Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses
Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different stat...
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PeerJ Inc.
2015-12-01
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| Online Access: | https://peerj.com/articles/1416.pdf |
| _version_ | 1797417925125079040 |
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| author | Jishan Chen Ruifen Zhu Ruixuan Xu Wenjun Zhang Yue Shen Yingjun Zhang |
| author_facet | Jishan Chen Ruifen Zhu Ruixuan Xu Wenjun Zhang Yue Shen Yingjun Zhang |
| author_sort | Jishan Chen |
| collection | DOAJ |
| description | Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymus chinensis) in Heilongjiang Province, China including the concentrations of crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Firstly, the linear partial least squares regression (PLS) was performed on the spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the MLR evaluation method for CP has a potential to be used for industry requirements, as it needs less sophisticated and cheaper instrumentation using only a few wavelengths. Results show that in terms of CP, ADF and NDF, (i) the prediction accuracy in terms of CP, ADF and NDF using PLS was obviously improved compared to the PCR algorithm, and comparable or even better than results generated using the MLR algorithm; (ii) the predictions were worse compared to laboratory-based spectra with the MLR algorithmin, and poor predictions were obtained (R2, 0.62, RPD, 0.9) using MLR in terms of NDF; (iii) a satisfactory accuracy with R2 and RPD by PLS method of 0.91, 3.2 for CP, 0.89, 3.1 for ADF and 0.88, 3.0 for NDF, respectively, was obtained. Our results highlight the use of the combined NIRs-PLS method could be applied as a valuable technique to rapidly and accurately evaluate the quality of sheepgrass hay. |
| first_indexed | 2024-03-09T06:25:37Z |
| format | Article |
| id | doaj.art-213170625f944233b85d61ffa76ba95f |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:25:37Z |
| publishDate | 2015-12-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj.art-213170625f944233b85d61ffa76ba95f2023-12-03T11:20:42ZengPeerJ Inc.PeerJ2167-83592015-12-013e141610.7717/peerj.1416Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analysesJishan Chen0Ruifen Zhu1Ruixuan Xu2Wenjun Zhang3Yue Shen4Yingjun Zhang5Department of Grassland Science, China Agricultural University, Beijing, ChinaHeilongjiang Academy of Agricultural Science, Institute of Pratacultural Science, Harbin, ChinaDepartment of Grassland Science, China Agricultural University, Beijing, ChinaDepartment of Grassland Science, China Agricultural University, Beijing, ChinaDepartment of Grassland Science, China Agricultural University, Beijing, ChinaDepartment of Grassland Science, China Agricultural University, Beijing, ChinaDue to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymus chinensis) in Heilongjiang Province, China including the concentrations of crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF). Firstly, the linear partial least squares regression (PLS) was performed on the spectra and the predictions were compared to those with laboratory-based recorded spectra. Then, the MLR evaluation method for CP has a potential to be used for industry requirements, as it needs less sophisticated and cheaper instrumentation using only a few wavelengths. Results show that in terms of CP, ADF and NDF, (i) the prediction accuracy in terms of CP, ADF and NDF using PLS was obviously improved compared to the PCR algorithm, and comparable or even better than results generated using the MLR algorithm; (ii) the predictions were worse compared to laboratory-based spectra with the MLR algorithmin, and poor predictions were obtained (R2, 0.62, RPD, 0.9) using MLR in terms of NDF; (iii) a satisfactory accuracy with R2 and RPD by PLS method of 0.91, 3.2 for CP, 0.89, 3.1 for ADF and 0.88, 3.0 for NDF, respectively, was obtained. Our results highlight the use of the combined NIRs-PLS method could be applied as a valuable technique to rapidly and accurately evaluate the quality of sheepgrass hay.https://peerj.com/articles/1416.pdfNear infrared spectroscopyChemical qualitySheepgrass (Leymus chinensis)Root mean squares error of calibration (RMSEC)Root mean squares error of prediction (RMSEP) |
| spellingShingle | Jishan Chen Ruifen Zhu Ruixuan Xu Wenjun Zhang Yue Shen Yingjun Zhang Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses PeerJ Near infrared spectroscopy Chemical quality Sheepgrass (Leymus chinensis) Root mean squares error of calibration (RMSEC) Root mean squares error of prediction (RMSEP) |
| title | Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses |
| title_full | Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses |
| title_fullStr | Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses |
| title_full_unstemmed | Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses |
| title_short | Evaluation of Leymus chinensis quality using near-infrared reflectance spectroscopy with three different statistical analyses |
| title_sort | evaluation of leymus chinensis quality using near infrared reflectance spectroscopy with three different statistical analyses |
| topic | Near infrared spectroscopy Chemical quality Sheepgrass (Leymus chinensis) Root mean squares error of calibration (RMSEC) Root mean squares error of prediction (RMSEP) |
| url | https://peerj.com/articles/1416.pdf |
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