Journal of Food Measurement and Characterization最新文献

We elucidated the effects of nitroso-hemoglobin-Arabic gum (N–H + GA) conjugates on the quality and safety of inoculated minced beef during cold-storage by investigating their microbiological and chemical changes, volatile compounds and amino acid profile. Results showed that N–H + GA significantly reduced the total bacterial counts, Salmonella, Pseudomonas, and Staphylococcus on day 7 of stored minced beef (p ≤ 0.05). Meanwhile, with the increasing storage time, water relation time and water populace ratios also fluctuated. N–H + GA significantly declined the decreasing ratio of T_21-values in refrigerated and freeze–thaw-refreeze minced beef compared to the inoculated beefs. Furthermore, N–H + GA (5 g/kg) maintained the levels of different amino acids, namely Glu, Cys, Asp, Thr, Ser, Ala, Ile, Leu, Tyr, Lys, His, and Arg. It also kept the volatile components than the control treatment after 3 weeks of frozen/refrozen storage. This is mostly due to the binding between hemoglobin-glycoprotein conjugates with myosin via H-bonds and electrostatic forces as supported by Lib-dock outcomes. Our results implied that using N–H + GA, as a natural colorant-based conjugate, could maintain the safety and quality of minced beef during freeze–thaw-refreeze.

In Mexico, beans rank as the third most important crop in terms of the area planted. Each person is estimated to consume approximately 10 kg/per year of beans. The national bean production for 2024 is projected to reach 918 million metric tons. As part of this study, the levels of potentially toxic elements were determined in 30 bean samples from four varieties: pinto, black, Peruvian, and mayflower. The analysis utilized high-resolution continuum source graphite furnace atomic absorption spectrometry (HR-CS GF AAS) to determine levels of cadmium (Cd) and lead (Pb), hydride generation graphite furnace atomic absorption spectrometry (HG-GF AAS) to measure arsenic (As) and antimony (Sb), and a direct mercury analyzer for mercury (Hg) detection. The limits of detection for As, Cd, Hg, Pb, and Sb were 2.74, 1.52, 0.131, 15.9 y 2.98 µg/kg, respectively. Concentrations (µg/kg) ranged from 11.1 to 164 for As, 5.12 to 44.7 for Cd, 0.560 to 3.74 for Hg, 105 to 202 for Pb, and 13.1–51.3 for Sb. While non-cancer risks were considered negligible for both adults and children, significant cancer risks were identified in certain samples for adults. These findings emphasize the critical need for ongoing monitoring and regulatory measures to ensure food safety, especially regarding potentially toxic elements. They also highlight the necessity for continued research to protect public health.

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The objective of this study is to investigate the optimal ultrasonic power for producing microemulsions with Ocimum gratissimum L. essential oil and to evaluate their physicochemical properties. The effects of three powers (125 W, 250 W, 375 W) on chitosan-based Ocimum gratissimum L. essential oil emulsion were examined by multi-frequency ultrasound-assisted homogenization. The results indicated that the physicochemical properties of chitosan-based Ocimum gratissimum L. essential oil emulsion were optimized under ultrasonic-assisted homogenization of three frequencies (20, 28, and 40 kHz combined) at 125 W for 15 min. Compared to the control group, significant reductions in particle size were observed in the emulsion treated with 125 W multi-frequency ultrasound (average particle size was 1.87 ± 0.01 μm; zeta potential was 13 ± 0.53 mV). Furthermore, the stability and antioxidant activity of the emulsion showed marked improvements (the scavenging rates of DPPH and ABTS increased to 87.57% and 81.71%, respectively). In addition, scanning electron microscopy and confocal laser scanning microscopy images revealed that 125 W multi-frequency ultrasonic-assisted homogenization emulsion droplets were evenly distributed and exhibited the highest encapsulation effect. These findings provide valuable insights into developing food-grade chitosan-based emulsions containing Ocimum gratissimum L. essential oil for functional food production.

