Journal of Food Process Engineering最新文献

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.

This research primarily aimed to conduct kinetic modeling of bioactive compound extraction from Sacha Inchi cake using ultrasound-assisted extraction (UAE). To achieve this, a Face-Centered Experimental Design (FCD) was employed to identify the optimal conditions for solvent ratio, extraction time, and power to quantify total extraction yield (TEY), total phenolic content (TPC), total carotenoid content (TCC), and antioxidant content using DPPH and ABTS assays. The results were optimized through response surface methodology and adjusted based on their quadratic models for TEY, TPC, TCC, DPPH, and ABTS to support kinetic modeling. The optimal solvent concentration (ethanol 25% v/v) was used to study extraction kinetics, and modeling was performed for the variables TEY, TPC, TCC, and ABTS, except for DPPH, as it was not feasible due to poor model fit. Among all the models, the power law showed a strong correlation with the experimental data for extracting bioactive compounds from Sacha Inchi cake. Overall, the UAE was more effective than conventional methods, and bioactive compounds in Sacha Inchi cake were successfully identified.

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.

The purpose of this study was to develop a novel snack bar formulation incorporating hemp seeds, flax seeds, and nettle leaves—three nutrient-dense ingredients with complementary functional attributes—to achieve an enhanced nutritional profile and antioxidant potential compared to conventional formulations. The bar formulation with the highest sensory scores was 35% honey, 25% hempseed, 10% flaxseed, 10% nettle, 10% hazelnut and 10% peanut. The nutritional, physicochemical, functional and sensory properties of the developed bar were investigated. The optimized bar contained 15.97% protein and 22.82% fat, making it a suitable choice for meeting daily calorie needs. It had a high-quality protein content, with nearly 40% essential amino acid content and an Arginine/Lysine ratio of 1.17. Additionally, the omega-6/omega-3 ratio was determined to be 2.35:1. The shelf life of the developed bar was defined as 9.04 ± 0.6 months at 25°C. After 4 weeks of storage at 25°C, color properties remained unchanged, while hardness and total phenolic content increased. Sensory evaluation revealed that appearance and flavor were unaffected, whereas texture and taste decreased. This innovative approach combines high-protein sources and bioactive components into a practical snack format, addressing both consumer demand for healthy options and the scientific gap in functional snack development.

In view of the problems of local overheating and low drying efficiency of rice caused by uneven energy distribution in the traditional microwave drying process, this study designed a mobile microwave heat source drying system and systematically explored the influence of mobile microwave drying on the drying uniformity of rice by combining simulation analysis and experimental testing. The moisture content variation of rice under different working conditions was quantitatively analyzed using a three-factor, three-level orthogonal experiment, and the study further explored a collaborative regulation strategy for moving the heat source across the electromagnetic field, temperature field, and moisture field based on the COMSOL multi-physics coupling model. The experimental results show that the drying efficiency is increased by 11%, and the temperature uniformity and drying efficiency of the rice layer are significantly improved by changing the microwave field distribution through the mobile heat source under the conditions of microwave intensity of 12 W/g, initial moisture content of 14.65%, and heating intermittency ratio of 1:3.

Modeling the drying process is essential for optimizing efficiency, reducing energy consumption, and improving product quality in food and agricultural systems. Over time, modeling approaches have progressed from empirical and semi-empirical correlations to theoretical and diffusion-based models, which offer insight into transport mechanisms but are often limited in adaptability and real-time prediction. Computational and multiphysics frameworks have enhanced accuracy by coupling heat, mass, and momentum transfer phenomena; however, their high computational cost restricts widespread industrial use. The advent of machine learning (ML) has transformed drying analysis by enabling data-driven, adaptive, and real-time modeling. Techniques such as artificial neural networks (ANN), support vector machines (SVM), random forest (RF), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid models provide superior flexibility in handling complex, nonlinear, and multivariate drying data. This review presents a comprehensive overview of the evolution of drying process modeling—spanning empirical, theoretical, computational, and ML-based approaches—across four critical domains: (i) drying kinetics and moisture prediction, (ii) product quality evaluation, (iii) energy and exergy analysis, and (iv) process control and automation. The synthesis highlights the transition toward intelligent, data-driven drying systems with improved prediction accuracy, optimized energy use, and enhanced product quality.

