Journal of Food Process Engineering最新文献

To address fruit and branch damage in mechanized sea buckthorn harvesting, this study investigates the dynamic response and damage mechanisms using the Finite Element Method (FEM) and vibration experiments. Vibration parameters were analyzed via Continuous Wavelet Transform (CWT). Key mechanical properties of branches were determined, and a fruit detachment acceleration model, along with damage indices, was established. Results indicated a fruit rupture strength of 1.577 MPa. The main contribution lies in integrating multiple approaches to elucidate vibration-induced damage mechanisms and quantify critical harvesting parameters. The use of optimized vibration parameters, with a frequency of 16.3 Hz and an acceleration of 2.5 g (g = 9.80 m/s²), was shown to improve the fruit harvest rate while minimizing damage. This study offers essential technical insights for designing efficient sea buckthorn vibration harvesters.

Smallholder farmers are vital to agricultural development and food security in developing countries, but face challenges like water waste, land degradation, low crop yields, and high costs due to inefficient conventional irrigation systems. To overcome these challenges, this manuscript proposes a starfish optimization algorithm (SFOA) for the smart center pivot irrigation system (SCPIS), integrating IoT-based environmental monitoring for efficient small-farm management by minimizing water loss. The input data was collected from agriculture crop yield dataset. The proposed SFOA method is used to optimize environmental factors like temperature, humidity, and CO₂ concentration to improve crop growth and resource utilization. The major objective is to minimize water loss by making precision agriculture technology accessible and cost-effective for small farmers, while comparing irrigation strategies to evaluate crop yield, water savings, and use efficiency, and assessing Christiansen's uniformity coefficient (CU) and application efficiency to optimize water use and productivity. The performance of the proposed method is implemented and compared with various existing methods on the MATLAB platform, such as attractiveness-based particle swarm optimization (APSO), Anti-Coronavirus Optimization Algorithm (ACVO), and Genetic Algorithm (GA). The proposed SFOA method reduces its water usage to 35% and the annual benefit increases by 325$ per hectare, demonstrating its economic and environmental benefits improving irrigation efficiency and crop productivity compared to existing methods. The outcomes indicate that the proposed SFOA technique substantially improves irrigation efficiency, crop yield, and water conservation, while enhancing economic returns and energy savings, thereby providing both environmental and financial benefits for smallholder farmers.

To achieve rapid and accurate classification of coated corn varieties, a dataset containing five types of coated corn was constructed, with an original resolution of 640 × 640 pixels, comprising a total of 6715 images. In order to reduce the excessive reliance of classification models on dataset preprocessing, three improved modules—namely the ES-FCA Block structure, LSK-FourBranch Model structure, and WP convolution structure—were proposed based on the YOLOv8 model framework. These three improved modules were sequentially combined to build a new backbone network, leading to the development of a lightweight classification model with multistage downsampling for feature extraction (YOLOv8-ES-LSK), which is specifically designed for the classification of coated corn varieties. Compared with deep learning network models such as EfficientNet, EfficientViT, ViT, ResNet50, and YOLOv11-cls, the proposed model has demonstrated excellent experimental performance, with a maximum classification accuracy of 97.99%, and the number of parameters and computational complexity are 0.77 M and 1.8 FLOPs, respectively. Experimental results indicate that the approach of multiple downsampling-based feature extractions enables effective extraction of features from coated corn varieties with high similarity. Meanwhile, the proposed model achieves both efficient classification and lightweight and efficient performance. Specifically, model inference conducted via ONNX Runtime has yielded favorable classification results, laying a solid foundation for the potential deployment of the model on edge devices. Furthermore, we have also developed a complete set of coated corn sorting system, along with its accompanying nondestructive testing (NDT) software platform.

Fruit leathers offer an innovative and sustainable alternative for fruit preservation, extending shelf life and reducing food waste. This work aims to review and update the knowledge on the production of fruit leather. It analyzes their production process from fruit selection to the final product, with a special focus on formulation and employed drying techniques as well as their effects on preserving organoleptic and nutritional properties. In addition, this work gives information about the effect of additives on the final product quality. Furthermore, advances in the incorporation of hydrocolloids, sugars, and antioxidants to enhance texture, stability, and sensory acceptance are highlighted. This review highlights the lack of standardized analytical criteria, which complicates the comparison of findings across different studies in the field of fruit leather formulation and drying technologies. Promising drying technologies such as refractance window and cast-tape drying warrant further pilot-scale validation and techno-economic assessment. Expanding functional formulations and optimizing sustainability will be key to broader industrial adoption.

