Evaluation of carbohydrate concentrations in Phalaenopsis using near-infrared spectroscopy

Yung Kun Chuang, I. Chang Yang, Chao Yin Tsai, Jiunn Yan Hou, Yung Huei Chang, Suming Chen

研究成果: 雜誌貢獻文章

摘要

Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. Inthisstudy, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of-0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc =0.96;SEC= 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias =-0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias =-0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.
原文英語
頁(從 - 到)494-502
頁數9
期刊Journal of the American Society for Horticultural Science
143
發行號6
DOIs
出版狀態已發佈 - 十一月 1 2018

指紋

Orchidaceae
Phalaenopsis
Near-Infrared Spectroscopy
near-infrared spectroscopy
Carbohydrates
carbohydrates
Weights and Measures
Least-Squares Analysis
least squares
Calibration
calibration
Linear Models
Fructose
Starch
Sucrose
fructose
starch
sucrose
Glucose
glucose

ASJC Scopus subject areas

  • Genetics
  • Horticulture

引用此文

Evaluation of carbohydrate concentrations in Phalaenopsis using near-infrared spectroscopy. / Chuang, Yung Kun; Yang, I. Chang; Tsai, Chao Yin; Hou, Jiunn Yan; Chang, Yung Huei; Chen, Suming.

於: Journal of the American Society for Horticultural Science, 卷 143, 編號 6, 01.11.2018, p. 494-502.

研究成果: 雜誌貢獻文章

Chuang, Yung Kun ; Yang, I. Chang ; Tsai, Chao Yin ; Hou, Jiunn Yan ; Chang, Yung Huei ; Chen, Suming. / Evaluation of carbohydrate concentrations in Phalaenopsis using near-infrared spectroscopy. 於: Journal of the American Society for Horticultural Science. 2018 ; 卷 143, 編號 6. 頁 494-502.
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abstract = "Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. Inthisstudy, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22{\%} dry weight (DW), standard error of validation (SEV) of 0.28{\%} DW, and bias of-0.01{\%} DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc =0.96;SEC= 0.26{\%} DW; SEV = 0.32{\%} DW; and bias = 0.01{\%} DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24{\%} DW; SEV = 0.31{\%} DW; bias =-0.03{\%} DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47{\%} DW; SEV = 0.56{\%} DW; bias =-0.02{\%} DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.",
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AU - Yang, I. Chang

AU - Tsai, Chao Yin

AU - Hou, Jiunn Yan

AU - Chang, Yung Huei

AU - Chen, Suming

PY - 2018/11/1

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N2 - Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. Inthisstudy, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of-0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc =0.96;SEC= 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias =-0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias =-0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.

AB - Carbohydrate concentrations are important indicators of the internal quality of Phalaenopsis. Inthisstudy, near-infrared (NIR) spectroscopy was used for quantitative analyses of fructose, glucose, sucrose, and starch in Phalaenopsis plants. Both modified partial least-squares regression (MPLSR) and stepwise multiple linear regression (SMLR) methods were used for spectral analysis of 302 Phalaenopsis samples in the full visible NIR wavelength range (400–2498 nm). Calibration models built by MPLSR were better than those built by SMLR. For fructose, the smoothed first derivative MPLSR model provided the best results, with a correlation coefficient of calibration (Rc) of 0.96, standard error of calibration (SEC) of 0.22% dry weight (DW), standard error of validation (SEV) of 0.28% DW, and bias of-0.01% DW. For glucose, the MPLSR model based on the smoothed first derivative spectra was the best (Rc =0.96;SEC= 0.26% DW; SEV = 0.32% DW; and bias = 0.01% DW). The best MPLSR model of sucrose was developed using the smoothed first derivative spectra (Rc = 0.96; SEC = 0.24% DW; SEV = 0.31% DW; bias =-0.03% DW). Regarding starch, the smoothed first derivative MPLSR model showed the best effects (Rc = 0.91; SEC = 0.47% DW; SEV = 0.56% DW; bias =-0.02% DW). Both the MPLSR and SMLR models showed satisfactory predictability, indicating that NIR has the potential to be adopted as an effective method of rapid and accurate inspection of the carbohydrate concentrations of Phalaenopsis plants. This technique could contribute substantially to quality management of Phalaenopsis.

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KW - Quantitative analysis

KW - Starch

KW - Stepwise multiple linear regression

KW - Sucrose

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