Journal of Food Process Engineering最新文献

This study aimed to develop an intelligent ammonia-responsive packaging film by functionalizing bacterial cellulose (BC) with purple potato anthocyanin extract (PPE), aiming to achieve reversible sensing capacity and practical application in food monitoring. Films were prepared with PPE loadings of 0.1%, 0.3%, and 0.5% (w/v) and characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and x-ray photoelectron spectroscopy (XPS), and then systematically evaluated for their comprehensive properties and colorimetric behavior. SEM analysis confirmed the uniform dispersion of PPE, while FTIR and XPS spectroscopy verified its successful incorporation into the BC matrix. Films with different PPE loadings exhibited concentration-dependent colorimetric behavior across the pH range of 2–12. Among all formulations, BC/PPE0.1% was identified as the optimal composition due to its balanced mechanical properties and significantly faster ammonia response compared to other loadings. This optimal film showed excellent reversible sensing capability, with a detection limit of 0.01 M. It was also successfully applied to monitor pork freshness at 25°C, where spoilage-induced color changes provided a clear visual signal. This work establishes BC/PPE0.1% as a promising stimuli-responsive biomaterial, positioning it as a reliable visual indicator for detecting freshness failure in the cold chain, such as during the transportation of high-value meats.

During the drying process of maize kernels (Zea mays L.), variations in moisture and temperature can induce a glass transition, resulting in changes to relaxation properties that ultimately lead to drying-induced cracking. However, the mechanisms by which the glass transition affects relaxation properties and subsequently causes crack formation remain unclear. This study aims to investigate the stress relaxation and glass transition behaviors of various components of maize kernels, as well as to explore the relationship between changes in relaxation properties and drying-induced cracking. Our findings indicate that both glass transition and relaxation properties are influenced by moisture content and temperature. The components of maize kernels exhibit distinct glass transition behaviors, leading to varying changes in their relaxation properties. During the drying process, the pericarp and germ maintain a single state, thereby avoiding crack formation. In contrast, cracks predominantly develop in the endosperm due to significant alterations in relaxation properties associated with the glass transition. As the maize kernels cool, the temperature decreases from the drying temperature to the cooling temperature, facilitating a transition to a glassy state, which primarily contributes to crack formation during this cooling stage. This study provides insights into the mechanisms of crack formation and may assist in optimizing operational conditions for maize drying.

Impacts of citrus fiber as the fat substitute on textural, rheological, sensory properties, microstructure, and stability of the low-fat mayonnaise were investigated. As the fat substitution ratio increased from 20% to 60%, firmness, adhesiveness and apparent viscosity of low-fat mayonnaise decreased, while its oil droplet size increased and color became light. In addition, low-fat mayonnaise displayed higher emulsion stability and lower peroxide value compared to full-fat mayonnaise. Rheology analysis revealed mayonnaise whose oil was replaced with 1.5% modified citrus fiber had higher apparent viscosity, storage modulus and loss modulus than mayonnaise whose oil was replaced with 1% modified citrus fiber. In organoleptic properties, mayonnaise in which 1.5% modified citrus fiber replaced 20% oil achieved similar status and overall acceptability to full-fat mayonnaise. Also, it had the best texture properties. Furthermore, its firmness, viscosity and adhesiveness were 101.58%, 90.23%, 84.07% higher than those of full-fat mayonnaise. The low-fat mayonnaise with 1.5% modified citrus fiber whose fat replacement ratio was 20% had the best stability, sensory, rheological and textural characteristics compared with other low-fat mayonnaises. This suggests that citrus fiber would have excellent potential as a fat substitute in the food industry.

