Journal of Food Measurement and Characterization最新文献

Plant proteins are gaining significant popularity as an alternative source of protein due to their health benefits, favorable organic properties, religious considerations, and the absence of ethical and environmental concerns. Additionally, the high prevalence of allergies or intolerances to animal-derived proteins, such as eggs and milk, further contributes to their appeal. Chia seeds offer promising nutritional and functional properties, primarily due to their high-quality protein and well-balanced amino acid profile. The presence of nonpolar amino acids and sulfur-containing amino acids enhances the physicochemical and functional characteristics of chia seed protein. This review explores the impact of various extraction conditions on chia seed protein and their subsequent effects on its structural, physicochemical, and functional properties. The improvement of chia seed protein via enzymatic hydrolysis and its application in the food industry are also discussed. While several reviews address the health benefits of chia seeds, there is a notable lack of comprehensive reviews focusing on the influence of extraction methods on the physicochemical and technological properties of chia seed protein. The aim of this review is to address this gap in the literature.

The field of taste detection faces significant challenges in both technological development and practical implementation, particularly concerning limitations in detection accuracy and sensitivity, difficulties in simulating the complexity of human taste perception, and insufficient sensor selectivity. Cell-based electrochemical taste sensors represent a promising biosensing technology. These sensors utilize living cells as the primary sensing element. Owing to their high biological correlation, they can capture complex biological responses in real-time based on external stimulus intensity, generating electrochemical signals that effectively mimic human taste perception. Unlike previous reviews, this work addresses a gap in the literature by comprehensively describing general strategies for constructing current cell-based taste sensors. The discussed cell-based electrochemical sensors are applicable for taste analysis in pharmaceuticals, food, and food additives. Key construction strategies reported include screening appropriate cell lines and receptor expression, selecting sensor platforms, modifying electrode materials, and transducing electrochemical signals. The favorable performance of these sensors in analyzing the taste of food and pharmaceuticals is highlighted. Currently, this technology predominantly enables only single-taste detection. Future advancements integrating the Internet of Things and Artificial Intelligence, alongside further optimization of sensor construction strategies, are envisioned to yield more efficient and cost-effective taste detection systems.

The quality evaluation of processed rice grains is an important factor in determining market acceptance, pricing, storage stability, processing quality, and overall consumer approval. In the literature, the machine vision methods are predominantly based on supervised machine learning which rely on manual labelling, and therefore, face issues such as time intensiveness, subjectiveness and overlapping classes. Therefore, in this study, a deep unsupervised method Contrastive-RC is developed that leverages contrastive self-supervised learning technique for fine-grained damage classification of milled white rice. Particularly, contrastive-RC is structured to combine SimCLR, UMAP, and HDBSCAN, where self-supervised contrastive learning (SimCLR) is used for feature representation followed by dimensionality reduction using UMAP, and clustering using HDBSCAN. To enable this, a dataset of 20,102 high magnification images (24 MP at 3.8 μm/pixel) of individual rice grains spread across six different types of surface damages is developed. It is demonstrated that the Contrastive-RC successfully clusters the rice grains into six main classes with well-defined attributes, namely normal-damage, chalky-discoloured, discoloured, half-chalky, healthy, and broken. The contrastive-RC achieves this clustering with an accuracy of 0.88, macro-F1 score of 0.82 and a silhouette score of 0.599, indicating a high clustering effectiveness in terms of clear separation between the clusters and their purity. It is demonstrated that the contrastive-RC method can be extended to subclassify these damages based on the damage severity by providing a low-level control, thereby enabling the method to be used in multiple use-cases. The method is fast, versatile and robust towards changes in variables like brightness and grain orientation, thus making it ideal for real world use and extension to other varieties of milled rice. Finally, a comparison of the unsupervised contrastive-RC method with both the existing methods such as K-means and t-SNE, and the supervised CNN-approach is presented. It is shown that the contrastive-RC method outperforms K-means across all the performance metrics (i.e., accuracy: 88% vs. 78%, macro-F1 score: 0.82 vs. 0.67, ARI, NMI and silhouette score: 0.599 vs. 0.523). Further in comparing with CNN-based supervised methods, the contrastive-RC method performs better ins terms of the ability to handle 2.5 times greater number of images, handle data set imbalance due to clustering resilience, and offering a nominally high accuracy of 88% (compared to 98% with CNN) with an unlabelled data.

