Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm
Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and me...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Analytical Methods in Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2017/2525147 |
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| _version_ | 1850173102576631808 |
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| author | Lin Zhang Baohua Zhang Jun Zhou Baoxing Gu Guangzhao Tian |
| author_facet | Lin Zhang Baohua Zhang Jun Zhou Baoxing Gu Guangzhao Tian |
| author_sort | Lin Zhang |
| collection | DOAJ |
| description | Uninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability. |
| format | Article |
| id | doaj-art-d143067bb7264983aa5953ee18a8e7c3 |
| institution | OA Journals |
| issn | 2090-8865 2090-8873 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Analytical Methods in Chemistry |
| spelling | doaj-art-d143067bb7264983aa5953ee18a8e7c32025-08-20T02:19:55ZengWileyJournal of Analytical Methods in Chemistry2090-88652090-88732017-01-01201710.1155/2017/25251472525147Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection AlgorithmLin Zhang0Baohua Zhang1Jun Zhou2Baoxing Gu3Guangzhao Tian4College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, ChinaCollege of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, ChinaUninformative biological variability elimination methods were studied in the near-infrared calibration model for predicting the soluble solids content of apples. Four different preprocessing methods, namely, Savitzky-Golay smoothing, multiplicative scatter correction, standard normal variate, and mean normalization, as well as their combinations were conducted on raw Fourier transform near-infrared spectra to eliminate the uninformative biological variability. Subsequently, robust calibration models were established by using partial least squares regression analysis and wavelength selection algorithms. Results indicated that the partial least squares calibration models with characteristic variables selected by CARS method coupled with preprocessing of Savitzky-Golay smoothing and multiplicative scatter correction had a considerable potential for predicting apple soluble solids content regardless of the biological variability.http://dx.doi.org/10.1155/2017/2525147 |
| spellingShingle | Lin Zhang Baohua Zhang Jun Zhou Baoxing Gu Guangzhao Tian Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm Journal of Analytical Methods in Chemistry |
| title | Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm |
| title_full | Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm |
| title_fullStr | Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm |
| title_full_unstemmed | Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm |
| title_short | Uninformative Biological Variability Elimination in Apple Soluble Solids Content Inspection by Using Fourier Transform Near-Infrared Spectroscopy Combined with Multivariate Analysis and Wavelength Selection Algorithm |
| title_sort | uninformative biological variability elimination in apple soluble solids content inspection by using fourier transform near infrared spectroscopy combined with multivariate analysis and wavelength selection algorithm |
| url | http://dx.doi.org/10.1155/2017/2525147 |
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