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| Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology |
| CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2* |
1. School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3. Hangzhou Fanghuichuntang Group Co., Ltd., Hangzhou 311500, China
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Abstract The industrial culture of microalgae provides an important way to produce natural carbohydrates and proteins. The high cost of cultivation is one of the main “bottlenecks” that influences microalgae industrialization development. Due to the high growth rate, the biomass and nutrients of microalgae vary rapidly. Therefore, a method that can -monitor the growth of microalgae and the dynamic information in the culture of microalgae would be of great necessity for the timely optimization of environmental parameters, thereby ensuring the efficient and quality production of microalgae. However, studies on the rapid and non-destructive detection of microalgae growth and metabolism information were mostly focused on lipids and their characteristics, with the other important components neglected, such as carbohydrates and proteins.In this study, T. obliquus was used as the research objects. A fast and nondestructive approach to estimate the carbohydrates and proteins concentration of T. obliquus in situ in a living environment was proposed based upon visible/near-infrared (VIS/NIRS) HSI system. Twelve data preprocessing approaches e.g. autoscaling and standard normal variate transform (SNV) etc., 3 feature selection methods including competitive adaptive reweighted sampling algorithm (CARS), interval random frog algorithm (iRF) and simulated annealing algorithm (SA), and 4 calibration models including multiple linear regression (MLR), partial least squares (PLS), support vector machine regression (SVR) and random forest regression (RFR) were applied to establish and optimize the estimation models. The results showed that vector normalization (VN) pretreatment combined with the CARS-MLR algorithm got the best performance on the biomass prediction of T. obliquus, with an R2p of 0.967 and RPD of 6.212. Raw spectra followed by the IRF-RFR algorithm performed the best for the carbohydrate of T. obliquus (R2p=0.996, RPD=36.156). Wavelet transform (WT) with SA-RFR obtained the best results for protein detection (R2p=0.909, RPD=10.116). Moreover, the visualization maps of these components' spatial distribution and abundance in the microalgal liquid were obtained based on the optimal models. The overall results show that VIS/NIRS HSI is expected to be applied for efficient and high-quality production in microalgae industries.
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Received: 2022-11-25
Accepted: 2023-04-29
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Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
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