On-line measure of donkey's milk properties by near infrared spectrometry. (June 2016)
- Record Type:
- Journal Article
- Title:
- On-line measure of donkey's milk properties by near infrared spectrometry. (June 2016)
- Main Title:
- On-line measure of donkey's milk properties by near infrared spectrometry
- Authors:
- Altieri, Giuseppe
Genovese, Francesco
Admane, Naouel
Di Renzo, Giovanni Carlo - Abstract:
- Abstract: The ranchers handle donkeys breeding in small livestock farms and there is a need to supply a suitable tool to assess milk characteristics. To this aim, near infrared (NIR) spectroscopy was assessed through nine statistical methods: partial least squares, principal components, multivariate adaptive splines, regression trees using M5′ method, least squares support vector machine, artificial neural network, regression trees, ensemble regression trees and regularized least squares regression. These methods were implemented using a forward sequential feature selection algorithm; their predicting capability was validated with ten-fold cross validation; different types of spectra pre-processing methods were tested. The trials confirmed that NIR analysis is an efficient method for quantitative analysis of protein, lactose and dry matter content of donkey's milk with a full-scale prediction error (FSPERR) of 3.0%, 4.4% and 4.5% respectively. Furthermore, a 16 NIR light emitting diodes (LED) device was investigated using simulation; in this case the prediction models of protein content showed the best FSPERR (3.1%), followed by those of dry matter content (7.0%) whereas those of the lactose content showed the worst (19.8%). These results could be used to design a low cost NIR LED device for the real-time control of donkey's milk in breeding farms. Highlights: Creation of good chemometric models is mandatory for a well performing NIR analysis. NIR analysis is an adequateAbstract: The ranchers handle donkeys breeding in small livestock farms and there is a need to supply a suitable tool to assess milk characteristics. To this aim, near infrared (NIR) spectroscopy was assessed through nine statistical methods: partial least squares, principal components, multivariate adaptive splines, regression trees using M5′ method, least squares support vector machine, artificial neural network, regression trees, ensemble regression trees and regularized least squares regression. These methods were implemented using a forward sequential feature selection algorithm; their predicting capability was validated with ten-fold cross validation; different types of spectra pre-processing methods were tested. The trials confirmed that NIR analysis is an efficient method for quantitative analysis of protein, lactose and dry matter content of donkey's milk with a full-scale prediction error (FSPERR) of 3.0%, 4.4% and 4.5% respectively. Furthermore, a 16 NIR light emitting diodes (LED) device was investigated using simulation; in this case the prediction models of protein content showed the best FSPERR (3.1%), followed by those of dry matter content (7.0%) whereas those of the lactose content showed the worst (19.8%). These results could be used to design a low cost NIR LED device for the real-time control of donkey's milk in breeding farms. Highlights: Creation of good chemometric models is mandatory for a well performing NIR analysis. NIR analysis is an adequate method for the quantitative analysis of donkey's milk. Obtained results could be employed to design a low cost discrete 16 NIR LED device. … (more)
- Is Part Of:
- Lebensmittel-Wissenschaft + Technologie =. Volume 69(2016)
- Journal:
- Lebensmittel-Wissenschaft + Technologie =
- Issue:
- Volume 69(2016)
- Issue Display:
- Volume 69, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 69
- Issue:
- 2016
- Issue Sort Value:
- 2016-0069-2016-0000
- Page Start:
- 348
- Page End:
- 357
- Publication Date:
- 2016-06
- Subjects:
- Regression methods -- NIR spectroscopy -- Discrete NIR LED device
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.2016.01.069 ↗
- 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
British Library HMNTS - ELD Digital store - Ingest File:
- 2269.xml