This investigation suggests a practical and economical method for producing carbon quantum dots (CQDs) from used oolong tea leaves using microwave and detection of polyphenols and various antioxidants in tea. Polyphenolic compounds and organic matter in oolong tea waste allow CQDs to be synthesized environment friendly and without the need for harmful chemical additives, orlong purification steps. The High-Resolution Transmission Electron Microscopy, Fourier transform Infrared Spectroscopy, thermogravimetric analysis, x-ray diffraction, fluorescence, and UV–visible spectroscopy were used to confirm that the synthesized CQDs had a 3–10 nm size, were spherical in appearance, had many types of functional groups, were nanocrystalline graphitic domains, and were capable of absorption and emission. Owing to the exceptional and steady fluorescence of the CQDs, we detected quercetin, gallic acid, and EGCG in samples of both black and oolong tea. The quenching method made it possible to detect the intercalator at a sensitive level, and EGCG was found to have the highest quenching response. We found that black tea contained 16.19 mg/L of quercetin, 3.65 mg/L of gallic acid, and 28.65 mg/L of EGCG, while oolong tea contained 15.77 mg/L of quercetin, 3.27 mg/L of gallic acid, and 50.42 mg/L of EGCG. The fluorescence of CQDs was shown to be highly linear as they responded to calibration curves (R² > 0.93). The research shows that tea waste can be recycled into useful nanomaterials, allowing us to use them for sensing and to reduce the use of conventional techniques. What we found makes it possible to create scalable, environmentally friendly systems for food quality checking and identifying antioxidants with nanomaterials made from agro-industrial residues.

Throughout the entire tea bud harvest cycle, the buds undergo a continuous transition from emergence to maturity, accompanied by substantial morphological changes. These dynamic changes introduce significant intraclass variations, under which traditional tea bud detection algorithms often exhibit limited generalization capability and reduced stability. To reduce false negatives caused by morphological variations across different maturation stages, this study builds upon LW-DETR and proposes a probabilistic framework for tea bud objectness estimation. The framework improves confidence prediction by modeling common features shared among tea buds. First, a common feature encoder is introduced to project each embedding into a tea bud common feature subspace, where the representations are encouraged to learn shared characteristics and reduce interembedding feature discrepancies. Then, probability distribution estimation and objectness likelihood maximization for known tea bud objects are alternated within the common feature subspace. This iterative process enables the estimation of objectness probabilities for different bonding boxes, which are subsequently used to refine the original model's output. Experimental results show that, on the 25_ZC108 dataset split by collection date, the proposed method achieved the best performance across all 7 days, with a maximum AP50 of 85.10%. Compared to state-of-the-art detection methods, the introduced probabilistic framework demonstrates superior detection accuracy and enhanced generalization capability, particularly on datasets characterized by substantial intra-class variations. Notably, the highest recall improved by 31.13%, effectively reducing false negatives. Furthermore, the proposed approach achieved the best results on datasets collected across different years and tea cultivars, which attests to the robustness and effectiveness of the common-feature-based probabilistic objectness framework.

The aim of the study was to develop a recipe for innovative and health-promoting extruded corn products enriched with powdered hazelnut or walnut oil cakes and to assess their quality. Nut oil cake is a by-product of the food industry, created during oil pressing. Corn semolina was replaced with nut oil cakes at the level of 10%, 20%, and 30%. The content of basic nutrients, the expansion ratio (ER), the color in the CIEL*a*b* space, the content of total polyphenols (TPC), antioxidant potential (AA), textural properties were determined, and an analysis of the smell was carried out using an electronic nose. It was found that replacing corn semolina with nut oil cakes increased the content of protein, dietary fiber, and ash in the tested extrudates, and the content of these components increased with their increasing share. Similarly, an increase in TPC and AA was observed in extrudates containing nut oil cakes. The highest ER values were observed in samples with 10% and 20% of walnut oil cakes. The hardest were extrudates with 30% of hazelnut oil cakes. Nut oil cakes caused a decrease in the L* parameter value, that is, a significant darkening of the extrudates compared to the control sample. The analysis of the smell allowed us to identify in the extrudates odorous substances belonging to five groups, such as aldehydes, alcohols, ketones, pyrazines, and terpenoids. The conducted studies showed the possibility of using by-products from the food industry, such as nut oil cakes, in the production of innovative corn extrudates with interesting functional properties and improved nutritional value.

Eating shellfish contaminated by heavy metals is harmful to human health, so it is imperative to detect such contamination. In this paper, a new model for rapid and nondestructive detection of heavy metal-contaminated shellfish, based on hyperspectral image technology and a machine learning algorithm, is proposed. First, hyperspectral images of shellfish samples are collected and the Competitive Adaptive Reweighted Sampling (CARS) algorithm is used to select wavelength variables. Then, the Linear Discriminant Analysis (LDA) algorithm is used to reduce the dimension of hyperspectral image data. Finally, the K-nearest Neighbors (KNN) classification model is applied to classify and detect heavy metal-contaminated shellfish. For binary classification of single heavy metal-contaminated and healthy samples, the accuracy of the CARS–LDA–KNN model for detecting heavy metal contaminated samples exceeds 99.91%. For multi-classification of cadmium-, copper-, lead- and zinc-contaminated and healthy samples, the accuracy reaches 100%. Moreover, the model's performance is virtually unaffected by the number of samples or the proportion division of the training set and test set, and the model exhibits strong robustness and generalization ability. For these reasons, the CARS–LDA–KNN method was established for an accurate recognition model of shellfish contaminated by heavy metals, thus it provides a new means for rapid detection of heavy metal-contaminated shellfish.

