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| Rapid Detection of Lotus Seed Powder Based on Near Infrared Spectrum Technology |
| FU Cai-li1, LI Ying1, CHEN Li-fan1, WANG Shao-yun1, WANG Wu2* |
1. College of Biological Science and Engineering, Fuzhou University, Fuzhou 350116, China
2. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, China |
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Abstract Lotus seed is an important medicine and edible food, but to dry lotus seeds cook requires a long time, so lotus seed powder is more popular by consumers to adapt to the modern fast-paced way of life. In this paper, lotus seed powder adulterated with sweet potato powder, corn flour and wheat flour were identified by near infrared spectroscopy (NIRs) technique. Support vector machine (SVM), least squares support vector machine (LS-SVM) and partial least squares discriminate analysis (PLS-DA) were used to identify the model when thecategory was known, and the clustering algorithm was usedotherwise. In addition, the moisture content of lotus seeds powder was quantitatively analyzed by partial least squares (PLS) regression. The results showed that the discrimination accuracy of LS-SVM modelis 100%, and the clustering algorithm could effectively identify the 5% adulteration ofsweet potato powder, corn flour and wheat flour. Moreover, performance of PLS model to predict the moisture content in the lotus seed powder is good, and the accuracy of model by Normalize was satisfactory with the coefficients of determination of calibration (R2c=0.973 2), the coefficients of determination of prediction (R2p=0.969 5), root mean square errors of calibration (RMSEC=0.111 5), and good root mean square errors of prediction (RMSEP=0.118 9). The results showed that the near infrared spectroscopy is a fast, accurate and nondestructive analysis method to rapidly identify the lotus seed powder, accurately determinate the water content in lotus seed powder, and availably provide a useful idea for quality testing of daily food.
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Received: 2017-05-08
Accepted: 2017-10-19
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Corresponding Authors:
WANG Wu
E-mail: wangwu@fzu.edu.cn
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