Bioactive packaging made from biopolymers can decrease pollution in the environment in addition to prolonging the shelf life of chilled beef. A novel composite antibacterial preservative film was prepared using gelatin (Gel)/chitosan (Ch) as the film material and Litsea cubeba essential oil (LCEO) as the antibacterial agent. Its physical properties, structure, antioxidant activity, and antibacterial performance were studied. When the ratio of gelatin to chitosan was 6:4, the film exhibited good tensile strength, flexibility, and water resistance. Adding 2% LCEO to the film material further enhanced its tensile strength and improved the composite film's hydrophobicity, antioxidant activity, and antibacterial performance. The water solubility and moisture content of the composite film were reduced to 29.73% and 14.91%, respectively, while the antioxidant activity increased to 82.86%. The composite film with 2% LCEO was applied to preserve chilled beef. Compared to the composite film without essential oils, the 10th day pH, thiobarbituric acid reactive substances (TBARS), and total viable count (TVC) decreased by 0.48, 0.34 mg MDA/kg, and 1.83 log CFU/g, respectively. This indicates that the composite film containing Litsea cubeba essential oil has an excellent preservative effect on beef, extending the shelf life of chilled beef by 6 days.

The perishable food cold chain is a vital part of the global food system, mainly ensuring the quality of temperature-sensitive products such as milk, seafood, fruits, and vegetables. However, this system still faces several challenges related to real-time monitoring, data transparency, and proactive demand planning, which often lead to spoilage, food safety violations, and disruptions. In this work, we propose a comprehensive end-to-end system that includes Internet of Things (IoT)-based environmental sensors, blockchain-enabled immutable logging, and a hybrid ARIMA–LSTM model for demand forecasting and spoilage risk detection. The system uses ESP32 microcontrollers with DHT11 sensors at key cold-chain points to collect temperature and humidity data, streamed to Blynk dashboards for real-time visualization and anomaly alerts. Anomalies and product metadata are permanently recorded through smart contracts on a private Ethereum blockchain, while payloads are stored off-chain on IPFS for traceability and auditability. Additionally, historical blockchain logs and external sales data are used to train a hybrid ARIMA–LSTM model that predicts future demand and spoilage risks more accurately. Experimental results show that the proposed model achieves a forecasting accuracy of 90% (R² = 0.90), outperforming baseline approaches on multiple metrics including RMSE, MAE, MAPE, and R². The framework is scalable and data-driven, aiming to improve supply availability and reduce food waste.

Drying is a crucial unit operation in cassava flour processing, as the product quality is largely determined by the drying method employed. The main objective of this study was to optimize the variety, slice thickness, and drying method to maintain the quality of cassava slices for producing high quality cassava flour (HQCF), which serves as a key ingredient in developing gluten free food products. Drying experiments were conducted using three drying methods (open sun, solar, and convective hot air drying), three slice thicknesses (1.5, 2.5, and 3.5 mm), and three varieties (Sree Athulya, Sree Suvarna, and M4). The moisture content of the dried slices ranged from 7.70% to 10.75% (dry basis). Among the tested conditions, convective hot air drying of 1.5 mm slices resulted in the highest drying rate (0.24 g•g⁻¹•min⁻¹) and shortest drying time (180 min), irrespective of variety. Cassava slices of 1.5 mm thickness dried under solar drying retained the most desirable quality attributes, including higher starch content and whiteness. The Sree Athulya variety exhibited the highest starch content (76.27% ± 0.82% d.b.) and a whiteness index of 87.43. Statistical analysis revealed that the interaction among drying method, slice thickness, and variety had a highly significant effect (p ≤ 0.01) on the quality attributes of cassava slices. Solar drying of 1.5 mm thick slices across all the varieties was identified as the optimal condition for producing high quality cassava flour (HQCF). Seven thin layer drying models were applied to describe the drying kinetics of cassava slices. The Page model provided the best fit, as indicated by its high R² and low RMSE and χ² values, confirming its suitability for characterizing thin-layer drying behavior under varying conditions. Thus, solar drying is an efficient method for producing high quality cassava flour from thin (1.5 mm) cassava slices, suitable for developing gluten free food products.