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Food quality and safety oversight now urgently require the identification of similar food samples. Using infant rice cereals as an example, this study collected the Raman spectra of samples from various brands and used a statistical control chart method to reveal that samples from the same brand have controllable quality fluctuations. Additionally, samples from different brands also fall within the control limit, indicating a high degree of similarity between the samples. Under conditions of raw data, the extreme learning machine algorithm and Raman spectroscopy for sample identification demonstrated a recognition rate of 77.6%, suggesting that the machine learning algorithm has some identification effect. After optimizing the conditions, it was found that the recognition accuracy was significantly improved, reaching 99.9%, based on coif3 wavelet denoising and 0~0.1 normalization processing. During the experiment, it took 100 seconds to collect the Raman spectrum signal of a single sample and running the intelligent algorithm only took less than one second to obtain the calculation results. The optimization and identification methods proposed in this work have the advantages of efficiency and accuracy, which can provide a reference for the identification of similar samples. [ABSTRACT FROM AUTHOR]

Rapid and intelligent identification of similar samples is an important technical aid for food quality and safety assessment. At present, relevant research studies are still relatively scarce. In this paper, Raman spectra of different brands of cheese products with different integration times were collected and analyzed statistically. The research showed that the original Raman spectral data of different brands of cheese products had a high similarity. The conventional statistical control chart method revealed that the samples of the same brand showed certain quality fluctuations, but it was still difficult to achieve effective differentiation from other brand samples. Different spectral preprocessing methods had a greater impact on the classification accuracy of the intelligent extreme learning machine algorithm, and different spectral integration times also had a greater impact. Before optimization, the intelligent recognition algorithm had only about 40% average accuracy with the integration time of 10 s. Under the optimal conditions – an 80 s Raman spectral integration time, wavelet denoising (coif wavelet basis, 5 decomposition layers), normalization processing ([−1, 1] interval), principal component dimensionality reduction (74 principal components were extracted, which can represent 100% of the information of the original data) and 400 hidden-layer neurons – the average recognition accuracy was 98%, and the algorithm operation time was less than 5 s. The establishment of this method provides a technical reference for rapid discrimination in food quality control. [ABSTRACT FROM AUTHOR]

[Display omitted] • Flexible functional nanomaterials hold advantages in electrochemical sensing field. • Recent progress of flexible electrochemical sensing materials are reviewed. • Different applications of flexible electrochemical sensor are discussed. • Perspectives and outlook for flexible electrochemical sensing are highlighted. With the widespread application of flexible electrochemical sensors in environmental monitoring, medical diagnosis, food quality and safety analysis, and the other related fields, many functional nanomaterials have been developed for the construction of flexible electrodes. Here, we review the recent advances in the construction of flexible electrochemical sensors, with special attention to the functional nanomaterials. Firstly, the working principle of such electrochemical sensors with functional materials are introduced. The functional materials are mainly including metal nanomaterials, carbon-based materials, metal organic framework-based materials, single-atom materials, conductive polymers, MXene-based materials, and others. These materials are employed in the construction of flexible electrochemical sensors based on their excellent electrochemical activity, large specific surface area, and good biocompatibility. Then, the review explores the different application of flexible electrochemical sensors in fields of sports monitoring, environmental monitoring, medical diagnosis, and food quality and safety. Finally, the challenges and future directions of flexible electrochemical sensing are further discussed. [ABSTRACT FROM AUTHOR]

Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification. [ABSTRACT FROM AUTHOR]

The effect of integration time on Raman spectral data collected from fresh milk products was investigated and analyzed. The collected spectral data were denoised by wavelet transform to remove signal interference. Two-dimensional correlation Raman spectra of the fresh milk products were then constructed using two-dimensional correlation analysis and laser integrating time as an external perturbation. Finally, feature extraction was carried out, and the Euclidean distance was calculated. Thus, the similarities between fresh milk products of the same brand and different brands were quantitatively analyzed and calculated from Raman spectroscopy, two-dimensional correlation Raman spectroscopy, and feature extraction. The results showed that the quality of fresh milk products of the same brand fluctuated but maintained a high consistency. The differences between different brands of fresh milk products under the same detection conditions were much greater than within the same brand samples. [ABSTRACT FROM AUTHOR]

The dynamic Raman spectra of a brand of red wine (aa) as the main research object were collected over a range of laser integration times (1–5 s) to observe the changing trends of molecules in the wine under experimental conditions. The three-dimensional Raman characteristic spectrum of this wine was then constructed further by two-dimensional correlation fusion analysis. The fluctuations of the three-dimensional Raman spectra were also evaluated using a similarity algorithm. The correlation coefficients were 0.977 ± 0.011 and 0.990 ± 0.006 based on synchronous and asynchronous two-dimensional correlation Raman spectroscopy, respectively. These results suggested that the samples of wine aa were highly self-similar and could be effectively distinguished from two different brands of red wine (bb and cc) based on their different spectral responses. Therefore, this method has the potential to supplement existing methods for the classification analysis of red wine. [ABSTRACT FROM AUTHOR]

