Milk is a nutrient-rich food, and water adulteration in milk can reduce its quality and increase food safety risks. Nondestructive and efficient detection of milk adulteration levels is crucial to addressing this issue. This study employed a portable near-infrared spectrometer to measure and analyze the absorbance of milk samples within the wavelength range of 900 to 1,800 nm. Based on the original spectra, models such as soft independent modeling of class analogy (SIMCA), naive Bayes, k-nearest neighbors, and support vector machine were constructed for the discrimination of water-adulterated milk. Preprocessing methods including Savitzky-Golay convolutional smoothing, Savitzky-Golay filtered derivative, multiplicative scatter correction, standard normal variate, vector normalization (VN), and min-max normalization were applied to the original spectra. Additionally, models such as one-dimensional convolutional neural network, partial least squares regression, and support vector regression (SVR) were established for the prediction of water content in milk. The results showed that the SIMCA model achieved 100% discrimination accuracy = 1, sensitivity = 1, specificity = 1, precision = 1, F₁-score = 1 in the analysis of water-adulterated milk. In the prediction of milk water content, the VN-SVR model performed the best (coefficient of determination for the training set R_C² = 0.9996, root mean square error for the training set = 0.0477%, coefficient of determination for the test set R_P² = 0.9992, root mean square error for the test set [RMSEP] = 0.066%), and also exhibited the best performance in model validation (R_P² = 0.9760, RMSEP = 0.2554%). This study provides theoretical references and technical support for dairy product quality control and food safety regulation.

This study was conducted to investigate the drying kinetics and internal and external mass transfer characteristics of walnuts for an understanding of the drying mechanism. The drying characteristics, mass transfer characteristics, and color of walnut during hot air drying (HAD) were investigated under different initial moisture content (IMC) (0.35, 0.39, and 0.43 g water/g wet mass) and drying temperatures (50, 60, 70, and 80 °C). The results indicated that the IMC and drying temperature both have significant effects on the drying process of walnut, showing the higher the IMC, the longer the preheating time, the smaller the effective moisture diffusivity (D_eff) and mass transfer coefficient (h_m), and the longer the drying time, but reverse results for drying temperature. The values of D_eff and h_m for walnut ranged from 4.94 × 10⁻¹⁰ to 1.44 × 10⁻⁹ m²/s and 1.24 × 10⁻⁷ to 3.90 × 10⁻⁷ m/s, respectively. The values of activation energy for moisture diffusion and mass transfer ranged from 21.56 to 23.35 kJ/mol and 28.92 to 33.43 kJ/mol, respectively. Multivariate linear prediction models were also established for estimating the D_eff and h_m as a function of the HAD process parameters. The drying temperature has a greater effect on the walnut kernel lightness than the IMC. The Verma et al model could be used to describe the HAD process of the walnut. The findings contribute to the understanding of moisture transfer mechanisms in walnuts and have practical value for the evaluation and improvement of drying systems.

Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900–1800 nm range, six preprocessing methods—Savitzky–Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)—were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.

The practical problems of existing methods of walnut cracking under compression loading, including incomplete walnut-shell crushing, broken walnut kernels, and so on, are widespread in walnut processing and are constraints that hinder mechanized walnut processing. Therefore, attempts have been made to design and optimize a multi-point extrusion walnut cracking device. For this, walnuts were fed manually into a cracking unit through the hopper. The tangential force of the grading roller graded the walnuts and dropped them into the gap between the rotating cracking roller and extrusion plate, causing them to crack. The developed machine was tested and the parameters were optimized using a central composite design (CCD). The objective functions involving the cracking angle (CA: 0.17, 0.27, 0.52, 0.76, 0.86°) and roller speed (RS: 63, 75, 105, 135, 147 r/min) were calculated. The shell cracking rate (SCR), whole kernel rate (WKR), and specific energy consumption (Es) regression models were established using the quadratic regression orthogonal combination test and the parameters were optimized using MATLAB software. The results showed that the most significant factors for the RS were the linear terms of the SCR and WKR, whereas for the CA the most significant factor was the linear term of the Es. The interaction term of the two factors had a significant effect on the three indicators. The optimal parameter combination was determined to be 0.47° for the CA and 108 r/min for the RS. On this basis, the adaptability test showed that the cracking device had a better cracking effect on walnuts with a gap between the walnut shell and kernel greater than 1.6 mm and a shell thickness less than 1.2 mm. The results have practical significance for the design of walnut cracking devices.

To understand the moisture transfer mechanism of walnuts during the combination of hot air (HA) and microwave–vacuum (MV) drying (HA-MVD) process, the drying characteristics and moisture diffusion characteristics of walnut during HA-MVD were investigated. The results indicated that the HA-MVD of walnuts occurred mainly in the falling-rate stage. The value of effective moisture diffusivity (D_eff) dropped continuously with the decrease in moisture content (MC) during the HA drying, while switching to MV drying could truncate the decrease in D_eff and still maintain a high value until the end of drying. The HA temperature, MC of the transition point, microwave power, and MV thermostatic temperature have significant effects on the moisture diffusion characteristics of walnuts. The values of D_eff for walnuts ranged from 2.33 × 10⁻⁹ m²/s to 6.89 × 10⁻⁸ m²/s. The third-order polynomial prediction model of D_eff related to the sample MC and drying conditions was established to describe the dynamic change in the D_eff of walnuts during the HA-MVD process. The application of MVD in the final stage of drying could rapidly increase the internal vapor pressure of the walnuts, accelerate the diffusion speed of the internal moisture, and re-enhance the drying rate. The findings have practical value for the development of efficient and energy-saving drying methods in the walnut industry.

To determine the optimal fertilizer discharging performance, a spiral fertilizer applicator was designed according to orchard agricultural requirements. The influence of different parameter combinations of the spiral speed, blade diameter, and pitch on the coefficient of variation (CV) of the fertilizer discharge uniformity was predicted using a neural-network-based model by using the Box–Behnken design (BBD) test. According to the extracted results, the neural network model has a good prediction ability, with the determination coefficient of the model and the mean relative error reaching 0.99 and 2.29%, respectively. The impact of the fertilizer discharge parameter combinations on the discharging performances was examined from both macroscopic and microscopic perspectives. During the fertilizer discharge process, the openness formed between the spiral blades and fertilizer outlet presented periodic changes with the continuous rotation of the spiral blade, thus resulting in the uneven discharge of the fertilizer particles. In addition, there are interacting force chains among fertilizer particles, which are not broken in time during the fertilizer discharge procedure, thus resulting in uneven fertilizer discharge. With comprehensive consideration of the fertilizer discharge efficiency, the fertilizer discharge effect, and CV of the fertilizer discharge uniformity, the spiral parameter combination of the fertilizer discharge after neural network optimization are as follows: rotating speed of 47.6 rpm, blade diameter of 90 mm, pitch of 60 mm, and CV of fertilizer discharge uniformity of 19.05%. Under this optimal spiral parameter combination, the fertilizer discharge effect and discharge efficiency were considered to be relatively good. Our work provides references for the design optimization of the spiral fertilizer applicator and fertilizer discharge parameter combination.