Search

The excessive content of additives in food is a radical problem that affects human health. However, traditional chemical methods are limited by a long cycle, low accuracy, and strong destructiveness, so a fast and accurate alternative is urgently needed. This paper proposes a prediction model introducing near-infrared spectroscopy and deep learning to perform fast and accurate non-destructive detection of artificial bright blue pigment in cream. The model results show that R

Artificial pigment is a common food additive in cream products. If added in excess, it will do harm to human body. At present, there is no research on the detection of cream pigment by Near Infrared (NIR) spectroscopy. In this paper, a method based on random forest was applied to determine the indigotine in cream. Weighting in the experiments was accomplished using analytical balances with precision as low as 0.0001 g. The NIR spectra data of cream with different concentration of indigotine were recorded. The original spectra was pretreated by SG smoothing, mean centering and second derivative. Random forest was applied to establish a quantitative analysis model for cream pigment content, and multiple evaluation criteria were selected to comprehensively evaluate the model. The R 2 was 0.9402, R M S E P was 0.2509 and R P D was 4.0893. Consequently, NIR spectroscopy, combined with data pretreatments and random forest model, was confirmed to be an interesting tool for non-destructive evaluation of pigment content in cream.

Based on near-infrared spectrum and interval random forest, a fast quantitative analysis method for the content of sunset yellow content was established. The spectra of 132 cream pigment samples were obtained by FT-NIR spectrometer, and various preprocessing methods such as standard normal variable (SNV), wavelet transform (WT), and SG (Savitzky-Golay) were used to smooth and denoise the original spectrum. In this paper, WT and first-order differentiation were used as pretreatment and the Kennard-Stone algorithm was used to divide the data set. Finally interval partial least squares, partial least squares, interval random forest and random forest were used to construct an optimal quantitative analysis model. The experimental results show that the interval random forest can find the best sub-interval to achieve the prediction ability of the model. The R2 (the coefficient of determination) and RMSEP (root mean square error of the prediction) of the prediction set are 0.8965 and 0.2454, respectively. The research results show that near-infrared spectroscopy combined with interval random forest algorithm is a fast and non-destructive method to detect the content of sunset yellow in cream.