Honey adulteration is a major issue in food production, which may reduce the effective components in honey and have a detrimental effect on human health. Herein, laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to fast quantify the adulterant content. Two common types of adulteration, including mixing acacia honey with high fructose corn syrup (HFCS) and rape honey, were quantified with univariate analysis and partial least squares regression (PLSR). In addition, the variable importance was tested with univariable analysis and feature selection methods (genetic algorithm (GA), variable importance in projection (VIP), selectivity ratio (SR)). The results indicated that emissions from Mg II 279.58, 280.30 nm, Mg I 285.25 nm, Ca II 393.37, 396.89 nm, Ca I 422.70 nm, Na I 589.03, 589.64 nm, and K I 766.57, 769.97 nm had compact relationship with adulterant content. Best models for detecting the adulteration ratio of HFCS 55, HFCS 90, and rape honey were achieved by SR-PLSR, VIP-PLSR, and VIP-PLSR, with root-mean-square error (RMSE) of 8.9%, 8.2%, and 4.8%, respectively. This study provided a fast and simple approach for detecting honey adulteration.

During preliminary tea processing, moisture content is an important consideration affecting the tea quality. Traditionally, the moisture content of tea leaves was manually controlled by the joint action of multiple processing units, and maintaining stability was difficult. In this paper, a multi-unit collaborative strategy was proposed for controlling moisture content in preliminary tea processing. Multivariate methods including polynomial regression, radical basis function neural network (RBFNN), and least squares support vector machine (LSSVM) were used to establish models for moisture content prediction in the first fixation, second fixation, and drying units, with minimal root mean square errors (RMSEs) of 1.34%, 0.86%, and 0.13%, respectively. The combination of RBFNN and LSSVM, with a RMSE of 0.03%, was used to model the preliminary processing of whole tea. Rough set data mining technology was used to obtain the optimum ranges of moisture content and critical process parameters. Finally, a Monte Carlo simulation experiment was carried out within the optimum range, and moisture content design spaces for the single unit and the whole processing line were obtained. With the proposed approach, the stability of the final moisture content of tea can be improved, which is of great significance for improving tea quality and accelerating the automation of tea production.

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