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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]

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]

High-resolution nuclear magnetic resonance (NMR) spectroscopy has been considered as a powerful tool for noninvasive, rapid, and accurate composition analyses on solution samples. However, because tissues are in semisolid phase, the high-resolution spectra cannot be obtained by conventional NMR spectroscopy. In the present study, an ultrafast intermolecular single quantum coherence (UF iSQC) NMR spectroscopic method based on spatial encoding is used for direct analyses of intact salmon muscle tissues, intact eggs from shishamo smelt, and a whole zebra fish. Most of free amino acid and fatty acid signals can be directly detected within 5 min in their original state without tedious sample preparation processes. The experimental results show that not only can the UF iSQC method overcome the inhomogeneity of biological tissues, but also it can greatly reduce experimental times owing to the introduction of ultrafast acquisition schemes. Changes occurring in amino acids and fatty acids of salmon muscle tissues, eggs from shishamo smelt, and the whole zebra fish during storage are investigated. Similar variation patterns of metabolic profiles have been deduced for three fish samples, yielding good agreements with literature results. [ABSTRACT FROM AUTHOR]

A modified correlation spectroscopy revamped by asymmetric z -gradients echo detection (CRAZED) sequence was applied to investigate the behavior of intermolecular double-quantum longitudinal relaxation processes in the tilted rotating frame. Theoretical formalism based on dipolar field theory was presented in detail. Spectroscopic measurements and quantitative analysis demonstrated that the signal intensities and intermolecular double-quantum off-resonance longitudinal relaxation time in the rotating frame ( T 1 ρ , DQC eff ) are inversely correlated with the tilt angle ( θ ), while positively correlated with the effective frequency of spin-locking field ( ω e ). Magnetic resonance imaging experiments of an agarose phantom also prove the validity of the theoretical analysis and demonstrated the feasibility of imaging based on T 1 ρ , DQC eff . The rotating-frame double-quantum relaxation measurements are useful for probing slow-motion molecules and this study provides the guidance for optimization of the spin-lock experiments. [ABSTRACT FROM AUTHOR]

A scheme based on Hadamard encoding and intermolecular double-quantum coherences is designed to obtain localized one-dimensional high-resolution NMR spectra in inhomogeneous fields. Brief theoretical derivation was performed to illuminate its principle. Experiments were carried out on phantom solution and biological tissues to verify its effectiveness in yielding useful spectral information and efficiency in suppressing solvent signal even when the field inhomogeneity is sufficiently severe to erase almost all spectral information. This sequence may provide a promising way for analyzing heterogeneous biological tissues and chemical systems. [ABSTRACT FROM AUTHOR]

NMR spectroscopy plays an important role in metabolite studies because it can provide atomic level information critical for understanding biological systems. Nevertheless, NMR investigations on biological tissues are hampered by the magnetic field inhomogeneities originating from variations in macroscopic magnetic susceptibility, which lead to broad spectral lines and subsequently obscure metabolite signals. A new pulse sequence based on intermolecular single-quantum coherences was proposed to obtain one-dimensional high-resolution NMR spectra in inhomogeneous magnetic fields via Hadamard encoding. The new method can provide resolution-improved spectra directly through one-dimensional acquisition within a relatively short acquisition time. Theoretical derivation was performed and the conclusion was tested by solution samples in purposely de-shimmed magnetic fields and pig brain tissue sample. The experimental results show that this sequence can yield useful structural information, even when the field inhomogeneity is sufficiently severe to erase almost all spectral information with conventional one-dimensional single-quantum coherence techniques. Moreover, good solvent suppression efficiency can be achieved by this sequence. This sequence may provide a promising way for high-resolution NMR spectroscopy of biological tissue. [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]

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]

Comparison of current-voltage characteristics of the Se implantation NiGe/ n -Ge contacts with (red) and without (blue) thermal annealing before germanidation. And (b) current-voltage characteristics of NiGe/p-Ge with (red) and without (black) Se implantation. • Debate about the mechanism to modulate the electrical properties. • The influence of impurity segregation on the effective Schottky barrier height. • The influence of implantation damage on the effective Schottky barrier height. • The influence of interface passivation on the effective Schottky barrier height. The mechanism for modulating the effective Schottky barrier height of NiGe/Ge contacts by incorporating impurities is still under debate. There is a view that impurities segregated at the NiGe/Ge interface act as donors or passivators that alleviate the Fermi-level pinning effect so as to significantly affect the electrical characteristics. Our study, however, clearly demonstrates that ion implantation damage is the primary factor in causing the lowering of the effective Schottky barrier, for NiGe/ n -Ge contacts doped with Se or Si, although passivation behavior is observed for P doping. [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]

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]

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]

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]

PSYCHE homonuclear decoupling, a prominent pure shift NMR method, is successfully applied to adulteration discrimination of honey and geographical originality identification of tea. Effects of homonuclear couplings are efficiently suppressed, producing resolution-enhanced spectra. The pair wise honey and tea samples are well separated in OPLS-DA models with high predictability. Due to the well-resolved and accurate assignment of singlet resonances after decoupling, PSYCHE is advantageous in the identification of differential components and accurate quantification of compound concentrations presented by enhanced volcano and Beeswarm plots of honey samples, while the analysis of NOESY is easily interfered by overlapped resonances, which is further proved by the STOCSY analysis, displaying the spectral stability and reproducibility. Experimental results show that PSYCHE can improve the spectral resolution of natural complex products such as honey and tea and be combined with multivariate statistical analysis and serve as a supplementary technique to the standard methods, especially for samples systems composed by a few high-content compounds. Unlabelled Image • Much better resolved pure shift 1H NMR spectra for honey and tea are obtained. • Comprehensive quality analysis models can be built and their reliability and robustness have been verified. • Adulteration discrimination of honey and geographical originality identification of tea have been demonstrated. • PSYCHE is advantageous in componential identification and compound concentration quantification. [ABSTRACT FROM AUTHOR]