Debranning results in the loss of peripheral layers (bran/seed coat, aleurone) and germ (G) and associated dietary fibre, proteins, minerals and phytochemicals in the kernel, leading to a concomitant increase in the starchy endosperm in debranned cereals. Such information is not available in millets. Millets (foxtail, little, kodo, barnyard and proso) in their dehusked (whole grain) and debranned (polished) forms were analysed for total phenolic contents. Debranning of millets resulted in a significant decrease in the total phenolic content in all millets. In addition, the millet sections were examined for autofluorescence, which visualized seed coat (SC), aleurone layer (AL), germ (G) and endosperm (E) architecture in a bright blue fluorescence. Debranning resulted in the loss of SC and AL but retained partial G. The areas of these botanical constituents were measured using ImageJ software and were used for computing E: G, E: G + SC + AL ratios. These ratios increased upon debranning in millets, indicating an increase in the E fraction. Starch and proteins in the millet sections were localized using iodine and FCF green stains. Starch was mainly concentrated in the E, while the proteins were distributed in the G, SC, AL, and E regions. Dehusked millets are healthier options in the context of non-communicable disease prevention.

The demand for sustainable food production necessitates the utilization of fish processing by-products. This study investigated the impact of ultrasonic (US) power and duration on the structure and functional properties of protein derived from surimi rinsing water (SRWP). The results revealed that US treatment decreased β-sheet content and increased β-turn content in SRWP, and also induced tertiary structural alterations, leading to increased exposure of hydrophobic groups. In contrast to the control group, the bands of SRWP disappeared at 100 kDa after US treatment. Gel electrophoresis indicated changes in primary structure and degradation of high molecular weight proteins. Scanning electron microscopy (SEM) and particle size analysis showed that particle size initially decreased then increased following US treatment, corresponding to changes in zeta potential. US treatment improved the solubility and emulsifying properties of SRWP, the emulsifying properties increased from 6.29 mg/mL to 7.74 mg/L, leading to smaller droplet size, higher zeta potential, reach the maximum absolute value of 24.08mV and improved storage stability in emulsion preparation. This study demonstrated the potential of US in modifying SRWP, offering a promising approach for valorizing fish processing by-products and expanding their application scope in food and other industries.

Ochratoxin A (OTA), a secondary metabolic product, is one of the most vicious mycotoxins produced by a large species of filamentous fungi that are often associated with different grains and have tolerance against unfavorable conditions. It has a fatal effect on the health of mammals through its direct impact on the efficiency and functions of several body organs. In this study, 153 random assorted samples of barley, yellow corn, soybean, and wheat grains were collected from four different regions of Saudi Arabia. The highest OTA contamination in barley, yellow corn, soybean and wheat grains was 53.0, 210.6, 146.6, and 51.6 µg kg^− 1, respectively. While, the highest average of OTA was recorded in yellow corn grains as 156.67 µg kg^− 1 and the lowest as 13.3 µg kg^− 1 in barley. Aspergillus ochraceus and A. carbonarius were identified as ochratoxigenic species by BIOLOG technique. The highest relative density (RD %) was recorded for A. ochraceus in yellow corn grains as 8.33% and decreased in barley, soybean and wheat as 5.56, 6.90, and 7.69%, respectively. Clinoptilolite binder reduced OTA concentration in all tested isolates but to varying rates, under recommended dose. The highest reduction was 26.6% with A. ochraceus, while reached 30% with A. carbonarius after 21 days. Mice were fed on yellow corn grains contaminated artificially by mild ochratoxigenic isolate (RIY-C1) resulted in increase of OTA to 5.65, 17.84 and 19.25 µg kg^− 1 after 15, 30, and 45 days, respectively. In conjunction with OTA increasing in mice-feed, the activity of liver and kidney function as Alanine Aminotransferase, Aspartate Aminotransferase, creatinine and uric acid increased in tested periods.

In this study, a self-developed K-band and KA-band microwave detection device was used to detect aflatoxin B1 (AFB1) in wheat with high accuracy. The microwave transmission spectra of 108 wheat samples with different mould levels were acquired, smoothed and preprocessed using Savitzky-Golay and moving average filters, and feature selection was performed using embedded algorithms such as Competitive Adaptive Re-weighted Sampling (CARS) and Multi-Feature Elimination using LASSO (MFE-LASSO). Based on the optimised feature wavelengths, Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVR) models were constructed and the feature wavelengths were quadratically optimised by the Spotted Kingfisher Optimisation (PKO)、Hiking Optimisation Algorithm (HOA). The results showed that the CARS_PKO_PLSR model performed best on the prediction set with an RMSE of 3.4202 and an R² of 0.9843, which demonstrated that microwave detection based on the K-band and the KA-band was able to accurately predict the AFB1 content of wheat and demonstrated the potential of millimetre and centimetre waves in food safety detection.

