Journal of Food Process Engineering最新文献

Moisture content of maize is a key indicator in maize production, processing and storage. In order to realize the rapid and accurate detection of maize grain moisture content, the original output signals such as input/output voltage and phase angle at 26 frequency points within the range of 10 kHz to 1.25 MHz were measured by using an oscilloscope, a signal generator and a self-made parallel plate capacitor. The six types of electrical parameters of maize under different moisture content conditions, namely capacitance, resistance, dielectric constant, dielectric loss factor, dissipation factor and quality factor, were calculated. Feature frequencies were selected using the Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm, and modeling was performed using the Support Vector Regression (SVR) and Random Forest Regression (RF). The experimental results showed that the coefficient of determination R² of the constructed SPA-SVR model reaches 0.989, and the root mean square error (RMSE) is 0.93. This method broke through the limitations of the existing detection technology that underutilizes the range of the frequency domain, and by establishing a nonlinear relationship between the electrical characteristics of the wide-frequency domain and the moisture content, it provided an effective means of rapid and accurate detection of the maize grain moisture content.

Recent developments in carbon quantum dot (CQD)-based detection technologies, with a focus on applications in food and environmental safety. CQDs, an emerging nanomaterial, are widely used to detect pollutants such as heavy metals, pesticides, microbial contaminants, and organic pollutants. The tremendous optical properties, biocompatibility, low toxicity, and tunable surface functionality of CQDs make them suitable for various real-time monitoring. It also discusses the role of CQD-based sensors in detecting contaminants in complex matrices, such as food and water, as well as in detecting contaminants using fluorescence, electrochemical, and upconversion emission methods. These sensors can address current field tasks, including expanding the detection field, improving cost-effectiveness, and increasing accuracy. In addition, the synthesis of novel materials and the development of advanced, targeted sensors by blending CQDs with other nanomaterials are expected to enhance food safety control and risk assessment for environmental protection.

Sweet cherry (Prunus avium L.) is a highly perishable temperate fruit of significant commercial importance, particularly in the Himalayan belt of India, with Jammu and Kashmir contributing over 94% of national production. For canning and processing, grading is essential for uniform quality, consumer acceptance and market competitiveness. At present, cherry grading in India is being performed manually, which is labor intensive, inconsistent and inefficient. To address this limitation, a power-operated cherry grader was designed and developed to evaluate its technical performance and economic feasibility. The grader was tested at nine combinations of feed rates (400–800 kg/h) and drum speeds (8–12 rpm), using “Double” cherry variety and the results were compared with the traditional manual method. The developed machine achieved the highest throughput capacity (660.22 kg/h), effective throughput capacity (651.31 kg/h) and grading efficiency (98.58%) at the operating combination of 800 kg/h feed rate and 12 rpm drum speed (F3R3). The grading error (1.42%), ungraded fruit percentage (0.82%) and fruit damage (0.31%) remained minimal, demonstrating high precision with negligible quality loss. In contrast, manual grading showed much lower efficiency (66.55%), higher grading error (33.45%) and excessive labor requirements (93.89 man-hours/tonne). Economic evaluation revealed a benefit–cost ratio of 2.3, a break-even point of 6897.7 kg and a payback period of 1165 operational hours, confirming its commercial viability. Overall, the developed cherry grader significantly reduced drudgery, improved grading uniformity and ensured economic profitability, making it a promising technology for large scale adoption in the cherry canning industry.

Honey crystallization studies are increasingly popular. Paradoxically, it is difficult to accept because it makes it harder to satisfy the consumer's senses. In this research, spectrum analysis was used to provide information about honey crystallization and its source using machine learning. All honey samples were maintained at +4°C during crystallization. The honey sample from Ankara city crystallized in almost two weeks, from Manisa city crystallized in 1 month, and from Artvin city crystallized in 2 months. The research measurement system consists of a shooting tent, a Celestron MicroDirect digital microscope, and Theremino spectral imaging software. Each sample was placed in a 9 × 1.5 cm Petri dish, with a 0.5 cm space from the digital microscope. Each honey sample in a Petri dish was then measured from 100 distinct sites, and the average measurement was recorded as the final data point for both pre- and post-crystallization states. All honey samples yielded a total of 1.634.400 spectral measurements. As a result, it has been determined that honey samples from Artvin city (Black Sea), Ankara city (Central Anatolia), and Manisa city (Aegean Region) of Türkiye can be separated based on wavelength and light intensity percentiles by using k-Means, k-Medoids, and Fuzzy c-Means clustering algorithms in the KNIME analytics platform. The best results were found as: 0.999 (k-Means), 0.995 (k-Medoids), 1 (Fuzzy c-Means) as control, and 0.998 (k-Means), 0.986 (k-Medoids), 0.998 (Fuzzy c-Means) during crystallization, and 0.999 (k-Means), 0.991 (k-Medoids), 0.998 (Fuzzy c-Means) as crystallized, crystallization clustering quality values, respectively.

