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| Non-Destructive Determination of Growth Quality Indicators of Spirulina sp. Using Vis/NIR Spectroscopy |
| JIANG Lu-lu1, WEI Xuan2, XIE Chuan-qi3*, HE Yong4* |
1. Zhejiang Technology Institute of Economy, Hangzhou 310018, China
2. College of Mechanical and Electronic Engineering，Fujian Agriculture and Forestry University，Fuzhou 350002, China
3. Department of Bioproducts and Biosystems Engineering, University of Minnestota, Saint Paul, MN 55108, USA
4. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
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Abstract In order to detect the growth quality indicators of Spirulina sp. using a fast and on-destructive method, this study was carried out to predict chlorophyll a and protein content under different red and bule light combinations (100% red light, 90% red light+10% blue light, 70% red light+30% blue light and 50% red light+50% blue light) using Vis/NIR spectroscopy (325～1 075 nm). The chlorophyll a and protein content were predicted using partial least squares (PLS) models. Then successive projections algorithm (SPA) was used to identify effective wavelengths for chlorophyll a and protein, resulting in five (404, 440, 518, 662 and 875 nm) and four (411, 531, 602 and 1 047 nm) wavelengths, respectively. Based on the selected wavelengths, multiple linear regression (MLR) models were established, which obtained the rp of 0.949 and 0.974, RMSEP of 0.018 8 and 0.006 74, respectively. The results demonstrated that Vis/NIR spectroscopy has the potential to be used for determination of chlorophyll a and protein content in Spirulina sp., and the growth condition can be monitored by the MLR equation and the corresponding spectral reflectance information.
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Received: 2017-10-10
Accepted: 2018-02-26
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Corresponding Authors:
XIE Chuan-qi, HE Yong
E-mail: cqxie@umn.edu; yhe@zju.edu.cn
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[1] LIU Hai-jing, SUN Su-qin, LI An, et al(刘海静，孙素琴，李 安，等). Scientia Agricultural Sinica(中国农业科学)，2012，45(22)：4738.
[2] Campanella L, Crescentini G, Avino P. Analusis, 1999, 27(6): 533.
[3] Shao Y N, Pan J, Zhang C, et al. Computers and Electronics in Agriculture, 2015, 112: 121.
[4] Meksiarun P, Spegazzini N, Matsui H, et al. Applied Spectroscopy, 2015, 69(1): 45.
[5] Challagulla V, Nayar S, Walsh K, et al. Critical Reviews in Biotechnology, 2017, 37(5): 566.
[6] Spetea C, Rintamki E, Schoefs B. Philosophical Transactions of the Royal Society B-Biological Sciences, 2014, 369: 20130220.
[7] Pu H B, Liu D, Wang L, et al. Food Analytical Methods, 2016, 9(1): 235.
[8] Zhang C, Jiang H, Liu F, et al. Food and Bioprocess Technology, 2017, 10(1): 213.
[9] Zhu Q B, Guan J Y, Huang M, et al. Postharvest Biology and Technology, 2016, 114: 86.
[10] Wu D, Nie P, Cuello J, et al. Journal of Food Engineering, 2011, 102(3): 278.
[11] Bieroza M, Baker A, Bridgeman J. Advances in Engineering Software, 2012, 44(1): 126.
[12] Wu D, Shi H, Wang S J, et al. Analytica Chimica Acta, 2012, 726: 57.
[13] Wu D, He Y, Nie P C, et al. Analytica Chimica Acta, 2010, 659(1-2): 229.
[14] Wu D, Wang S J, Wang N F, et al. Food and Bioprocess Technology, 2013, 6(11): 2943.
[15] Xie C Q, Xu N, Shao Y N, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2015, 149：971.
[16] Wei X, Liu F, Qiu Z J, et al. Food and Bioprocess Technology, 2014, 7(5): 1371.
[17] Schoefs B. Trends in Food Science & Technology, 2002, 13(11): 361.
[18] Hoenecke M E, Bula R J, Tibbitts T W. HortScience, 1992, 27(5): 427. |
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