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Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify Porphyra haitnensis into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency. [ABSTRACT FROM AUTHOR]

Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real‐time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone‐based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre‐training and fine‐tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin‐Tiny (Swin‐T) achieved the highest trade‐off performance, with a Top‐1 accuracy of 0.997, a detection speed of 3.2 ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin‐T model was also capable of distinguishing between different types of hydrocolloids with a Top‐1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real‐time quantitative information about meat adulteration anytime, anywhere. Practical Application: This research provides a practical solution for regulators and consumers to non‐destructively and quantitatively detect the content and type of hydrocolloids in beef in real‐time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments. [ABSTRACT FROM AUTHOR]

Summary: The fraudulent practice of beef adulteration is a growing concern, as it violates consumer rights. Electrical impedance spectroscopy (EIS) combined with machine learning has emerged as a widely used approach to identify low‐quality meat. Unlike traditional biochemical methods that require expensive instruments, complex sample preparation, and chemical reagents, EIS is a cost‐effective alternative. However, EIS data are susceptible to temperature fluctuations, requiring a waiting period under consistent temperature conditions for data stabilisation before measurements. This process becomes impractical when dealing with a large number of samples. To overcome this limitation, standardisation, normalisation, and smoothing methods were introduced in the meat quality detection based on EIS data. A recognition model for detecting carrageenan adulteration in beef was established. Under an inconsistent temperature condition, by applying the spectra pre‐processing methods to the prediction dataset, the model accuracy reached 84%, whereas the accuracy of the unprocessed prediction dataset dropped to 54%. This study demonstrates that acquiring EIS data under consistent temperature conditions is unnecessary if proper spectra pre‐processing methods are applied. By eliminating the waiting time for data stabilisation, this practical approach enhances the efficiency and accuracy of meat quality detection. [ABSTRACT FROM AUTHOR]

Mathematical models based on infrared spectroscopy and machine learning have been successfully used to trace the origin of soybeans. However, as previous research reported, it is necessary to employ spectra data that undergo multiple pre-processing operations in order to achieve optimal accuracy during model training. And these established models are only capable of predicting samples with identical spectra pre-processing. Specifically, baseline correction, which necessitates individual processing of each spectrum, requiring substantial investments of time and human resources with a large dataset. In this study, the spectra augmentation technique was proposed based on the theory of data augmentation, in order to simplify or even eliminate the pre-processing steps for the prediction dataset. The technique utilized a combination of the standard spectra pre-processed data and the "boost data" to train models, specifically, a total of 180 spectra, including 90 pre-processed standard spectra and 90 "boost" spectra. The "boost" data refers to data without the standard spectra pre-processing. On the prediction dataset without the standard spectra pre-processing, the model with the spectra augmentation technique had an accuracy of 0.91 for the recognition of Northeast China soybeans, while the accuracy of the model with the training method frequently reported in previous studies only reached 0.71, demonstrating that the model trained with the proposed technique possessed higher robustness and generalization capabilities. The spectra augmentation technique can maintain high accuracy while simplifying spectra pre-processing steps on prediction data, therefore, providing a more efficient and expedited method for practical food traceability and authentication. [ABSTRACT FROM AUTHOR]

The quality of seaweed is greatly influenced by its harvest period. Hence, the development of efficient and precise analytical techniques for identifying the harvest period is significant from a practical standpoint. In this study, mid-infrared spectroscopy was utilized to perform quantitative analysis of nutritional components, evaluate the quality grades, and determine the harvest period of Porphyra haitanensis. While the outcomes of one-factor analysis of variance and correlation analysis indicated a general decrease in P. haitanensis quality with each subsequent harvest, the nutritional contents of samples obtained from the first and fifth harvest were similar, suggesting that the harvest period alone could not fully represent the quality of seaweed. To create discriminant models of the harvest period, decision tree, K-nearest neighbor, partial least squares-discriminant analysis, and adaptive boosting algorithms were adopted based on band integral areas and a small sample size (30 per harvest period, 6 periods in total). The adaptive boosting algorithm provided the most comprehensive results with regards to forecast accuracy (96.7%), macro precision (0.971), macro recall (0.965) and RMSE (0.183). The findings demonstrated that band integral areas of the mid-infrared spectroscopy not only provided quantitative information on the nutrients but also offered effective prediction variables for classification algorithms. Thus, the proposed method can serve as an essential instrument to evaluate the seaweed quality swiftly and precisely. [ABSTRACT FROM AUTHOR]

Summary: Unscrupulous merchants would sell cheap, low‐nutrition formula milk powder as pasteurised milk, or use it as a raw material in dairy products, for the purpose of making a profit. Currently, biochemical methods are utilised to identify the type of processed milk, which could involve chemical reagents, sample preparation and costly instruments. This paper investigates the utility of electrical impedance spectroscopy (EIS) in distinguishing different types of processed milk. Ultra‐high temperature sterilised milk, pasteurised milk and formula milk powder were studied. The one‐factor analysis of variance revealed that milk type had a significant influence on electrical properties, whereas sensory analysis, pH and total soluble solids showed poor discrimination of milk type. Recognition models were developed using Support Vector Classification, Partial Least Squares‐Discriminant Analysis (PLS‐DA), Gaussian Naive Bayes and Random Forest (RF). The nested cross‐validation and the external validation set (N = 45) indicated the RF model had the highest predicting accuracy of 97.8%; whereas PLS‐DA had a misclassification rate of 33.3% on positive samples and was, therefore, unsuitable for the authentication. The results demonstrated that the EIS data was featured enough for a small sample modelling (N = 75), and the established method was simple, fast and effective for liquid food classification. [ABSTRACT FROM AUTHOR]

