Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging
Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitativel...
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/17/8201 |
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| author | Geonwoo Kim Hoonsoo Lee Byoung-Kwan Cho Insuck Baek Moon S. Kim |
| author_facet | Geonwoo Kim Hoonsoo Lee Byoung-Kwan Cho Insuck Baek Moon S. Kim |
| author_sort | Geonwoo Kim |
| collection | DOAJ |
| description | Excessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation. |
| first_indexed | 2024-03-10T08:15:59Z |
| format | Article |
| id | doaj.art-c8669938c6614365b137095a4d24de80 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:15:59Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c8669938c6614365b137095a4d24de802023-11-22T10:23:13ZengMDPI AGApplied Sciences2076-34172021-09-011117820110.3390/app11178201Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral ImagingGeonwoo Kim0Hoonsoo Lee1Byoung-Kwan Cho2Insuck Baek3Moon S. Kim4Department of Bio-Industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, KoreaDepartment of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, Chungdae-ro, Seowon-gu, Cheongju 28644, KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseoung-gu, Daejeon 34134, KoreaEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Rd, Building 303, BARC-East, Beltsville, MD 20705, USAEnvironmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Powder Mill Rd, Building 303, BARC-East, Beltsville, MD 20705, USAExcessive addition of food waste fertilizer to organic fertilizer (OF) is forbidden in the Republic of Korea because of high sodium chloride and capsaicin concentrations in Korean food. Thus, rapid and nondestructive evaluation techniques are required. The objective of this study is to quantitatively evaluate food-waste components (FWCs) using hyperspectral imaging (HSI) in the visible–near-infrared (Vis/NIR) region. A HSI system for evaluating fertilizer components and prediction algorithms based on partial least squares (PLS) analysis and least squares support vector machines (LS-SVM) are developed. PLS and LS-SVM preprocessing methods are employed and compared to select the optimal of two chemometrics methods. Finally, distribution maps visualized using the LS-SVM model are created to interpret the dynamic changes in the OF FWCs with increasing FWC concentration. The developed model quantitively evaluates the OF FWCs with a coefficient of determination of 0.83 between the predicted and actual values. The developed Vis/NIR HIS system and optimized model exhibit high potential for OF FWC discrimination and quantitative evaluation.https://www.mdpi.com/2076-3417/11/17/8201organic fertilizerfood wastehyperspectral imagingpartial least squaressupport vector machine |
| spellingShingle | Geonwoo Kim Hoonsoo Lee Byoung-Kwan Cho Insuck Baek Moon S. Kim Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging Applied Sciences organic fertilizer food waste hyperspectral imaging partial least squares support vector machine |
| title | Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging |
| title_full | Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging |
| title_fullStr | Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging |
| title_full_unstemmed | Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging |
| title_short | Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging |
| title_sort | quantitative evaluation of food waste components in organic fertilizer using visible near infrared hyperspectral imaging |
| topic | organic fertilizer food waste hyperspectral imaging partial least squares support vector machine |
| url | https://www.mdpi.com/2076-3417/11/17/8201 |
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