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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]

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]