Post-harvest losses in fruits and vegetables remain a major barrier to profitability and food security for smallholder farmers in India, with spoilage rates often exceeding 30% due to the lack of affordable and accessible storage infrastructure. This review critically examines a range of low-cost, zero-energy, and sustainable storage technologies—including Zero Energy Cool Chambers (ZECC), evaporative cooling systems, solar dryers, clay pot refrigeration, and underground storage with a focus on their practical applicability in rural, resource-constrained settings. Unlike conventional cold storage and Modified Atmosphere Packaging (MAP), which demand high energy input and significant capital investment, the solutions reviewed here are characterized by simplicity, scalability, and reliance on natural processes, making them suitable for decentralized, small-scale use. The study evaluates the technical effectiveness, economic feasibility, and socio-environmental benefits of these systems, while also highlighting their role in extending shelf life, improving market access, and enhancing income stability for farmers. By contextualizing these technologies within India's unique agricultural challenges, the review not only identifies gaps in current adoption and awareness but also explores how supportive policy frameworks, community-based models, and climate-resilient innovations can accelerate their uptake. The findings contribute to the broader discourse on sustainable post-harvest management and underscore the urgent need for localized, inclusive solutions that address both economic and environmental sustainability in smallholder farming systems.

Mulberry seed, a byproduct of mulberry fruit processing, is a high oil content oilseed with abundant unsaturated fatty acid and physiologically active substances. The impact of extraction methods on composition and quality of mulberry seed oil (MSO) is critical for its commercial development, however the effect of extraction on its quality is still unknown. Five extraction methods, including cold pressing (CP), hot pressing (HP), Leaching extraction (LE), supercritical CO₂ (SFE-CO₂), and micro-aqueous extraction (MAE), were adopted to prepare MSO. The effect of extraction methods on the yield, composition and antioxidant activities of MSO was analyzed in this paper. Principal component analysis (PCA) and Mantel test were used for estimating the effect of different extraction methods on oil quality. Results revealed that the yield of MSO extracted with different methods was in range of 61.52%–90.57%, with the SFE-CO₂ gained the highest oil yield. The free fatty acids (FFA) content and PV from 3.18%–4.84% to 1.34–4.19 meq/kg, respectively. MSO was rich in linoleic acid (76.39%–77.47%). A total of 25 TAG components were identified in MSO, LLL was most abundant TAGs in MSO (37.86%–39.15%), and different extraction methods showed no significant differences on the fatty acids and TAG content. MSO contained β-sitosterol (382.11–436.71 mg/100 g), Δ-avenasterol (51.25–64.32 mg/100 g), campesterol (27.04–32.46 mg/100 g) and total tocopherols (2306.80–2849.26 mg/kg), and the DPPH radical scavenging ability of the polar components and whole oil in MSO were 2445.21–3146.1 mg α-TE/kg and 1341.17–1438.73 mg α-TE/kg. Mantel test indicated that the antioxidant capacity of MSO was positively correlated with the γ-tocopherol, β-tocopherol, Δ-avenasterol, and campesterol content. PCA analysis showed that the comprehensive quality of MSO ranked as followed: SFE-CO₂ > CP> MAE> LE> HP. The study provides new insight for the MSO process, and this study is beneficial to the development of MSO in the edible oil industry.

This research studies Coffea arabica L var. Castillo coffee fruit elastic properties using experimental techniques and finite element analysis (FEA). Young's modulus and Poisson's ratio for both fruit and bean at several stages of ripeness were measured through indentation and compression testing to evaluate mechanical behavior. Three simulation approaches in FEA were utilized, successively developing material characterizations from isotropic to orthotropic. The first simulation, with isotropic material characterizations, showed a significant deviation in agreement with experimental observations. In contrast, the second simulation, with incorporated properties derived through indentation, showed increased accuracy but continued to have discrepancies for overripe and ripe fruits. By contrast, in the third simulation, an orthotropic model with parametric variation in Young's modulus closely agreed with experimental observations in terms of compression, accurately depicting anisotropy in mechanical properties in coffee fruit. The observations can be utilized in optimizing mechanical harvesting and postharvest processing machinery, such as pulpers and sorters, with consideration for mechanical features in relation to stage of ripeness. Future studies with increased refinement in representing materials and including dynamic loading will have a significant impact in terms of predictive accuracy relevant to coffee processing operations.