To establish a method for the simultaneous determination of 9 kinds of food additives in foods by ultra performance liquid chromatography.

The samples were subjected to ultrasonic extraction with water, purified by a PWA-2 solid-phase extraction cartridge, and then separated on a Poroshell 120 EC-C₁₈ column (150 mm×3.0 mm, 2.7 μm) under gradient elution using a mobile phase composed of 25 mmol/L potassium dihydrogen phosphate solution and acetonitrile. Detection was carried out at 215 nm.

The results showed good linearity for the 9 kinds of analytes in the mass concentration range of 2.0–100.0 mg/L, with correlation coefficients (r²) not less than 0.999. The limits of detection and limits of quantitation ranged from 0.0025 to 0.0050 g/kg and from 0.005 to 0.010 g/kg, respectively. Recovery experiments were performed at low, medium and high spiked levels in 4 kinds of representative matrices (tea beverage, sausage, milk, red wine). The average recoveries were 80%–118%, with relative standard deviations in the range of 2.8%–8.5% (n=6).

The method exhibited high sensitivity, good reproducibility and high accuracy, and is applicable for the simultaneous determination of 9 kinds of food additives,including tannic acid, in food matrices.

To establish a machine learning predictive model based on multidimensional spatiotemporal risk feature engineering for forecasting the results of supervisory sampling inspections of edible agricultural products.

Using supervisory sampling data for edible agricultural products from Fujian’s prefecture-level cities between 2022 and 2024, information on product type, sampling location, sampling site and production date were systematically integrated. Multidimensional spatiotemporal risk feature engineering was applied to process the dataset, constructing 6 kinds of new features to form a novel dataset based on the original data. The 3 kinds of mainstream binary classification prediction models were applied to forecast inspection outcomes using the processed data.

Across all 3 kinds of mainstream models, the multidimensional spatiotemporal risk feature engineering method demonstrated superior predictive performance compared to traditional sampling methods.Specifically, the combination of multidimensional spatiotemporal risk feature engineering with light gradient boosting machine (LightGBM) achieved the best overall performance on the test set samples, with an AUC of 0.845,Recall of 0.881, G-mean of 0.741 and misclassification costs of 8561.

Compared to traditional sampling methods, multidimensional spatiotemporal risk feature engineering enhances the model’s ability to capture complex risk patterns, leading to a comprehensive improvement in predictive performance. The LightGBM model based on multidimensional spatiotemporal risk feature engineering can accurately predict the inspection results of edible agricultural products, providing technical support for formulating differentiated, dynamic supervision and inspection plans and risk early warning systems for edible agricultural products.

To establish a method for concurrent qualitative screening of 6 kinds of new phenylethylamine psychoactive substances in apple juice by liquid chromatography-quadrupole-time of flight mass spectrometry.

The samples were extracted with 10 mL acetonitrile, purified with C₁₈ powder, and separated on a C₁₈ column (100 mm×2.1 mm, 2.7 μm). The 10 mmol/L ammonium acetate aqueous solution (0.1% formic acid) and acetonitrile was as mobile phase, and the elution method was gradient elution. Mass spectrometry methods included electrospray ionization, positive ion scanning and information-dependent acquisition (IDA) mode detection. Screening was conducted by considering multiple factors, including the mass deviation, retention time,isotope distribution characteristics and similarity to the secondary spectrum library of the target signal.

The 6 kinds of targets showed good linearity (0.1–50.0 μg/L, r>0.9990) with matrix effects was all less than 20%, which could be classified as weak matrix effects. The limit of detection was 5.0 μg/L. The comprehensive scores of the qualitative screening were all above 90 points, meeting the requirements for qualitative confirmation.

The method is simple, rapid and sensitive, and can be used for the qualitative screening and detection of 6 kinds of new phenylethylamine psychoactive substances in apple juice.

To evaluate and analyze the rapid detection products for tropical fruit.

The representative tropical fruits (litchi, banana, mango) were selected as the research objects, and the mainstream colloidal gold rapid detection products for common risk factors were selected. The quality of the products was evaluated and verified by 2 kinds of methods: Laboratory standard blind sample evaluation and actual sample verification. It mainly investigated the false positive rate, false negative rate, product type, brand difference and so on.

A total of 98 rapid detection products participated in the blind sample evaluation with additional standards, with a matching rate of 46.9%; 26 products participated in the actual sample verification, with a matching rate of 42.3%. Among the 16 different batch numbers of the same product that participated in the verification by 2 kinds of methods, 9 results were consistent, accounting for 56.3%. Overall, the matching rate of single-card was higher than that of multi-card, and the false negative rate exceeding 5% was the main reason for the inaccuracy of tropical fruit rapid testing products, which also showed significant brand differences.

The total matching rate of rapid detection products for tropical fruits is relatively low. It is necessary to increase research and development efforts, improve product quality and perfect the product evaluation system. This paper can provide data support and theoretical reference for the optimization, standard formulation and scientific application of rapid testing technology for the quality and safety of tropical fruit.

To establish a method for the determination of the monosaccharide components and content in polysaccharides from Liubao tea by ultrasonic-assisted enzymatic extraction-ion chromatography.

