Zhilong Du’s research while affiliated with Chinese Academy of Agricultural Sciences and other places

This study investigated the optical properties (OPs) and Monte Carlo (MC) simulations of light propagation in Healthy Group (HG) and Blackhearted Group (BG) potatoes. The MC simulation of light propagation indicated that both the photon packet weight and the penetration depth were significantly lower in blackhearted tissues than in healthy tissues. The simulation revealed deeper light penetration in healthy tissues than in the blackhearted tissues, approximately 6.73 mm at 805 nm, whereas the penetration depth in blackhearted tissues was much shallower (1.30 mm at 805 nm). Additionally, the simulated absorption energy at both 490 nm and 805 nm was higher in blackhearted tissues, suggesting that these wavelengths effectively detect blackheart in potatoes. The absorption (μa) and reduced scattering (μ’s) coefficients were obtained using Vis-NIR spectroscopy, which represented a notable increase in μa in BH tissues, particularly around 550–850 nm, and an increase in μ’s across the Vis-NIR region. Based on transmittance (Tt), μa and μ’s, Support Vector Machine Discriminant Analysis (SVM-DA) models demonstrated exceptional performance, achieving 95.83–100.00% accuracy in Cross-Validation sets, thereby confirming the robustness and reliability of the optical features for accurate blackheart detection. These findings provide valuable theoretical insights into the accuracy and robustness of predictive models for detecting blackhearted potatoes.

Introduction Blackheart is one of the most common physiological diseases in potatoes during storage. In the initial stage, black spots only occur in tissues near the potato core and cannot be detected from an outward appearance. If not identified and removed in time, the disease will seriously undermine the quality and sale of theentire batch of potatoes. There is an urgent need to develop a method for early detection of blackheart in potatoes. Methods This paper used visible-near infrared (Vis/NIR) spectroscopy to conduct online discriminant analysis on potatoes with varying degrees of blackheart and healthy potatoes to achieve real-time detection. An efficient and lightweight detection model was developed for detecting different degrees of blackheart in potatoes by introducing the depthwise convolution, pointwise convolution, and efficient channel attention modules into the ResNet model. Two discriminative models, the support vector machine (SVM) and the ResNet model were compared with the modified ResNet model. Results and discussion The prediction accuracy for blackheart and healthy potatoes test sets reached 0.971 using the original spectrum combined with a modified ResNet model. Moreover, the modified ResNet model significantly reduced the number of parameters to 1434052, achieving a substantial 62.71% reduction in model complexity. Meanwhile, its performance was evidenced by a 4.18% improvement in accuracy. The Grad-CAM++ visualizations provided a qualitative assessment of the model’s focus across different severity grades of blackheart condition, highlighting the importance of different wavelengths in the analysis. In these visualizations, the most significant features were predominantly found in the 650–750 nm range, with a notable peak near 700 nm. This peak was speculated to be associated with the vibrational activities of the C-H bond, specifically the fourth overtone of the C-H functional group, within the molecular structure of the potato components. This research demonstrated that the modified ResNet model combined with Vis/NIR could assist in the detection of different degrees of black in potatoes.

More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R²P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R²P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.

This study aimed to investigate the effects of osmotic dehydration (OD) pretreatment on the drying and quality of pineapple slices dehydrated using microwave hot air rolling bed drying method. The result suggested the OD pretreatment significantly reduced the moisture content of the food material before drying. It also slightly increased the moisture ratio decreasing rate in the early stage of drying based on the formation of microchannels during the pretreatment. The pretreatment also increased overall temperature and facilitated the transition of free water to bounded water during drying. Based on the pretreatment, the undesired browning of pineapple slices during drying was significantly inhibited. However, the OD pretreatment with 60 % (w/v) sucrose solution caused significant dehydration of the sample before drying, finally resulting in more dramatic browning. Compared with the sample without OD pretreatment, the dried pretreated pineapple slices exhibited an increased amount of collapsed cell structures and pores, due to the cell deformation and water diffusion during the pretreatment. These microstructure changes finally contributed to their increased hardness, springiness and chewiness. Although the pretreatment caused a slightly higher ascorbic acid loss in the final dried food materials, the product with an improved sugar-to-acid ratio was prepared, indicating its improved acceptability. Considering the overall characteristics profile of the dried pineapple slices, the OD pretreatment using 50 % sucrose solution for 24 h was the optimum condition to develop promising dried pineapple slices products.

