Non-destructive Detection of Fatty Acid Content of Camellia Seed Based on Hyperspectral

Abstract

jjocs costly. At the same time, the residual chemical detection reagents will also pollute the ecological environment. Therefore, it is of great significance to find an efficient and convenient nondestructive detection method for fatty acid content of camellia seed. Currently, hyperspectral technology has been widely used in food quality detection 3) , food safety detection 4,5) , fruit damage and internal components detection 6) , and vegetable freshness detection 7) . Using various spectral equipment to detect food related components and contents has the advantages of high efficiency and convenience, and has been widely used in the field of food detection. For example, Galtier et al. 8) established qualitative models of different producing areas of French virgin olive oil and quantitative models of various fatty Abstract: As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R 2 ) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (R C 2 ) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (R P 2 ) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.