Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging

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1 Supportng nformaton for Nondestructve and ntutve determnaton of crcadan chlorophyll rhythms n soybean leaves usng multspectral magng Wen-Juan Pan 1, Xa Wang 2, Yong-Ren Deng 3, Ja-Hang L 3, We Chen 1, John Y. Chang 3,4, Jan-Bo Yang 5 & Le Zheng 1, 2 1 School of Botechnology and Food Engneerng, Hefe Unversty of Technology, Hefe , Chna, 2 School of Medcal Engneerng, Hefe Unversty of Technology, Hefe , Chna, 3 Department of Computer Scence and Engneerng, Natonal Sun Yat-sen Unversty, Kaohsung 80424, Tawan, 4 Department of Healthcare Admnstraton and Medcal Informatcs, Kaohsung 80708, Tawan, 5 Rce Research Insttute, Anhu Academy of Agrcultural Scences, Hefe , Chna. Correspondence: Le Zheng, Tel: E-mal: lzheng@hfut.edu.cn, le.zheng@alyun.com Ths supplemental nformaton secton ncludes the followng: Ten pages: spectral data extracton detals, partal least squares regresson (PLSR calbraton models detals, optmal wavelength selecton detals, fgures S1-S5, and reference 1

2 Spectral data extracton The reflectance spectrum data, calculated by averagng the spectral value of all pxels n the ROI to produce only one mean value (spectrum, were extracted from solely the soybean leave regons by gnorng both the background and shadow. The fnal mask named Automatc Mask, whch s resultng from segmentaton step, was used as the man ROI to extract spectral data from the calbrated multspectral mage. The reflectance spectrum data, calculated by averagng the spectral value of all pxels n the ROI to produce only one mean value (spectrum, were extracted from solely the soybean leave regons by gnorng both the background and shadow. The same procedure was repeated to obtan the 19 mean spectrum values of other soybean leave samples from all multspectral mages saved n a spectral excel. Partal least squares regresson (PLSR calbraton models PLSR, a dmensonalty reducton method, s emergng as the most robust and relable chemometrc method for constructng models and ams at determnng predctor combnatons wth maxmum covarance wth the response varable when the measured varables are many and hghly collnear 1-3. The PLSR s an optmzed verson based on the lnear algorthm, so the good calbraton performance of PLSR for one response varable can be mproved by elmnatng of unnformatve varables. The varable elmnaton s based on the PLSR coeffcents β, the vector contanng regresson coeffcents b (K 1, whch are calculated by, β= b =W (P T W 1 q, equaton (1 Where W (K A s the X weght matrx, P (K A s an x-loadng matrx and q (1 A s the y-loadng vector. 2

3 The PLSR model derved for chlorophyll a and b content on the full range spectra s shown as follow: n YChl X equaton (2 0 1 Whereβ 0 and β are regresson coeffcents, Y Chl s the measured chlorophyll a or b content of soybean leaves samples, X s the varable at the th wavelength, n represents the number of wavelength. In ths study, the tranng set and the test set contan 90 spectra and 60 spectra, respectvely. After that, PLSR was executed usng MATLAB 7.11 (The Mathworks Inc., Natck, MA, USA. The performance of the fnal PLSR models s evaluated n terms root mean squared error of calbraton (RMSEC and the root mean squared error of predcton (RMSEP, as follows, n ^ ( 2 y y RMSEP 1 n, equaton (3 n 2 ( y y 1 R p 1, equaton (4 n 2 ( y y 1 ^ Where n s the number of samples n the test set. y, y^ are the reference measurement result and the estmated result of the model for the test set. Optmal wavelength selecton The successve projectons algorthm (SPA s a varable selecton technque desgned to 3

4 solve collnearty effects by mnmzng redundant varables n the tranng data set. The SPA process s dsposed n a matrx X (N K = (x 1,,x. Let M ((N-1 K be the maxmum number of selected varables. The algorthm for constructon of each chan starts from one of the varables x k as follows, Z X SEL (1, for all 1,..., N I I ( N N P X SEL ( 1, k j Z 1 j end x, for all k 1,..., K ( the ntal 1 j x, for all j 1,..., K( the ntal Z ( Z I T ( Z Z 1 1 X end k j P k k end X j 1,..., k 1 j j j arg max T ( the X ( the ( the j 1 projected next projected ( the lagerest teraton matrx vectors of projected the projected projected vectors projecton vectors vectors operaton The algorthm was establshed based on prevous studes 4-5, and the process of SPA, all fgures analyses and statstcs were carred out n Matlab 7.11 (The Mathworks Inc., Natck, MA, USA and Orgn 8.5.Ths s the frst tme to use SPA for the optmal spectral wavelength selecton representng chlorophyll crcadan clock. 4

5 Supplemental fgure S1 Crcadan rhythms can be measured by MSI n stress condtons and a range of plant speces. srgb mages of soybean leaves at 20 d (a, 30 d (b, 40 d (c and ten leaves (d were harvested for scorng for each bologcal replcate under LD, LL, and DD condtons. Soybean plants under three drought stress condtons (e. Wheat plants (f. The moss Physcomtrella patens (g. 5

6 Supplemental fgure S2. Reflectance dfferental mages (color exhbt the rhythm of heterogenety at 660 nm durng the recordngs of Fg. 4 for soybean leaves n the LL condtons (a at dfferent tme pont n addton to DD condtons (b. Soybean seedlngs were exposed to LL (contnuous lght condtons and DD (contnuous dark condtons at 28 C. Twenty-day-old soybean seedlngs grown under 16 h of lght and 8 h of dark were transferred to LL (tme 0 and DD(tme 0. Soybean leaves mages were captured by the magng system at 4 h ntervals for a total of 48 h. 6

7 Supplemental fgure S3. Reflectance dfferental mages (gray exhbt the rhythm of heterogenety at 780 nm durng the recordngs of Fg. 4 for soybean leaves n the LL condton (a at dfferent tme pont n addton to DD (b. 7

8 Supplemental fgure S4. Crcadan rhythms can be measured by MSI n the moss Physcomtrella patens. 8

9 Supplemental fgure S5. Prncpal setup of the multspectral magng system. An ntegratng sphere wth a matte whte coatng ensures optmal lghtng condtons. The lght emttng dodes located n the rm of the sphere ensures narrowband llumnaton. The mage acquston s performed by a monochrome grayscale CCD camera mounted n the top of the sphere. 9

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