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Proteins and polysaccharides are two essential nutrients in the human food system that can significantly influence their structural and functional properties. Pea protein-polysaccharide natural mixtures (PPMs, namely PPM1, PPM2, PPM3 and PPM4) were extracted from defatted pea powder through different extraction processes. PPM1 is derived from the supernatant of a neutral aqueous solution of defatted pea flour. The precipitate is subjected to alkaline dissolution at pH 9.0, yielding a supernatant and a precipitate (PPM3). A portion of the supernatant is lyophilized to produce PPM2, while another portion undergoes acid precipitation at pH 4.0 to yield PPM4. The protein and soluble polysaccharide content, SDS-PAGE of protein, solubility, thermal property, water and oil holding capacity, emulsion properties and foaming properties of these mixtures were examined in this work. The results indicate that PPM1 and PPM2 contain significantly higher (p < 0.05) soluble polysaccharide contents compared to PPM3 and PPM4. The SDS-PAGE analysis of PPMs reveals distinct variations in protein composition during the various extraction processes. Notably, PPM1, PPM2 and PPM4 demonstrated a gradual decrease in solubility from pH 2.0 to 4.0, followed by an increase from pH 4.0 to 10.0. However, the solubility of PPM3 rose as the pH increase from 2.0–10.0. FTIR analysis suggests that PPM2 is dominated in β-sheet, PPM1 and PPM4 were primarily composed of random coil; additionally, a high content of β-sheet and β-turn is observed in PPM3. Furthermore, PPM2 demonstrates the highest (p < 0.05) denaturation temperature of 98.19 °C compared to W1(70.7 °C), W3(77.74 °C), and W4(72.28 °C). PPM1 exhibits the best WHC and OHC relative to other PPMs. PPM1, PPM2 and PPM4 exhibited the better emulsifying property (p < 0.05) than PPM3. At pH levels ranging from 2.0 to 10.0, PPM1 and PPM2 displayed significantly higher (p < 0.05) foaming capacity and foam stability than both PPM3 and PPM4. Especially for PPM1, which presented the highest (p < 0.05) foaming capacity across the pH range of 2.0–10.0. This work provides valuable insights into the properties of PPMs and will be beneficial for expanding their application respect in food industry.

This study investigates the effects of tea polyphenols and their compound preservatives on the post-harvest quality of sweet cherry fruits. Specifically, it compares and analyzes the effects of three treatments: tea polyphenols (TP), tea polyphenols combined with natamycin (TN), and tea polyphenols combined with nisin (TI). The research focuses on various quality parameters, including fruit texture, color difference, weight loss rate, and soluble solids (TSS) during storage, using the ‘Brooks’ and ‘Summit’ cultivars as subjects. The results indicate that there was a significant decrease in hardness, chewiness, resilience, and springiness with the extension of storage time. Meanwhile, color difference parameters including brightness, the red-green axis color index, the yellow-blue axis color index, and chroma value also showed a significant decrease. In contrast, the weight loss rate and TSS exhibit an upward trend. Principal component analysis reveals that ‘Brooks’ demonstrates greater resistance to storage compared to ‘Summit’ under identical preservation treatments and storage duration. All three types of preservation solutions effectively slow the decline in fruit quality and color changes for both ‘Brooks’ and ‘Summit’ fruits to varying extents. The preservation effects, in descending order, are as follows: TI > TP > TN > control samples (CK). The combination of the bio-preservatives tea polyphenols and nisin can synergistically extend the storage period of sweet cherry fruits, thereby mitigating quality deterioration and providing a novel strategy for the post-harvest preservation of sweet cherry fruits, which holds significant application value.

With the continuous growth of tomato yields and the need to address the cumbersome task of manual harvesting, the development of harvesting robots presents a promising solution for tomato harvesting. A key challenge lies in improving the success rate of these robots for reliable tomato harvesting without causing damage. This study aims to propose a rigid-flexible coupled gripper to ensure higher success rates in tomato harvesting, while also incorporating an advanced method for detecting fruit ripeness. The paper introduces a flexible hydrogel pressure sensor, featuring a wide detection range, high sensitivity, and excellent stability, integrated into the gripper design. A signal acquisition system based on a sensor array is developed, enabling the accurate capture of force signals during the tomato grasping process. The sensor array collects tactile sequence data in real-time, which, when combined with compressive deformation data from the fruit, forms a comprehensive dataset. To detect tomato ripeness, four classification models—Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP), and Fully Convolutional Networks (FCN)—are implemented. Among the four models, the LSTM network achieves the highest classification performance. The overall recognition accuracy for tomatoes of different ripeness levels, within the same variety and at room temperature, is determined to be 99%. The results demonstrate that the combination of a flexible gripper with hydrogel sensors not only preserves the integrity of the tomatoes but also ensures accurate detection of fruit ripeness. This innovation has the potential to significantly enhance the performance of tomato harvesting robots, contributing to more efficient and automated agricultural practices.

Quality parameters, such as moisture content (MC) and textural properties, are critical indicators reflecting the quality of fruits and vegetables and significantly influence their shelf life. Monitoring these parameters of agricultural products during post-harvest processing, drying, and storage is crucial for ensuring product quality, safety, and cost-efficiency. This study proposes the integration of machine learning (ML) algorithms with hyperspectral imaging (HSI) to effectively estimate the moisture content and texture characteristics like firmness and consistency of sweetpotatoes. In light of this, orange-fleshed and purple-fleshed sweetpotato samples were imaged using a hyperspectral camera with a spectral range of 400–1000 nm. The extracted spectral data underwent preprocessing to select key wavelengths, construct various models, and compare their accuracy and efficacy. The multiplicative scatter correction-competitive adaptive reweighted sampling-radial basis function (MSC-CARS-RBF) model (RMSE = 0.066%, R² = 0.97) demonstrated superior performance for the moisture content prediction, while the standard normal variate-competitive adaptive reweighted sampling-extreme learning machine (SNV-CARS-ELM) model showed the best predicting results for the texture characteristics. The results indicated that selecting key wavelengths can enhance the predictive ability for sweetpotato quality assessment. Furthermore, this study demonstrates that combining HSI with ML algorithms have the potential to improve the quality assessment of sweetpotatoes by enhancing the accuracy, consistency, and speed of evaluating moisture content and firmness, ensuring uniformity in grading, and enabling near-real-time, non-destructive assessment during handling and processing, thereby ensuring a higher quality product for consumers.