Synbiotic foods have garnered significant attention due to their functional, nutritional, and therapeutic benefits. This study explores the traditional Iranian beverage, Doogh, known for its health-promoting properties, by assessing the effects of microencapsulation on the viability and functional properties of Lactobacillus acidophilus (L. acidophilus) and Lactobacillus plantarum (L. plantarum) during its production. The microencapsulation utilized calcium alginate coated with chitosan (Alg-CH) and beta-cyclodextrin (β.CD), combined with the prebiotic beta-glucan (β.GL) at concentrations of 0.5, 0.75, and 1%. Samples were stored at 4 °C and analyzed at five time intervals (1, 7, 14, 21, and 28 days), with each interval comprising three replicates. Results indicated that incorporating both free and microencapsulated probiotics reduced sodium chloride levels while maintaining consistent fat content. Over time, a decrease in pH and an increase in acidity were recorded, particularly in samples containing L. plantarum encapsulated with β.CD and β.GL at 1%. The highest viscosity levels were also observed in these samples. Mold and yeast growth were absent in all samples except the control. Probiotic stability declined over time, but the reduction was less significant in samples with encapsulated bacteria. The highest probiotic viability was observed in Doogh containing L. plantarum encapsulated with β.CD and β.GL at 1%. This method significantly enhanced probiotic viability, with L. plantarum showing higher viability rates than L. acidophilus. The findings highlight how integrating β-glucan and encapsulated probiotics can enhance Doogh’s nutritional and functional profile, making it an even more effective synbiotic beverage.

Due to their high perishability, mangoes are susceptible to internal browning during ripening as a result of incorrect handling, storage, and environmental factors. This browning significantly affects post-harvest quality, reducing both marketability and consumer satisfaction. In this research, Near-Infrared Spectroscopy and 3D Deep Neural Networks for Mango Internal Browning Detection (3DSDNN-IBM-NIS) are proposed. The process begins with collecting input images from commercial orchards in Petrolina, Brazil, followed by a pre-processing phase using Broad Collaborative Filtering (BCF) for resizing, noise reduction, and normalization. The pre-processed image is then fed into 3D Shallow Deep Neural Network (3DSDNN) to detect internal browning in mangoes, classifying them as healthy or brown. The Modified Adaptive Ant Colony Optimization Algorithm (MAACOA) is used to optimize the 3DSDNN in order to improve classification accuracy. The proposed 3DSDNN-IBM-NIS method is excluded in Python and evaluated using performance metrics like Accuracy, Recall, Precision, Sample Frequency, and RMSEC (Root Mean Square Error Calibration). Comparing the proposed 3DSDNN-IBM-NIS approach to the current one, it covers 16.42%, 23.36%, and 19.27% greater accuracy, and 16.26%, 34.41%, and 23.26% higher precision, compared with existing methods, Automatic Estimation of Polyphenol Oxidase and Peroxidase Activity in Bell Peppers Using Vis/NIR Spectroscopy (NDE-NIS-SVM), the classification of mango disorder (CMD-RCNN), internal browning in mangoes utilizing visible as well as near-infrared spectroscopy (IBM-NIS-ANN). The 3DSDNN-IBM-NIS method outperforms existing techniques, providing a robust and efficient solution for detecting mango internal browning in post-harvest management.

Bulgur is a parboiled, dried, cracked, and semi-ready-to-eat wheat product, whose quality is influenced by wheat cultivars. The objective of the study was to evaluate the bulgur-making potential of Indian wheats. Technique for order preference by similarity to ideal solution (TOPSIS) which is a multi-criteria decision-making (MCDM) technique, in combination with AHP and entropy weighting methods, was utilised for selection of wheat variety from nineteen alternatives, on the basis of five quality criteria (yield, colour, cooking time, cooking loss and hardness). Overall, bulgur made from durum varieties demonstrated higher yield, yellowness, and lower cooking loss, while aestivum varieties had lower cooking time and hardness. The variety HI 8663 was found to be the most suitable for making bulgur, while variety UP 2338 was found to be the least suitable. The three superior varieties suggested by both weighting methods for processing wheat into bulgur belonged to Triticum durum, namely HI 8663, WH 896, and PDW 233.

Graphical Abstract