Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.)
Laver (<i>Pyropia</i> spp.) is a major seaweed that is cultivated and consumed globally. Although quality standards for laver products have been established, traditional physicochemical analyses and sensory evaluations have notable drawbacks regarding rapid-quality inspection. Not all re...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2304-8158/14/3/497 |
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| author | Jong Bong Lee Yeon Joo Bae Ga Yeon Kwon Suk Kyung Sohn Hyo Rim Lee Hyeong Jun Kim Min Jae Kim Ha Eun Park Kil Bo Shim |
| author_facet | Jong Bong Lee Yeon Joo Bae Ga Yeon Kwon Suk Kyung Sohn Hyo Rim Lee Hyeong Jun Kim Min Jae Kim Ha Eun Park Kil Bo Shim |
| author_sort | Jong Bong Lee |
| collection | DOAJ |
| description | Laver (<i>Pyropia</i> spp.) is a major seaweed that is cultivated and consumed globally. Although quality standards for laver products have been established, traditional physicochemical analyses and sensory evaluations have notable drawbacks regarding rapid-quality inspection. Not all relevant physicochemical quality indices, such as texture, are typically evaluated. Therefore, in this study, we investigated the use of hyperspectral imaging to rapidly, accurately, and objectively determine the quality of dried laver. Hyperspectral images of 25 dried laver samples were captured in the short-wave infrared range from 980 to 2576 nm to assess their moisture, protein content, cutting stress, and other key quality indicators. Spectral signatures were analyzed using partial least-squares discriminant analysis (PLS-DA) to correlate the spectral data with three primary quality index values. The performance of PLS-DA was compared with that of the variable importance in projection score and nonlinear regression analysis methods. The comprehensive quality grading model demonstrated accuracies ranging from 96 to 100%, R<sup>2</sup> values from 75 to 92%, and root-mean-square errors from 0.14 to 0.25. These results suggest that the PLS-DA regression model shows great potential for the multivariate analysis of hyperspectral images, serving as an effective quality grading system for dried laver. |
| format | Article |
| id | doaj-art-d8dffaed431b4457bdbaac8924b43291 |
| institution | DOAJ |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d8dffaed431b4457bdbaac8924b432912025-08-20T02:48:02ZengMDPI AGFoods2304-81582025-02-0114349710.3390/foods14030497Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.)Jong Bong Lee0Yeon Joo Bae1Ga Yeon Kwon2Suk Kyung Sohn3Hyo Rim Lee4Hyeong Jun Kim5Min Jae Kim6Ha Eun Park7Kil Bo Shim8Department of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaDepartment of Food Science and Technology, Pukyong National University, Busan 48513, Republic of KoreaLaver (<i>Pyropia</i> spp.) is a major seaweed that is cultivated and consumed globally. Although quality standards for laver products have been established, traditional physicochemical analyses and sensory evaluations have notable drawbacks regarding rapid-quality inspection. Not all relevant physicochemical quality indices, such as texture, are typically evaluated. Therefore, in this study, we investigated the use of hyperspectral imaging to rapidly, accurately, and objectively determine the quality of dried laver. Hyperspectral images of 25 dried laver samples were captured in the short-wave infrared range from 980 to 2576 nm to assess their moisture, protein content, cutting stress, and other key quality indicators. Spectral signatures were analyzed using partial least-squares discriminant analysis (PLS-DA) to correlate the spectral data with three primary quality index values. The performance of PLS-DA was compared with that of the variable importance in projection score and nonlinear regression analysis methods. The comprehensive quality grading model demonstrated accuracies ranging from 96 to 100%, R<sup>2</sup> values from 75 to 92%, and root-mean-square errors from 0.14 to 0.25. These results suggest that the PLS-DA regression model shows great potential for the multivariate analysis of hyperspectral images, serving as an effective quality grading system for dried laver.https://www.mdpi.com/2304-8158/14/3/497hyperspectral imagingquality gradingdried laverpartial least-squares discriminant analysis |
| spellingShingle | Jong Bong Lee Yeon Joo Bae Ga Yeon Kwon Suk Kyung Sohn Hyo Rim Lee Hyeong Jun Kim Min Jae Kim Ha Eun Park Kil Bo Shim Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) Foods hyperspectral imaging quality grading dried laver partial least-squares discriminant analysis |
| title | Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) |
| title_full | Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) |
| title_fullStr | Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) |
| title_full_unstemmed | Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) |
| title_short | Short-Wave Infrared Hyperspectral Image-Based Quality Grading of Dried Laver (<i>Pyropia</i> spp.) |
| title_sort | short wave infrared hyperspectral image based quality grading of dried laver i pyropia i spp |
| topic | hyperspectral imaging quality grading dried laver partial least-squares discriminant analysis |
| url | https://www.mdpi.com/2304-8158/14/3/497 |
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