Based on the features of cracks, this research proposes the concept of a crack key point as a method for crack characterization and establishes a model of image crack detection based on the reference anchor points method, named KP-CraNet. Based on ResNet, the last three feature layers are repurposed for the specific task of crack key point feature extraction, named a feature filtration network. The accuracy of the model recognition is controllable and can meet both the pixel-level requirements and the efficiency needs of engineering. In order to verify the rationality and applicability of the image crack detection model in this study, we propose a distribution map of distance. The results for factors of a classical evaluation such as accuracy, recall rate, F1 score, and the distribution map of distance show that the method established in this research can improve crack detection quality and has a strong generalization ability. Our model provides a new method of crack detection based on computer vision technology. [ABSTRACT FROM AUTHOR]

Understanding the water state in Nafion is not only crucial for operating a proton-exchange membrane (PEM)-based fuel cell, but also intimately related to the elucidation of the proton transport mechanism in a PEM. Although many studies have been published on this subject, some controversies and ambiguities remain unresolved. In this work, we design three different types of Nafion samples by substituting protons with lithium or sodium cations. We also pay special attention to the preparation of samples for carrying out broad-range variable temperature solid state NMR experiments so that no membrane dehydration occurs during the long experimental time at low temperatures. With these precautions and improvements, clear and largely straightforward information could be obtained to ensure minimal ambiguity and complexity in the interpretation of the experimental data. Our results show that about 40–60% of water remains unfrozen at −70 °C, depending on the type of the substituting cation. Both the 1H and 2H spectral and relaxation results indicate that water freezing starts from the center of the nanopores inside Nafion and increases gradually as the temperature decreases. The protons remain dissociated with sulfonate groups even at the lowest temperature we reached (−70 °C), whereas both lithium and sodium are associated with sulfonate groups at most temperatures below 0 °C. The experimental data also suggest that besides frozen and unfrozen water, there is broad distribution of water state and dynamics in Nafion as the temperature is lowered from above zero down to −70 °C. The effect of the size of the substituting cation significantly affects the properties of supercooled water by modifying the cation–water interaction and impeding the rotation of sulfonate groups. These novel results not only help us in establishing a better understanding of the water state in Nafion and its performance as a proton exchange mebrane, but also provide insights into water freezing, antifreeze and supercooling in other nanoscopic environments. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) spectroscopy, also known as magnetic resonance spectroscopy, is a preeminent and noninvasive analytical technique that provides detailed information about the structure, dynamics, reaction state, and chemical environment of molecules. The development of NMR spectroscopy has led to the awarding of many Nobel Prizes, and today NMR spectroscopy serves as an important and irreplaceable tool in physics and chemistry. Two-dimensional (2D) NMR is effective at separating resonances which have similar chemical shifts, although the interpretation of 2D spectra can be challenging. A systematic density operator-based derivation will aid the understanding of the quantitative mechanism of 2D NMR spectroscopy and the interpreting of outcomes of 2D NMR experiments. Therefore, in this study, we systematically analyzed and compared the quantitative basis of 2D and 1D NMR. Meanwhile, as a proof of principle, simulations using the FID Appliance software toolkit were performed and interpreted using a brain phantom, a popular model for studying brain metabolites. The scheme shown in this paper will facilitate the understanding of quantitative 2D NMR spectroscopic analyses in chemistry and biology. [ABSTRACT FROM AUTHOR]

High‐resolution nuclear magnetic resonance (NMR) spectroscopy is an indispensable technique for obtaining chemical structure information. Its quantitative and noninvasive properties have led to its growing popularity as an analytical tool in many fields, including biology, chemistry, medicine, and food science. During transportation and storage, chemical reactions among the many nutrients lead to a loss of food quality. In these circumstances, portable NMR spectrometers can readily be used for food inspection and quality control. Because of the heterogeneous tissue distribution in food, a high‐resolution NMR method is required for detailed food inspection. Therefore, in this study, we demonstrated the feasibility of using an intermolecular double‐quantum coherence signal to obtain high‐resolution metabolic profiles of several fruits, including grape, cantaloupe, tomato, and watermelon. The resulting high‐resolution NMR spectra facilitate the identification of important metabolites, which can be used as biomarkers for food quality control. The method established here may be adapted for food inspection using portable NMR equipment. [ABSTRACT FROM AUTHOR]

