)ELV009008748 (ELSEVIER)S0308-8146(22)03138-7 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl Deng, Fei verfasserin aut Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties Lu, Hui verfasserin aut Yuan, Yujie verfasserin aut Chen, Hong verfasserin aut Li, Qiuping verfasserin aut Wang, Li verfasserin aut Tao, Youfeng verfasserin aut Zhou, Wei verfasserin aut Cheng, Hong verfasserin aut Chen, Yong verfasserin aut Lei, Xiaolong verfasserin aut Li, Guiyong verfasserin aut Li, Min verfasserin aut Ren, Wanjun verfasserin (orcid)0000-0003-0985-8399 aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 407 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:407 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ AR 407 |
| spelling |
10.1016/j.foodchem.2022.135176 doi (DE-627)ELV009008748 (ELSEVIER)S0308-8146(22)03138-7 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl Deng, Fei verfasserin aut Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties Lu, Hui verfasserin aut Yuan, Yujie verfasserin aut Chen, Hong verfasserin aut Li, Qiuping verfasserin aut Wang, Li verfasserin aut Tao, Youfeng verfasserin aut Zhou, Wei verfasserin aut Cheng, Hong verfasserin aut Chen, Yong verfasserin aut Lei, Xiaolong verfasserin aut Li, Guiyong verfasserin aut Li, Min verfasserin aut Ren, Wanjun verfasserin (orcid)0000-0003-0985-8399 aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 407 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:407 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ AR 407 |
| allfields_unstemmed |
10.1016/j.foodchem.2022.135176 doi (DE-627)ELV009008748 (ELSEVIER)S0308-8146(22)03138-7 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl Deng, Fei verfasserin aut Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties Lu, Hui verfasserin aut Yuan, Yujie verfasserin aut Chen, Hong verfasserin aut Li, Qiuping verfasserin aut Wang, Li verfasserin aut Tao, Youfeng verfasserin aut Zhou, Wei verfasserin aut Cheng, Hong verfasserin aut Chen, Yong verfasserin aut Lei, Xiaolong verfasserin aut Li, Guiyong verfasserin aut Li, Min verfasserin aut Ren, Wanjun verfasserin (orcid)0000-0003-0985-8399 aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 407 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:407 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ AR 407 |
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10.1016/j.foodchem.2022.135176 doi (DE-627)ELV009008748 (ELSEVIER)S0308-8146(22)03138-7 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl Deng, Fei verfasserin aut Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties Lu, Hui verfasserin aut Yuan, Yujie verfasserin aut Chen, Hong verfasserin aut Li, Qiuping verfasserin aut Wang, Li verfasserin aut Tao, Youfeng verfasserin aut Zhou, Wei verfasserin aut Cheng, Hong verfasserin aut Chen, Yong verfasserin aut Lei, Xiaolong verfasserin aut Li, Guiyong verfasserin aut Li, Min verfasserin aut Ren, Wanjun verfasserin (orcid)0000-0003-0985-8399 aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 407 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:407 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ AR 407 |
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10.1016/j.foodchem.2022.135176 doi (DE-627)ELV009008748 (ELSEVIER)S0308-8146(22)03138-7 DE-627 ger DE-627 rda eng 540 660 VZ 58.34 bkl Deng, Fei verfasserin aut Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties Lu, Hui verfasserin aut Yuan, Yujie verfasserin aut Chen, Hong verfasserin aut Li, Qiuping verfasserin aut Wang, Li verfasserin aut Tao, Youfeng verfasserin aut Zhou, Wei verfasserin aut Cheng, Hong verfasserin aut Chen, Yong verfasserin aut Lei, Xiaolong verfasserin aut Li, Guiyong verfasserin aut Li, Min verfasserin aut Ren, Wanjun verfasserin (orcid)0000-0003-0985-8399 aut Enthalten in Food chemistry New York, NY [u.a.] : Elsevier, 1976 407 Online-Ressource (DE-627)300898509 (DE-600)1483647-6 (DE-576)098330225 1873-7072 nnns volume:407 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 58.34 Lebensmitteltechnologie VZ AR 407 |
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Deng, Fei @@aut@@ Lu, Hui @@aut@@ Yuan, Yujie @@aut@@ Chen, Hong @@aut@@ Li, Qiuping @@aut@@ Wang, Li @@aut@@ Tao, Youfeng @@aut@@ Zhou, Wei @@aut@@ Cheng, Hong @@aut@@ Chen, Yong @@aut@@ Lei, Xiaolong @@aut@@ Li, Guiyong @@aut@@ Li, Min @@aut@@ Ren, Wanjun @@aut@@ |
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540 660 VZ 58.34 bkl Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice Artificial neural networks Eating and cooking quality Prediction model Rice Texture properties |
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accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice |
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Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice |
| abstract |
Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. |
| abstractGer |
Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. |
| abstract_unstemmed |
Accurate prediction of the eating and cooking quality (ECQ) of rice is of great importance. Statistical and machine learning models were developed to predict the overall acceptability of cooked rice. The results showed that the models developed using stepwise multiple linear regression, principal component analysis plus multiple linear regression, partial least square regression, k-nearest neighbor, random forest, and gradient boosted decision tree had determination coefficients (R2) of 0.156–0.452, 0.357, 0.160–0.460, 0.192–0.746, 0.453–0.708, and 0.469–0.880, respectively, which were improved to 0.675–0.979 by artificial neural networks (ANN) models. The ANN models also had lower root mean square errors (0.574–1.32). Further, the ANN model using textural properties could accurately predict 92.1 % of overall acceptability, which could be improved to >96 % using the components and/or pasting characteristics. Overall, the accuracy of ECQ prediction was substantially improved by the model developed using ANN with texture properties of rice. |
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Accurate prediction of the eating and cooking quality of rice using artificial neural networks and the texture properties of cooked rice |
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Lu, Hui Yuan, Yujie Chen, Hong Li, Qiuping Wang, Li Tao, Youfeng Zhou, Wei Cheng, Hong Chen, Yong Lei, Xiaolong Li, Guiyong Li, Min Ren, Wanjun |
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| score |
7.398486 |