Citation
Vichasilp C., . and Kawano S., . Prediction of starch content in meatballs using near infrared spectroscopy (NIRS). pp. 1501-1506. ISSN 22317546
Abstract
Meatballs are a popular food in Asian countries. A good quality consists of low starch. In this study the quality of meatballs was evaluated by starch content using short and long-wavelength near infrared spectroscopy (NIRS). The result found that long-wavelength NIRS can predict starch contents in all kinds of meatballs. The model of beef meatballs showed a high coefficient of multiple determination of validation set (R2-val) of 0.97 and a low standard error of cross-validation (SECV) of 2.64; the chicken meatballs model had an R2-val of 0.97 and a SECV of 2.63; and the pork meatballs model had an R2-val of 0.98 and a SECV of 2.37. In addition a universal model was created by combining the spectra of all meatballs. The universal model had an coefficient of multiple determination of calibration set (R2-cal) of 0.98 standard error of calibration (SEC) of 2.22 R2-val of 0.97 standard error of prediction (SEP) of 2.67 and Bias of 0.05. The results indicated that NIRS can predict starch contents with high accuracy and could apply for quality classification via rapid analysis.
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Abstract
Meatballs are a popular food in Asian countries. A good quality consists of low starch. In this study the quality of meatballs was evaluated by starch content using short and long-wavelength near infrared spectroscopy (NIRS). The result found that long-wavelength NIRS can predict starch contents in all kinds of meatballs. The model of beef meatballs showed a high coefficient of multiple determination of validation set (R2-val) of 0.97 and a low standard error of cross-validation (SECV) of 2.64; the chicken meatballs model had an R2-val of 0.97 and a SECV of 2.63; and the pork meatballs model had an R2-val of 0.98 and a SECV of 2.37. In addition a universal model was created by combining the spectra of all meatballs. The universal model had an coefficient of multiple determination of calibration set (R2-cal) of 0.98 standard error of calibration (SEC) of 2.22 R2-val of 0.97 standard error of prediction (SEP) of 2.67 and Bias of 0.05. The results indicated that NIRS can predict starch contents with high accuracy and could apply for quality classification via rapid analysis.
Additional Metadata
| Item Type: | Article |
|---|---|
| AGROVOC Term: | Processed foods |
| AGROVOC Term: | Beef |
| AGROVOC Term: | Pork |
| AGROVOC Term: | Chicken meat |
| AGROVOC Term: | Starch |
| AGROVOC Term: | Infrared spectrophotometry |
| AGROVOC Term: | Food quality |
| AGROVOC Term: | Temperature profile |
| AGROVOC Term: | Polysaccharides |
| AGROVOC Term: | Food science |
| Depositing User: | Ms. Suzila Mohamad Kasim |
| Last Modified: | 24 Apr 2025 06:27 |
| URI: | http://webagris.upm.edu.my/id/eprint/22143 |
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