The goal of this study was to address the inefficiencies of manual yellow wine jar sealing—characterized by high labor intensity and low productivity—by innovatively designing an automated feeding machine using TRIZ theory. We aimed to resolve technical contradictions in material handling and sealing processes while ensuring compatibility with traditional practices. Initially, the technical contradictions in the sealing and feeding process were analyzed, and a contradiction matrix was constructed to identify pertinent invention principles, including Division, Extraction, Inversion, Periodical Action, Copying, Pneumatic and Hydraulic, Changing State and Composite Materials. Based on these principles, a design scheme for the sealing and feeding machinery was developed. The optimal solution was subsequently determined through hierarchical analysis and fuzzy comprehensive evaluation. This solution has been implemented in the company's production line, achieving an average sealing time of 8 s per unit. This advancement demonstrates significant practical value and represents a novel approach to the development of automated feeding equipment for yellow wine production and sealing.

Pearl millet balls (PMB) were developed to counteract tapioca balls (TB), which are made up of starch. However, the PMB were less accepted by consumers due to their poor textural properties, particularly in terms of surface integrity. To improve the properties and consumer acceptability, hydrocolloids, namely carboxy methyl cellulose (CMC—water-soluble, cellulose-derived polymer), hydroxypropyl methyl cellulose (HPMC), and guar gum (GG), were added to pearl millet flour at three proportions (0.5, 1, and 2 g parts of hydrocolloid per 100 parts of flour). The hydrocolloids added to the flour altered its characteristics and textural and sensory properties, as well as the in vitro digestibility of the balls. The addition of hydrocolloids significantly reduces starch digestibility in PMB by increasing viscosity, which was revealed in a rheological study. Hydrocolloid-added pearl millet balls showed an absence of cracks and fissures, with 2 g of CMC/100 g of PMF having optimal hardness (908–2064 g) and minimal cooking loss (4.86%–5.14%). Rapidly digestible starch declined from 62% to 46% when studied in combination with hydrocolloids. Carboxymethyl cellulose (2 g) added PMB showed a similar resemblance to TB, as indicated by their pasting properties, rheological studies and sensory analysis, with higher consumer acceptance than the 0.5 and 1 g concentrations. These hydrocolloids added to PMB exhibited better textural properties and were consumer acceptable.

This study investigated a novel hybrid cooking approach that integrates ohmic heating with sous vide (O-SV) for preparing camel (Camelus dromedarius) meat. The method was compared with conventional sous vide (SV) in terms of energy efficiency, cooking yield, and physical attributes (pH and color). Camel meat samples were cooked at 70°C, 80°C, and 90°C for 30–120 min. Results demonstrated that cooking temperature and duration markedly influenced pH and color development. Extended cooking reduced redness and lightness but increased yellowness, reflecting protein denaturation and myoglobin oxidation. Compared with SV, the O-SV system significantly improved energy efficiency, requiring less electrical and specific energy input while maintaining comparable cooking yields. The O-SV method also produced distinct color profiles linked to rapid internal heating. These findings highlight O-SV as a promising cooking system for achieving sustainable energy use and desirable physical quality in camel meat preparation.

Polysaccharides, as important bioactive components in medicinal and functional food materials, are key indicators for quality evaluation. Conventional analytical methods for polysaccharides quantification often require laborious sample preparation and may cause irreversible damage to samples, which limits their suitability for routine quality control. To overcome these limitations, we developed a rapid and non-destructive detection approach by integrating hyperspectral imaging with an iteratively retained information variables-artificial neural network (IRIV-ANN) model. Spectral data (400–1000 nm) were processed using the IRIV algorithm, which classified variables into four categories-strong information, weak information, no information, and interference. The optimal subset was then used to construct an ANN model, and its performance was compared with models based on shuffled frog leaping algorithm (SFLA), successive projections algorithm (SPA), one-dimension convolutional neural network (1D-CNN), full-spectrum ANN, and multiple linear regression (MLR). The IRIV-ANN model successfully eliminated redundant variables, reduced model complexity, and improved predictive performance, achieving a coefficient of determination (R²) of 0.9301 and a root mean square error (RMSE) of 0.7348 for the prediction set. The characteristic wavelengths selected were closely associated with vibrational changes in O-H and C-H functional groups, providing chemical interpretability of the spectral features. This method provides an efficient and reliable analytical tool for rapid quantification of polysaccharides and has considerable potential for broader application in the quality control of functional foods, traditional medicines, and other plant-derived materials.