The large-scale production of edible mushrooms typically requires the use of greenhouses, as the greenhouse environment significantly affects the growth of edible mushrooms. It is crucial to effectively predict the temperature, humidity, and carbon dioxide fluctuations within the mushroom greenhouse for determining the environmental stress and pre-regulation of edible mushrooms. To address the nonlinearity, temporal dynamics, and strong coupling of the edible mushroom greenhouse environment, a temperature, humidity, and carbon dioxide prediction model based on the combination of the attention mechanism, the convolutional neural network, and the long short-term memory neural network (A-CNN-LSTM) is proposed. Experimental data were collected from both the inside and outside of the greenhouse, including environmental data and the on–off data of environmental control devices. After completing missing data using linear interpolation, denoising with Kalman filtering, and normalization, the recurrent neural network (RNN) model, long short-term memory (LSTM) model, and A-CNN-LSTM model were trained and tested on the time series data. These models were used to predict the environmental changes in temperature, humidity, and carbon dioxide inside the greenhouse. The results indicate that the A-CNN-LSTM model outperforms the other two models in terms of denoising, non-denoising, and different prediction time steps. The proposed method accurately predicts temperature, humidity, and carbon dioxide levels with errors of 0.17 °C (R² = 0.974), 2.06% (R² = 0.804), and 8.367 ppm (R² = 0.993), respectively. These results indicate improved prediction accuracy for temperature, humidity, and carbon dioxide values inside the edible mushroom greenhouse. The findings provide a decision basis for the precise control of the greenhouse environment.

A rapid detection method based on surface enhanced Raman spectroscopy (SERS) was proposed in this paper in order to realize the detection of tetracycline residues in duck meat. Firstly, surface enhanced Raman spectra characteristics of tetracycline aqueous solution, duck meat extract, and duck meat extract containing tetracycline were analyzed. Secondly, the effect of the addition amount of duck meat extract containing tetracycline on SERS intensity and the effect of the adsorption time on SERS intensity were discussed, respectively. Thirdly, SERS intensity ratio at 1272 and 1558 cm⁻¹ (I1272/I1558) was used to establish the SERS calibration curve. A good linearity relationship between the tetracycline concentration in duck meat extract and I1272/I1558 was obtained, and the linear regression equation and the correlation coefficient (r) were y=0.0177x+0.1213 and 0.950, respectively. The average recovery of tetracycline in duck meat extract was 101~108% with relative standard deviation (RSD) of 2.4~4.6%. The experimental results showed that the method proposed in this paper was a good detection scheme for the rapid detection of tetracycline residues in duck meat.

This paper investigated on 478 duck meat samples for the identification of 2 kinds of antibiotics, that is, doxycycline hydrochloride and tylosin, that were classified based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate techniques. The optimal detection parameters, including the effects of the adsorption time, and 2 enhancement substrates (i.e., gold nanoparticles as well as gold nanoparticles and NaCl) on Raman intensities, were analyzed using single factor analysis method. The results showed that the optimal adsorption time between gold nanoparticles and analytes was 2 min, and the colloidal gold nanoparticles without NaCl as the active substrate were more conducive to enhance the Raman spectra signal. The SERS data were pretreated by using the method of adaptive iterative penalty least square method (air-PLS) and second derivative, and from which the feature vectors were extracted with the help of principal component analysis. The first four principal components scores were selected as the input values of support vector machines model. The overall classification accuracy of the test set was 100%. The experimental results showed that the combination of SERS and multivariate analysis could identify the residues of doxycycline hydrochloride and tylosin in duck meat quickly and sensitively.

In order to meet the growing demand for food safety, the rapid classification of sulfadimidine and sulfapyridine in duck meat based on surface-enhanced Raman spectroscopy (SERS) was investigated using gold nanoparticles (Au NPs) and NaCl solution as the active substrate and active agent, respectively. The results showed that the Raman characteristic peaks at 557 and 573 cm⁻¹ were used to determine whether sulfadimidine and sulfapyridine remained in duck meat. A single factor experimental method was used to optimize the conditions. Three pretreatment methods were employed to optimize the raw measurements. According to the classification accuracies, adaptive iterative reweighted penalty least squares and second derivative were selected to be the optimal pretreatment method. Next, principal component analysis (PCA) was performed to extract the characteristic variables. The first four score values of PCA were selected to be the input values of support vector machine (SVM) classification model, which classified duck meat samples into four categories. Nu-support vector classification and radial basis function were selected to be the employed SVM classification model and the Kernel type, respectively. The gamma and nu values were 0.25 and 0.255, respectively. The classification accuracy for the test set was 90.44%. The sensitivities and specificities of test set were calculated, and the highest sensitivity and specificity values were 96.97% and 100%, respectively. The experimental results showed that this classification model provides favorable classification results. Therefore, the adopted method may be used for the rapid classification of sulfadimidine and sulfapyridine residues in duck meat.

Excess use of ofloxacin (OFL) and norfloxacin (NOR) in duck breeding is a significant food safety issue due to its residues. Therefore, a surface-enhanced Raman spectroscopy (SERS) method was developed to detect these analytes in duck meat to protect consumer health. The SERS conditions for OFL and NOR including the adsorption time, the volume ratio of gold nanoparticles to NaCl solution, and the volume of enhancement solution, were optimized by single factor experiments, and their values were 6 min, 3:1, and 20 μL, respectively. Furthermore, a total of 396 samples were used to establish a principal component analysis–support vector machine (PCA-SVM) model. The performance was evaluated using three pretreatment methods: adaptive iterative–penalty least squares (air-PLS) and standard normal variate (SNV), air-PLS and first derivative coupled with SNV, and air-PLS and second derivative coupled with SNV. Air-PLS and second derivative coupled with SNV was selected to be the optimal pretreatment. The sensitivity and specificity of PCA-SVM for the classification of the meat samples were 85 to 100% and 95 to 100% with an accuracy of 93%. The results show that SERS was an effective and rapid approach for the identification of OFL and NOR in duck meat.