Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. (November 2016)
- Record Type:
- Journal Article
- Title:
- Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data. (November 2016)
- Main Title:
- Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data
- Authors:
- Cheng, Weiwei
Sun, Da-Wen
Cheng, Jun-Hu - Abstract:
- Abstract: Biogenic amine index (BAI) is a sensitive indicator of meat freshness and quality. This study investigated the use of chemometric methods for analyzing hyperspectral imaging (HSI) data between 400 nm and 1000 nm to rapidly and non-destructively determine BAI values in pork. Partial least square regression (PLSR) model established using full wavelengths showed good results. In order to simplify the calibration model, four new PLSR and multiple linear regression (MLR) models based on the two sets of feature-related wavelengths selected by successive projections algorithm (SPA) and regression coefficients (RC) were built and compared. The optimized simplified model (RC-MLR) yielded excellent results with R 2 P of 0.957 and RMSEP of 4.866 mg/kg, which was thus used to visualize BAI value corresponding to each pixel of the image using pseudo color. In addition, the mechanisms of HSI for BAI determination were discussed. The established models used to determine BAI values were based on physiochemical changes associated with BAI generation in meat rather than direct detection of the BAI contents. The overall results of this study demonstrated that HSI data can be utilized to predict BAI values in pork based on chemometric analysis. Graphical abstract: Highlights: Hyperspectral imaging was used to determine biogenic amine index in pork. Chemometric methods were utilized to analyze hyperspectral imaging data. Calibration model showed good capability for predicting biogenicAbstract: Biogenic amine index (BAI) is a sensitive indicator of meat freshness and quality. This study investigated the use of chemometric methods for analyzing hyperspectral imaging (HSI) data between 400 nm and 1000 nm to rapidly and non-destructively determine BAI values in pork. Partial least square regression (PLSR) model established using full wavelengths showed good results. In order to simplify the calibration model, four new PLSR and multiple linear regression (MLR) models based on the two sets of feature-related wavelengths selected by successive projections algorithm (SPA) and regression coefficients (RC) were built and compared. The optimized simplified model (RC-MLR) yielded excellent results with R 2 P of 0.957 and RMSEP of 4.866 mg/kg, which was thus used to visualize BAI value corresponding to each pixel of the image using pseudo color. In addition, the mechanisms of HSI for BAI determination were discussed. The established models used to determine BAI values were based on physiochemical changes associated with BAI generation in meat rather than direct detection of the BAI contents. The overall results of this study demonstrated that HSI data can be utilized to predict BAI values in pork based on chemometric analysis. Graphical abstract: Highlights: Hyperspectral imaging was used to determine biogenic amine index in pork. Chemometric methods were utilized to analyze hyperspectral imaging data. Calibration model showed good capability for predicting biogenic amine index. The biogenic amine indexes were visualized by the best simplified model. … (more)
- Is Part Of:
- Lebensmittel-Wissenschaft + Technologie =. Volume 73(2016)
- Journal:
- Lebensmittel-Wissenschaft + Technologie =
- Issue:
- Volume 73(2016)
- Issue Display:
- Volume 73, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 73
- Issue:
- 2016
- Issue Sort Value:
- 2016-0073-2016-0000
- Page Start:
- 13
- Page End:
- 19
- Publication Date:
- 2016-11
- Subjects:
- Hyperspectral imaging -- Biogenic amines -- Partial least squares regression -- Multiple linear regression -- Successive projections algorithm
Food industry and trade -- Periodicals
Food -- Composition -- Periodicals
Microbiology -- Periodicals
Nutrition -- Periodicals
664.005 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00236438 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.lwt.2016.05.031 ↗
- Languages:
- English
- ISSNs:
- 0023-6438
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3983.070000
British Library DSC - BLDSS-3PM
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