Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry
Sweet potatoes are a substantial source of nutrition and can be added to processed foods in the form of paste. The moisture and starch contents of these potatoes affect the physicochemical properties of sweet potato paste. In this study, the changes in the moisture, starch, and α-amylase content of...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/11/2/135 |
| _version_ | 1797413598471913472 |
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| author | Da-Song Kim Moon-Hee Choi Hyun-Jae Shin |
| author_facet | Da-Song Kim Moon-Hee Choi Hyun-Jae Shin |
| author_sort | Da-Song Kim |
| collection | DOAJ |
| description | Sweet potatoes are a substantial source of nutrition and can be added to processed foods in the form of paste. The moisture and starch contents of these potatoes affect the physicochemical properties of sweet potato paste. In this study, the changes in the moisture, starch, and α-amylase content of sweet potatoes were measured for eight weeks after harvest. Using nondestructive near-infrared analyses and chemometric models, the moisture and starch contents were predicted. The partial least squares (PLS) method was used for prediction, while linear discriminant analysis (LDA) was used for discrimination. To increase the accuracy of the model, the near-infrared spectrum was preprocessed using the Savitzky–Golay derivative (S–G), standard normal variate (SNV), and multiplicative scattering correction methods. When applying PLS to the moisture content, the best calibration model accuracy was obtained using the S–G preprocessed spectrum. Furthermore, the best calibration model accuracy for starch content was obtained using the SNV preprocessed spectrum. The moisture and starch contents were categorized into five classes for LDA, with results indicating that the internal quality of sweet potatoes can be predicted and classified using chemometric models through nondestructive detection. |
| first_indexed | 2024-03-09T05:20:18Z |
| format | Article |
| id | doaj.art-93589ccd5d314ce386423b789463602d |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T05:20:18Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-93589ccd5d314ce386423b789463602d2023-12-03T12:40:56ZengMDPI AGAgriculture2077-04722021-02-0111213510.3390/agriculture11020135Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared SpectrometryDa-Song Kim0Moon-Hee Choi1Hyun-Jae Shin2Department of Chemical Engineering, Graduate School of Chosun University, Gwangju 61452, KoreaDepartment of Biochemical and Polymer Engineering, Chosun University, Gwangju 61452, KoreaDepartment of Chemical Engineering, Graduate School of Chosun University, Gwangju 61452, KoreaSweet potatoes are a substantial source of nutrition and can be added to processed foods in the form of paste. The moisture and starch contents of these potatoes affect the physicochemical properties of sweet potato paste. In this study, the changes in the moisture, starch, and α-amylase content of sweet potatoes were measured for eight weeks after harvest. Using nondestructive near-infrared analyses and chemometric models, the moisture and starch contents were predicted. The partial least squares (PLS) method was used for prediction, while linear discriminant analysis (LDA) was used for discrimination. To increase the accuracy of the model, the near-infrared spectrum was preprocessed using the Savitzky–Golay derivative (S–G), standard normal variate (SNV), and multiplicative scattering correction methods. When applying PLS to the moisture content, the best calibration model accuracy was obtained using the S–G preprocessed spectrum. Furthermore, the best calibration model accuracy for starch content was obtained using the SNV preprocessed spectrum. The moisture and starch contents were categorized into five classes for LDA, with results indicating that the internal quality of sweet potatoes can be predicted and classified using chemometric models through nondestructive detection.https://www.mdpi.com/2077-0472/11/2/135nondestructive techniqueinternal qualitysweet potatoesnear-infrared spectroscopychemometrics |
| spellingShingle | Da-Song Kim Moon-Hee Choi Hyun-Jae Shin Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry Agriculture nondestructive technique internal quality sweet potatoes near-infrared spectroscopy chemometrics |
| title | Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry |
| title_full | Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry |
| title_fullStr | Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry |
| title_full_unstemmed | Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry |
| title_short | Estimation of Starch Hydrolysis in Sweet Potato (<i>Beni Haruka</i>) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry |
| title_sort | estimation of starch hydrolysis in sweet potato i beni haruka i based on storage period using nondestructive near infrared spectrometry |
| topic | nondestructive technique internal quality sweet potatoes near-infrared spectroscopy chemometrics |
| url | https://www.mdpi.com/2077-0472/11/2/135 |
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