Although microwaveable foods are expanding, developments in the bakery category is limited due to challenges like lack of crust formation and browning when microwaved. These drawbacks can be addressed through active packaging such as the use of susceptor materials. The effects of power, time, and formulation were evaluated on the physicochemical parameters of bread samples heated by microwaves without (NSC) and with (SC) a susceptor package, and compared to conventionally baked breads. Additionally, the heating profiles were assessed under all microwave conditions, and empirical mathematical models were developed to describe this process. Time, power, and formulation were observed to have a significant effect (p < 0.05) on the moisture content, weight loss, and texture parameters for both NSC and SC samples. In the case of the NSC samples, the variables also had a significant effect (p < 0.05) on specific volume. The NSC samples showed significant differences (p < 0.05) compared to the control samples (CF1 and CF2) in moisture content, weight loss, and specific volume. In contrast, no significant differences were found in the color parameters between the SC and control samples. Both models fit the experimental data correctly (R² > 0.99) for both cases (NSC and SC samples). Furthermore, the parameter k₁ and k₂ of the applied models were related to the heating rate of the samples. Microwaved breads in susceptor packaging materials were comparable to conventionally baked breads.

Accurate and efficient separation of saffron (Crocus sativus L.) components, particularly stigma from style, is essential for ensuring product quality and commercial value. Manual separation methods are labor-intensive and prone to variability, necessitating automated, non-destructive alternatives. In this study, the dielectric properties of saffron tissues were systematically investigated over a range of temperatures (20°C–60°C), moisture contents (10%–18%), and frequencies (10–1000 kHz) to assess the potential for electrostatic-based separation. The results revealed marked dielectric contrasts between stigma and style, with notable differences in the real dielectric constant (ε'), particularly at elevated moisture levels and frequencies. Dielectric separation performance was most influenced by moisture content, followed by frequency and temperature. Three-dimensional response surface plots and sensitivity analysis confirmed the dominance of moisture-driven dielectric behavior, with nonlinear interactions reflecting complex physical mechanisms such as interfacial polarization. These findings underscore the intrinsic suitability of dielectric properties as reliable discriminators for saffron tissue separation. While Support Vector Machine (SVM) regression was used to model the dielectric responses, achieving high predictive accuracy (R² > 0.98), the core contribution of this study lies in revealing the physicochemical dielectric mechanisms that support the development of precision electrostatic separation technologies for industrial saffron processing.

The change in food culture is forcing people to move away from a traditional, balanced diet and leading them to consume food low in essential nutrients and high in calories, causing the occurrence of non-communicable diseases. Millet is one of the major food sources in developing countries, rich in essential nutrients and containing balanced amounts of micronutrients. Although essential nutrient-rich millets are a basic diet in developing countries, people are consuming millet-based nutrient-deficient diets due to the low bioavailability of nutrients. Millet processing has been going on for centuries, but it has not yet reached the level where it can provide an ideal functional food. Therefore, sustainable approaches should be applied in a way that improves nutritional properties by modulating biochemical pathways through various processing methods. Lactic acid bacteria-based fermentation of millet is a sustainable but independently unproven way to access essential nutrients required for proper health function. Biological fortification, which biologically enhances the essential nutrients of millet by manipulating the biochemical pathways of lactic acid bacteria, adds large quantities of various biologically active components in addition to essential nutrients. It helps to improve overall health and physical condition and reduce the possibility of disease. In this review, promising approaches to improve the functional properties of millet are discussed, including manipulation of biochemical pathways by modifying fermentation conditions and providing stress non-thermal conditions to manipulate metabolic pathways, which lead to increased nutrient bioavailability. Thus, modified lactic acid bacteria-based fermentation improves consumers' health and enhances their immunity.