We developed and optimized microwave hot-airflow vibrating drying (MHAVD) equipment to improve drying efficiency and control the quality of dried agricultural products such as fruits and vegetables. Through three-dimensional modeling and finite element analysis, we simulated and demonstrated microwave source distribution modes and field intensity uniformity. We proposed two flow diversion modes, parallel deflection mode and V-shaped deflection mode, for the hot air distribution system, and we used finite element modeling to analyze them and determine the most optimal modes for use with the dryer. The microwave field strength that achieved the greatest uniformity on the surface of the material tray was used with feeding ports at X = 60 mm, Y = 80 mm, and H = 70 mm. The V-shaped deflector reduced the difference in the wind speeds on the material from 1.1 m/s to 0.36 m/s, thereby significantly increasing the uniformity, although no significant improvement in the local uniformity was noted when the parallel deflector was used. We used fresh balsam pear slices as a representative raw material to measure temperature distribution variations, moisture states, and distribution variations, and we also measured the polyphenol oxidase (PPO) enzymatic activity of the material during the primary drying with MHAVD. The results showed that balsam pear slices could be uniformly and efficiently dried in an environment below 70 °C, and the dry basis moisture content can be reduced from 15.82 to 12.35 in 12 minutes. The NMR/MRI results indicate that mainly free water in the balsam pear slices was decreased in the MHAVD process, with good moisture reduction uniformity. The PPO enzymatic activity decreased from 15.92 U/g to 0 U/g after drying. MHAVD provides a feasible method of enzyme killing, which also has the function of pre-drying for fruits and vegetables. • Highlights • A microwave hot-airflow vibrating drying experimental apparatus was designed • Hot-air and vibrated can improve the uniformity of microwave drying • There is a perfect correlation between moisture content and signal amplitude • Free water can transform into immobilized water and combined water • Microwave pretreatment can inactivate PPO enzyme

In order to protect the quality of peanuts after harvest and increase the efficiency of hot-air drying, a dryer based on superposition and variable temperature control technology was built. Samples layered with a thickness of 60 mm were dried with hot air temperatures of 70 °C, 90 °C, and 110 °C. Variable temperature drying was performed in three stages, including 110 °C hot air with a sample layer thickness of 30 mm, 90 °C hot air with a sample layer thickness of 60 mm, and 70 °C hot air with a sample layer thickness of 120 mm. The change in the temperature, moisture ratio, and state were monitored during the drying process. It was found that the temperature distribution for peanuts was uneven at the beginning stage of hot-air drying, but gradually became uniform as the drying proceeded. Low-field nuclear magnetic resonance and magnetic resonance imaging (LF-NMR/MRI) analysis showed that the water in fresh peanuts was mainly distributed inside the cotyledons. The amount of free water, immobilized water, and bound water varied during the drying process, when the signal amplitudes of the free water and total water were significantly reduced, and the moisture state moved in the direction closely associated with the organic components. The results of infrared thermography and MRI showed that there was satisfactory moisture and heat diffusion uniformity for peanuts during the process. This study will provide a reference for improving the hot-air drying technology of agricultural products.

Non-uniform heating is an important obstacle for applying radio frequency (RF) energy in food processing, especially for the material with high moisture content. To further extend wide applications of the RF heating uniformity improvement based on our previous study with cross electromagnetic wave conductor (EWC), a novel and effective method with cylindrical electromagnetic wave conductors and cylindrical containers was introduced in this study to improve the electromagnetic energy distribution inside the sample with mid-high moisture content. The associated computer simulation model with cylindrical EWC and container was also developed and validated based on RF experimental results to evaluate the heating uniformity. The results showed that the parameters of EWC (diameter and height) had a positive effect on the RF heating uniformity index. The sample treated with cylindrical EWC had better heating uniformity but lower temperature than that treated with cross EWC based on the comparison results. The improved target uniformity index (TUI) and the decreased heating time also indicated the positive effects of cylindrical EWC. A simplified structure for cylindrical EWC was developed and evaluated by computer simulation, which may provide potential applications of the cylindrical EWC to achieve the required RF heating uniformity in mid-high moisture food.