Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy
The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for mois...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/9986940 |
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| _version_ | 1832546051572105216 |
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| author | Dan Peng Yali Liu Jiasheng Yang Yanlan Bi Jingnan Chen |
| author_facet | Dan Peng Yali Liu Jiasheng Yang Yanlan Bi Jingnan Chen |
| author_sort | Dan Peng |
| collection | DOAJ |
| description | The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set. |
| format | Article |
| id | doaj-art-3d612a77e318428d80a5e4d3f943bdac |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-3d612a77e318428d80a5e4d3f943bdac2025-02-03T07:24:01ZengWileyJournal of Spectroscopy2314-49202314-49392021-01-01202110.1155/2021/99869409986940Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance SpectroscopyDan Peng0Yali Liu1Jiasheng Yang2Yanlan Bi3Jingnan Chen4College of Food Science and Technology, Henan University of Technology, Lianhua Road 100, Zhengzhou 450001, Henan Province, ChinaCollege of Food Science and Technology, Henan University of Technology, Lianhua Road 100, Zhengzhou 450001, Henan Province, ChinaCollege of Food Science and Technology, Henan University of Technology, Lianhua Road 100, Zhengzhou 450001, Henan Province, ChinaCollege of Food Science and Technology, Henan University of Technology, Lianhua Road 100, Zhengzhou 450001, Henan Province, ChinaCollege of Food Science and Technology, Henan University of Technology, Lianhua Road 100, Zhengzhou 450001, Henan Province, ChinaThe rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20 subbands to screen specific wavelengths relevant to the walnut kernel moisture content. PLS (partial least square regression), MLR (multivariate linear regression), PCR (principle component regression), and SVR (support vector regression) were used to establish the relationship model between the spectral data and measurement values of the moisture content. In comparison, the optimized modeling conditions were determined as follows: detection wavelength 1349–1490 nm, SNV-FD (standard normal variate transformation and first derivative) preprocessing method, and PLS algorithm. Under these conditions, the square correlation coefficient (R2) and root mean square error of prediction (RMSEP) of the prediction model were 0.9865 and 0.0017, respectively. The results of this study provided a feasible method for the rapid detection of moisture content in walnut kernel. To improve the performance and applicability of the model, it is necessary to continuously expand the size of the sample set.http://dx.doi.org/10.1155/2021/9986940 |
| spellingShingle | Dan Peng Yali Liu Jiasheng Yang Yanlan Bi Jingnan Chen Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy Journal of Spectroscopy |
| title | Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy |
| title_full | Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy |
| title_fullStr | Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy |
| title_full_unstemmed | Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy |
| title_short | Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy |
| title_sort | nondestructive detection of moisture content in walnut kernel by near infrared diffuse reflectance spectroscopy |
| url | http://dx.doi.org/10.1155/2021/9986940 |
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