Journal
ROYAL SOCIETY OPEN SCIENCE
Volume 6, Issue 7, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsos.190485
Keywords
terahertz; spectroscopy; food additive; machine learning; quantitative analysis
Categories
Funding
- Outstanding Youth Talent Program of Jiangxi Province [20171BCB23060]
- Education Department Project of Jiangxi Province [GJJ160478]
- China Scholarship Council [201808360317]
- Jiangxi Association for Science and Technology (JAST)
- [368]
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Investigations were initiated to develop terahertz (THz) techniques associated with machine learning methods of generalized regression neural network (GRNN) and back-propagation neural network (BPNN) to rapidly measure benzoic acid (BA) content in wheat flour. The absorption coefficient exhibited a maximum absorption peak at 1.94 THz, which generally increased with the content of BA additive. THz spectra were transformed into orthogonal principal component analysis (PCA) scores as the input vectors of GRNN and BPNN models. The best GRNN model was achieved with three PCA scores and spread value of 0.2. Compared with the BPNN model, GRNN model to powder samples could be considered very successful for quality control of wheat flour with a correlation coefficient of prediction (r(p)) of 0.85 and root mean square error of prediction of 0.10%. The results suggest that THz technique association with GRNN has a significant potential to quantitatively analyse BA additive in wheat flour.
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