Wayan Dipasasri Aozora, Achiraya Tantinantrakun, Anthony Keith Thompson, Sontisuk Teerachaichayut
{"title":"近红外高光谱成像技术预测SO2预处理和脱水芒果的品质。","authors":"Wayan Dipasasri Aozora, Achiraya Tantinantrakun, Anthony Keith Thompson, Sontisuk Teerachaichayut","doi":"10.1007/s13197-024-06132-8","DOIUrl":null,"url":null,"abstract":"<p><p>This study tested a non-destructive technique for predicting quality indices of SO<sub>2</sub> pre-treated and dehydrated mangoes. Near-infrared hyperspectral imaging (NIR-HSI), a non-destructive technique, was tested for classifying and predicting total soluble solids (TSS) and sulfur dioxide (SO<sub>2</sub>) content of the treated mangoes. The samples were scanned through a laboratory-based near infrared hyperspectral imaging system within the wavelength range of 935-1720 nm in order to acquire the spectral data. The calibration models were developed for quality indices (dependent variables) by spectral data (independent variables) using partial least square regression (PLSR). Savitzky- Golay smoothing pretreatment was used for creating the calibration model for TSS while the original spectra were used for creating the calibration model for SO<sub>2</sub> content. The models obtained predictive results for TSS and SO<sub>2</sub> content with correlation coefficient of prediction (R<sub>p</sub>) 0.82 and 0.83 respectively and root mean square error of prediction (RMSEP) of 2.42% and 56.40 mg/kg, respectively. The models were used to predict TSS and SO<sub>2</sub> content in each pixel of each SO<sub>2</sub> pre-treated and dehydrated mango image using HSI scanning and interpolated to a linear-color-scale in order to obtain the predictive images, which showed TSS and SO<sub>2</sub> content of SO<sub>2</sub> pre-treated and dehydrated mango by color visualization. The results showed that NIR-HSI could possibly be used to predict TSS and SO<sub>2</sub> content. These factors are important quality indices of SO<sub>2</sub> pre-treated and dehydrated mangoes.</p><p><strong>Graphical abstract: </strong></p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13197-024-06132-8.</p>","PeriodicalId":16004,"journal":{"name":"Journal of Food Science and Technology-mysore","volume":"62 8","pages":"1580-1589"},"PeriodicalIF":2.6000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214093/pdf/","citationCount":"0","resultStr":"{\"title\":\"Near-infrared hyperspectral imaging for predicting the quality of SO<sub>2</sub> pre-treated and dehydrated mango.\",\"authors\":\"Wayan Dipasasri Aozora, Achiraya Tantinantrakun, Anthony Keith Thompson, Sontisuk Teerachaichayut\",\"doi\":\"10.1007/s13197-024-06132-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study tested a non-destructive technique for predicting quality indices of SO<sub>2</sub> pre-treated and dehydrated mangoes. Near-infrared hyperspectral imaging (NIR-HSI), a non-destructive technique, was tested for classifying and predicting total soluble solids (TSS) and sulfur dioxide (SO<sub>2</sub>) content of the treated mangoes. The samples were scanned through a laboratory-based near infrared hyperspectral imaging system within the wavelength range of 935-1720 nm in order to acquire the spectral data. The calibration models were developed for quality indices (dependent variables) by spectral data (independent variables) using partial least square regression (PLSR). Savitzky- Golay smoothing pretreatment was used for creating the calibration model for TSS while the original spectra were used for creating the calibration model for SO<sub>2</sub> content. The models obtained predictive results for TSS and SO<sub>2</sub> content with correlation coefficient of prediction (R<sub>p</sub>) 0.82 and 0.83 respectively and root mean square error of prediction (RMSEP) of 2.42% and 56.40 mg/kg, respectively. The models were used to predict TSS and SO<sub>2</sub> content in each pixel of each SO<sub>2</sub> pre-treated and dehydrated mango image using HSI scanning and interpolated to a linear-color-scale in order to obtain the predictive images, which showed TSS and SO<sub>2</sub> content of SO<sub>2</sub> pre-treated and dehydrated mango by color visualization. The results showed that NIR-HSI could possibly be used to predict TSS and SO<sub>2</sub> content. These factors are important quality indices of SO<sub>2</sub> pre-treated and dehydrated mangoes.</p><p><strong>Graphical abstract: </strong></p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13197-024-06132-8.</p>\",\"PeriodicalId\":16004,\"journal\":{\"name\":\"Journal of Food Science and Technology-mysore\",\"volume\":\"62 8\",\"pages\":\"1580-1589\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214093/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Science and Technology-mysore\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s13197-024-06132-8\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/11 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Science and Technology-mysore","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s13197-024-06132-8","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/11 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Near-infrared hyperspectral imaging for predicting the quality of SO2 pre-treated and dehydrated mango.
This study tested a non-destructive technique for predicting quality indices of SO2 pre-treated and dehydrated mangoes. Near-infrared hyperspectral imaging (NIR-HSI), a non-destructive technique, was tested for classifying and predicting total soluble solids (TSS) and sulfur dioxide (SO2) content of the treated mangoes. The samples were scanned through a laboratory-based near infrared hyperspectral imaging system within the wavelength range of 935-1720 nm in order to acquire the spectral data. The calibration models were developed for quality indices (dependent variables) by spectral data (independent variables) using partial least square regression (PLSR). Savitzky- Golay smoothing pretreatment was used for creating the calibration model for TSS while the original spectra were used for creating the calibration model for SO2 content. The models obtained predictive results for TSS and SO2 content with correlation coefficient of prediction (Rp) 0.82 and 0.83 respectively and root mean square error of prediction (RMSEP) of 2.42% and 56.40 mg/kg, respectively. The models were used to predict TSS and SO2 content in each pixel of each SO2 pre-treated and dehydrated mango image using HSI scanning and interpolated to a linear-color-scale in order to obtain the predictive images, which showed TSS and SO2 content of SO2 pre-treated and dehydrated mango by color visualization. The results showed that NIR-HSI could possibly be used to predict TSS and SO2 content. These factors are important quality indices of SO2 pre-treated and dehydrated mangoes.
Graphical abstract:
Supplementary information: The online version contains supplementary material available at 10.1007/s13197-024-06132-8.
期刊介绍:
The Journal of Food Science and Technology (JFST) is the official publication of the Association of Food Scientists and Technologists of India (AFSTI). This monthly publishes peer-reviewed research papers and reviews in all branches of science, technology, packaging and engineering of foods and food products. Special emphasis is given to fundamental and applied research findings that have potential for enhancing product quality, extend shelf life of fresh and processed food products and improve process efficiency. Critical reviews on new perspectives in food handling and processing, innovative and emerging technologies and trends and future research in food products and food industry byproducts are also welcome. The journal also publishes book reviews relevant to all aspects of food science, technology and engineering.