Share Email Print
cover

Proceedings Paper

Evaluation of Phalaenopsis flowering quality using near infrared spectroscopy
Author(s): Suming Chen; Yung-Kun Chuang; Chao-Yin Tsai; Yao-Chien A. Chang; I-Chang Yang; Yung-Huei Chang; Chu-Chun Tai; Jiunn-Yan Hou
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Carbohydrate contents have been demonstrated as indicators for flowering quality of Phalaenopsis plants. In this study, near infrared reflectance (NIR) spectroscopy was employed for quantitative analysis of carbohydrate contents like fructose, glucose, sucrose, and starch in Phalaenopsis. The modified partial least squares regression (MPLSR) method was adopted for spectra analyses of 176 grown plant samples (88 shoots and 88 roots), over the full wavelength range (FWR, 400 to 2498 nm). For fructose concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.961, SEC = 0.210% DW, SEV = 0.324% DW) in the wavelength ranges of 1400-1600, 1800-2000, and 2200-2300 nm. For glucose concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.975, SEC = 0.196% DW, SEV = 0.264% DW) in the wavelength range of 1400-1600, 1800-2000, and 2100-2400 nm. For sucrose concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.961, SEC = 0.237% DW, SEV = 0.322% DW) in the wavelength range of 1300-1400, 1500-1800, 2000-2100, and 2200-2300 nm. For starch concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.873, SEC = 0.697% DW, SEV = 0.774% DW) in the wavelength ranges of 500-700, 1200-1300, 1700-1800, and 2200-2300 nm. This study successfully developed the calibration models for inspecting concentrations of carbohydrates to predict the flowering quality in different cultivation environments of Phalaenopsis. The specific wavelengths can be used to predict the quality of Phalaenopsis flowers and thus to adjust cultivation managements.

Paper Details

Date Published: 17 May 2013
PDF: 4 pages
Proc. SPIE 8881, Sensing Technologies for Biomaterial, Food, and Agriculture 2013, 88810F (17 May 2013); doi: 10.1117/12.2030710
Show Author Affiliations
Suming Chen, National Taiwan Univ. (Taiwan)
Taiwan Agricultural Mechanization Research and Development Ctr. (Taiwan)
Yung-Kun Chuang, National Taiwan Univ. (Taiwan)
Chao-Yin Tsai, National Taiwan Univ. (Taiwan)
Yao-Chien A. Chang, National Taiwan Univ. (Taiwan)
I-Chang Yang, Taiwan Agricultural Mechanization Research and Development Ctr. (Taiwan)
Yung-Huei Chang, National Taiwan Univ. (Taiwan)
Chu-Chun Tai, National Taiwan Univ. (Taiwan)
Jiunn-Yan Hou, National Taiwan Univ. (Taiwan)


Published in SPIE Proceedings Vol. 8881:
Sensing Technologies for Biomaterial, Food, and Agriculture 2013
Naoshi Kondo, Editor(s)

© SPIE. Terms of Use
Back to Top