As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear. [ABSTRACT FROM AUTHOR]

In this paper, laser perturbation two-dimensional correlation Raman spectroscopy was explored as a rapid and reliable determination method for the assessment of bovine colostrum products and their analogue, namely, milk powder. Compared with the traditional instrumental analytical methods, such as chromatography, Raman spectroscopy measurements can be realized easily without the need of sample preparation. Two-dimensional correlation spectroscopy can enhance the spectral resolution and provide more useful information of samples. Laser perturbation technique is a new way, which has been developed in this work; additionally, only few minutes (<5 min) are in demand of the whole experiment process for one sample. Correlation coefficient method was employed to quantitatively analyze the similarities between bovine colostrum products and the other brands of milk powder that include bovine colostrum products containing some milk powder based on the developed two-dimensional correlation Raman spectroscopy. The results showed that the correlation coefficients between each bovine colostrum product with their mean value could reach 0.991 ± 0.003, which indicated that there were high similarities between these products. Under the same experimental conditions, the correlation coefficients between the bovine colostrum product (mean) and the other brands of milk powder that include bovine colostrum products containing some milk powder were located in the range of 0.596-0.761, far below the 0.991 ± 0.003. Hence, these results clearly demonstrated the utility of the laser perturbation two-dimensional correlation Raman spectroscopy combined with correlation coefficient as a rapid method for quality control of bovine colostrum products. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The rapid quantitative analysis of food systems may offer improved control of food quality. In this work, the quantitative analysis of Chinese liquor was investigated using ultraviolet (UV) spectroscopy and a similarity algorithm. The potential application of this approach was evaluated using Chinese liquors, including the same and different batches of Gu-Jing-Gong spirits. The effectiveness of the proposed quality control quantitative model has been demonstrated, and the method could be applied to control the quality of Gu-Jing-Gong spirits and to identify counterfeit products. This analytical method is simple, rapid and efficient, and has potential practical application. Copyright © 2017 The Institute of Brewing & Distilling [ABSTRACT FROM AUTHOR]

A simple integrated agarose microchip-based substrate is reported for surface enhanced Raman spectroscopy. The substrate was evaluated for the determination of sodium thiocyanate in milk. The limit of detection was 5 × 10−7grams per milliliter. The microarray design allowed the synchronous processing of many samples. The reported procedure is green and practical for on-site determination of sodium thiocyanate in milk with potential for additional applications. [ABSTRACT FROM AUTHOR]

Accumulating evidence showed abnormalities in brain network connectivity in depressive individuals with suicidal ideation (SI). We aimed to investigate the large-scale brain network dynamics in adolescents with SI and major depressive disorder (MDD). We recruited 47 first-episode drug-naïve adolescents with MDD and SI, 26 depressed adolescents without SI (noSI), and 26 age-matched healthy controls (HC). The Columbia Suicidal Ideation Severity Scale (C-SSRS) was utilized to assess suicide ideation. We acquired 64-channel resting-state EEG recordings from all subjects and used microstate analysis to investigate the large-scale brain network dynamics. We observed a significant reduction in the occurrence and coverage of microstate B within the SI group when contrasted with the noSI group. Conversely, there was a significant increase in the occurrence and coverage of microstate A in the SI group as compared to the HC group. Additionally, we observed heightened transition probabilities from microstates D and C to microstate A in the SI group; meanwhile, transitions from microstate D to B were more prevalent in the noSI group. Furthermore, the noSI group exhibited a significant decline in the transition probabilities from microstate D to microstate C. The cross-sectional nature limits the capacity to determine whether microstate dynamics have prognostic significance for SI. We provided evidence that depressed adolescents with SI have a distinct pattern in microstate dynamics compared to those without SI. These findings suggest that microstate dynamics might serve as a potential neurobiomarker for identifying SI in depressed adolescents. • We investigated the large-scale brain dynamics in depressed adolescents with SI. • Depressed adolescents with SI have a distinct pattern in microstate dynamics. • SI group showed an increase in the occurrence and coverage of microstate A. • SI group showed a reduction in the occurrence and coverage of microstate B. [ABSTRACT FROM AUTHOR]

Raman spectra and ultraviolet-visible absorption spectra of four different geographic origins of Radix Astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet-visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet-visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data. [ABSTRACT FROM AUTHOR]