Abstract: Contrast enhancement agents are often employed in magnetic resonance imaging (MRI) for clinical diagnosis and biomedical research. However, the current theory on MRI contrast generation does not consider the ubiquitous presence of macromolecular crowders in biological systems, which poses the risk of inaccurate data interpretation and misdiagnosis. To address this issue, herein the macromolecular crowding effects on MRI contrast agent are investigated with the 1H relaxation rate of water in aqueous solutions of Dotarem with different concentrations of macromolecules. Two representative macromolecular crowder systems are used: polyethylene glycol (with no specific secondary structure) and bovine serum albumin (with compact secondary and tertiary structures). The water 1H relaxation rates in various solutions are measured in a fixed magnetic field and in variable magnetic fields. The results show significant crowding effects for both crowders. The relaxation rate is proportional to the concentration of the MRI contrast agent but shows conspicuous superlinearity with respect to the concentration of the crowder. The size of polyethylene glycol does not affect the relaxivity of water in Dotarem solutions. The above effects are verified with T1‐ and T2‐weighted NMR microimages. These results highlight the importance of the effect of macromolecular crowding on the MRI contrast agent and are valuable for understanding the mechanism of MRI contrast agents and designing new‐generation MRI contrast agents. [ABSTRACT FROM AUTHOR]

Intermolecular double quantum coherence (iDQC) 1H-nuclear magnetic resonance spectroscopy (1H NMR), which serves as a complementary method in the analyses of fatty acids, is used to investigate the intact salmon muscle and the whole zebra fish. The spectra of fatty acids of the intact salmon muscle, whose resonances are overlapped with metabolites peaks in the conventional NMR spectra, can be resolved in the presence of severe intrinsic structural inhomogeneity without sample pretreatments and special NMR accessories, such as those for high-speed sample spinning. For improving the practicability of the iDQC method, a localized module is combined with the iDQC method so that the fatty acids of the whole zebra fish can be detected non-invasively. All the iDQC results are verified by the extraction NMR spectra. In addition, fatty acid composition of the salmon muscle is quantitatively analyzed based on the iDQC and extraction NMR spectra. The calculated results from these two methods are in good agreement. Therefore, the iDQC method may serve as a feasible one-step and fast screening method for fish quality analyses and lipid inspections of other biological tissues. Practical applications: The intermolecular doublequantum coherence (iDQC) can be an appropriate lipid detecting method of tissues when the samples are not suitable for chemical extraction. The iDQC with a localized module may be more practical than magic angle spinning for applications in in vivo and in situ NMR experiments, in which samples are not allowed to spin. Fatty acids of the whole zebra fish can be identified by iDQC method when PRESS with water suppression fails to produce high-resolution spectrum. [ABSTRACT FROM AUTHOR]

Background and Purpose: Nuclear magnetic resonance (NMR) spectroscopy has become an important technique for tissue studies. Since tissues are in semisolid-state, their high-resolution (HR) spectra cannot be obtained by conventional NMR spectroscopy. Because of this restriction, extraction and high-resolution magic angle spinning (HR MAS) are widely applied for HR NMR spectra of tissues. However, both of the methods are subject to limitations. In this study, the feasibility of HR 1H NMR spectroscopy based on intermolecular multiple-quantum coherence (iMQC) technique is explored using fish muscle, fish eggs, and a whole fish as examples. Materials and Methods: Intact salmon muscle tissues, intact eggs from shishamo smelt and a whole fish (Siamese algae eater) are studied by using conventional 1D one-pulse sequence, Hadamard-encoded iMQC sequence, and HR MAS. Results: When we use the conventional 1D one-pulse sequence, hardly any useful spectral information can be obtained due to the severe field inhomogeneity. By contrast, HR NMR spectra can be obtained in a short period of time by using the Hadamard-encoded iMQC method without shimming. Most signals from fatty acids and small metabolites can be observed. Compared to HR MAS, the iMQC method is non-invasive, but the resolution and the sensitivity of resulting spectra are not as high as those of HR MAS spectra. Conclusion: Due to the immunity to field inhomogeneity, the iMQC technique can be a proper supplement to HR MAS, and it provides an alternative for the investigation in cases with field distortions and with samples unsuitable for spinning. The acquisition time of the proposed method is greatly reduced by introduction of the Hadamard-encoded technique, in comparison with that of conventional iMQC method. [ABSTRACT FROM AUTHOR]

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper. [ABSTRACT FROM AUTHOR]

The classification of Wuyi rock tea (Oolong type) is performed by solid-state nuclear magnetic resonance ( NMR) for the first time. Quality differences of eight Oolong teas grown in Wuyi Mountains in southeastern China are analyzed by a combination of solid-state 1H and 1H- 13C CP-MAS (cross-polarization magic angle spinning) NMR, partial least-squares discriminate analysis ( PLS-DA), and quantitative descriptive analysis ( QDA). The contents of caffeine, carbohydrate, polyphenol, and terpenoid were distinguished, and quantification of the metabolites was made based on the 1H and 13C MAS spectra. PLS-DA shows a separation of high- and low-quality Oolong teas, in good agreement with the result of QDA. These results indicate that NMR metabolomic approach enables us to differentiate the analyzed teas based on their chemical composition. [ABSTRACT FROM AUTHOR]