Optimization of extraction conditions for monosaccharide components and content in Liubao tea polysaccharides by single-factor experiments and orthogonal experiments. The chromatographic conditions for the determination monosaccharides in Liubao tea polysaccharides by ion chromatography were optimized simultaneously, and the established method was evaluated methodologically.

Through analysis of range and variance, the optimal extraction process conditions for monosaccharide components and content in Liubao tea polysaccharides were that m_cellulase to m_pectinase ratio of 1:5, enzyme addition level of 4.0%, enzymatic hydrolysis pH of 5.0, solid-to-liquid ratio of 1:25 (g:mL), enzymatic hydrolysis-ultrasonic treatment duration of 40 minutes and enzymatic hydrolysis-ultrasonic temperature at 50 ℃; after methodological evaluation, all 7 kinds of monosaccharides exhibited good linear relationships, with correlation coefficients greater than 0.995, the relative standard deviation of precision was less than 3.2%, the limit of quantitation were satisfactory, and the recovery rates ranged from 83.33% to 101.74%. Through the analysis of monosaccharide components in the polysaccharides of Liubao tea showed that the tea polysaccharides in Liubao tea were composed of rhamnose, arabinose, galactose,glucose, D-xylose, D-mannose and galacturonic acid.

The ultrasonic-assisted enzymatic extraction method can significantly shorten the extraction time of sample, and can be utilized for the rapid determination of monosaccharide components and content in the polysaccharides of Liubao tea.

To develop a plant-based walnut-oat-coffee yogurt, and subsequently analyze its basic physicochemical properties as well as nutritional components including amino acids and fatty acids.

Taking walnuts, oats and coffee as the main raw materials, the production process of the walnut-oat-coffee plant-based yogurt was optimized through single-factor experiments and response surface methodology and a systematic nutritional analysis was conducted on the unfermented yogurt base.

The optimal exhibited optimal quality when formulated with 14% walnut kernel, 9% oat flour, 8% coffee extract, 0.3% carboxymethyl cellulose (CMC), followed by fermentation at 40 °C for 8 h. The final product exhibited a sensory score of 85.49, titratable acidity of (62.50±1.04) °T, a protein content of (3.20±0.11) g/100 g, and a fat content of (3.62±0.13) g/100 g. Nutritional analysis revealed that, compared to the unfermented base, the total amino acid content of the final product increased significantly from (225.57±5.17) μg/g to (594.29±5.91) μg/g, along with a more balanced fatty acid profile (saturated fatty acids:monounsaturated fatty acids:polyunsaturated fatty acids=1:1.7068:5.9335).

The optimized process in this study produces a walnut-oat-coffee plant-based yogurt with favorable taste and rich nutritional value, characterized by significantly increase amino acid content and a balanced fatty acid profile. This approach provides a feasible processing protocol for developing composite plant-based yogurts that integrate good flavor with balanced nutrition.

To develop a method for rapid detection of lead in dairy products by anodic stripping voltammetry.

The gold electrode and machine learning algorithm were used to optimize the deposition time, pulse height and sample pretreatment parameters. The linear range, limit of detection, recovery rate, relative standard deviation and other indicators of the method were determined.

Setting deposition time to 360 s,pulse height to 270 mV and the optimized parameters of sample pretreatment were as follows: 1:3 (V:V) for milk,1:2 (m:V) for milk powder and 1:4 (V:V) for yogurt, centrifugation 10 min (milk), 10 min (powder), 14 min (yogurt)were determined as the optimal parameters for rapid detection. The results showed that the method had good linearity in the range of 187.5–6000.0 ng/mL (r²>0.99), the limit of detections were 123.32–273.10 ng/mL, the recovery rates were 90%–110%, and the relative standard deviation was less than 5%.

This study establish a fast, simple and accurate method for detecting lead content in milk powder, yogurt and milk, providing standardized solutions for heavy metal prevention and control.

To establish a method for the determination of total arabinoxylan content in cereals by microwave digestion-high performance liquid chromatography-evaporative light scattering detection.

Following digestion with 1.0 mol/L trifluoroacetic acid at 120 ℃ for 30 min, the samples were de-acidified,re-dissolved and subsequently analyzed. Separation was performed on an NH₂ chromatographic column (4.6 mm×250 mm, 5 µm) with isocratic elution using acetonitrile-water (90:10, V:V) as the mobile phase. Detection was conducted using an evaporative light scattering detection, quantification was performed by the external standard method, and the developed method was applied to the analysis of common cereal samples.

After methodological verification, good linearity was observed for xylose and arabinose in the range of 0.2 to 2.0 mg/mL,with coefficient of determination exceeding 0.999. The limits of detection were 1.0 mg/g for xylose and 1.5 mg/g for arabinose, and the corresponding limits of quantitation were 2.5 mg/g and 5.0 mg/g. Recoveries ranged from 86.8% to 93.1% (relative standard deviation all less than 3.5%).

The method is accurate, reliable and sensitive,making it suitable for the determination of total arabinoxylan in cereals and providing a reference for cereal quality evaluation, processing adaptability analysis and dietary nutrition research.