Rice (Oryza sativa) is a staple food that provides more than half of the world’s population with the calories they need each day. Peoples in Southeast Asia, relying heavily on rice cultivation, are at an elevated susceptibility to the onset of type II diabetes. Traditional rice varieties found in these regions are classified as genotypes with a low glycemic index, yet they are underutilized due to current consumer preferences. To address this, the study measured the glycemic index of three types of rice flakes: those made from traditional white rice and two varieties, red kavuni and kattuyanam. These flakes were produced at a roller pressure of 145 kg/cm² and a temperature of 75 °C. Participants consumed the flakes, and they were measured for levels of blood glucose using a glucometer with the finger prick method. Glycemic responses were assessed using the comparison of the area under the 2-hour response curve of glucose to that of glucose alone. The study found that traditional rice flakes led to a higher postprandial glucose level after 90 min, while red kavuni flakes peaked earlier, at 30 min. Traditional rice varieties offer superior nutritional properties and provide prolonged satiety. According to this study, milled rice discharges more energy than regular rice flakes.

Sago pith, a potential substrate for liquid sugar production, presents challenges due to its complex lignocellulosic structure and the presence of phenolic compounds. This study aimed to elucidate the structural changes in sago pith during pretreatment, investigate enzyme kinetics, and assess the influence of phenolic compounds in multi-step enzymatic hydrolysis. Phenolic compounds were extracted from sago pith, and pretreated sago pith flour (PSPF) was obtained. Enzymatic hydrolysis employed α-amylase, xylanase, mannanase, and cellulase, with kinetics and phenolic effects analyzed. FTIR and optical microscopy revealed changes in PSPF, including alterations in lignin distribution, cellulose, and hemicellulose concentrations, starch granule disruption, and partial phenolic compound removal. The process primarily solubilized amorphous cellulose, starch, and soluble lignin into the liquid fraction. Following this, multi-step enzymatic hydrolysis using α-amylase, xylanase, and amyloglucosidase significantly enhanced sugar yields by maximizing enzyme–substrate interaction and enabling enzyme synergy, achieving a conversion degree of 95–98% after 216–240 h. This approach also demonstrated potential for extracting phenolic by-products from PSPF through enzyme-assisted methods. Furthermore, phenolic accumulation in PSPF influenced enzyme activity: at lower phenolic concentrations, the compounds acted as activators for α-amylase and xylanase, while at higher concentrations, they inhibited enzymatic activity. This study presents a novel approach by using the entire sago pith, including starch and lignocellulose fractions, and examining the dual role of sago pith phenolic compounds in enzymatic hydrolysis, which has not been explored in previous research.

Present research was undertaken to assess total phenolic content (TPC) and antimicrobial activity of Prunus spinosa L. Combined design including mixture design and response surface methodology (RSM) was used to create experimental points and to determine the best solvent composition for extraction of bioactive components of P. spinosa. To construct model, water, methanol, acetone (A, B, C, 0–100%) were selected as solvents and extraction time (D, 30–60 °C) was used to be categorical process factor. The TPC analysis results for each experimental point ranged from 5.40 ± 0.32 to 108.70 ± 2.14 mg GAE/g. The optimal condition for the highest phenolic compounds was obtained with the point including the mixture containing water: acetone (61.28:38.72, v/v) and 56.27 °C of extraction temperature, which was the most suitable for maximum TPC (108.74 ± 2.806 mg GAE/g). The ANOVA analysis of the combined design showed that TPC response were statistically significant, with determination coefficients of 98.85%, and fit the quadratic x linear regression models. Extracts obtained from the experimental point were evaluated to assess the antimicrobial activity of P. spinosa. The results were evaluated through PROMETHEE. The PROMETHEE results indicate that the extract demonstrating the most significant efficacy against all five bacteria is composed of 50% water and 50% methanol, processed at 60 °C. In conclusion, bioactive compounds from P. spinosa were successfully extracted using the chosen temperatures and solvents, and the antibacterial efficacy of the experimental results derived from the design expert program was thoroughly analyzed using PROMETHEE. The findings indicate that PROMETHEE is applicable for comparing and optimizing the extraction of bioactive chemicals from botanical sources. Moreover, it can be utilized in the development of a product aimed at particular microorganisms or to assess the synergy among various phytochemicals based on their antibacterial and antioxidant characteristics.