Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network

Zhang, Yaoxin; Zheng, Minchong; Zhu, Rongguang; Ma, Rong

doi:10.1016/j.meatsci.2022.108900

Cited by 23 publications

(3 citation statements)

References 28 publications

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“…τ determines the amount of information contained in the reconstructed phase space, smaller values will result in excessive correlation between phase space delay coordinates and the reconstructed phase space trajectory will be concentrated around the main diagonal. Conversely, when τ is large, the interrelated information within the phase space delay coordinates may be completely independent [42]. Based on the observations in figure 5, it is clear that the selection of m = 2,τ = 2 minimizes the structural similarity of the recursive plots associated with different concentrations.…”

Section: Unthresholded Recurrence Plotsmentioning

confidence: 97%

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

Chen,

Dong,

et al. 2024

Meas. Sci. Technol.

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In recent years, there have been frequent outbreaks of harmful algal blooms (HAB) in coastal areas, which have caused serious economic losses to the local community. Therefore, accurate and rapid prediction of microalgal concentrations is necessary for early warning and countermeasures before the occurrence of HAB. This paper presents a model for predicting microalgae concentration based on unthresholded recurrence plots (UTRPs) combined with an improved broad learning system (BLS). Spectral data acquisition of algae species at different concentrations using l-induced fluorescence spectroscopy. Then, the 1D spectral data are dimensionally lifted by UTRPs transformation, recurrence plots (RPs) transformation can fully extract the internal information of 1D sequence data, and at the same time, UTRPs avoids the influence of artificially selected thresholds on the feature transformation results of traditional (RPs). Finally, a lightweight flat network BLS was used for microalgae concentration regression prediction, at the same time the BLS regularization method was improved. When comparing the two most commonly used deep learning regression models and integrated learning models, UTRPs and elastic net (UTRPs-ENBLS) achieves convincing results.

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Section: Unthresholded Recurrence Plotsmentioning

confidence: 97%

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

Chen,

Dong,

et al. 2024

Meas. Sci. Technol.

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show abstract

“…To analyze the classification outcomes in this work, the training set root mean square error (RMSE-Train), test set root mean square error RMSE-Test), and relative analysis error RPD are utilized as assessment metrics ( [45][46][47][48]). The formula for the metrics used is as follows:…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Zhou,

Li,

Wang

et al. 2024

Plants

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Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

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“…Common technical methods used for mutton detection in recent years mainly include image processing, spectroscopic techniques combined with machine learning classification, and regressors. By extracting and analyzing multi-dimensional feature information such as color, texture, contour, protein, water content, and total volatile basic nitrogen (TVB-N) in mutton sample images, and establishing the relationship with mutton freshness [ 6 , 7 , 8 ], tenderness [ 9 , 10 , 11 ], authenticity [ 12 , 13 ], pH [ 14 , 15 ], storage time [ 16 , 17 ], and other indicators, these methods allow effective and nondestructive detection of mutton quality. Although the aforementioned technical methods can achieve high detection accuracy, they also have shortcomings such as cumbersome artificial extraction of sample features, poor generalization of models, and low adaptability, which are not suitable for the classification and detection of mutton with multiple categories, large quantities, and complex natural feature expression.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer

Zhao

Bai

Wang

et al. 2023

Foods

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In order to realize the real-time classification and detection of mutton multi-part, this paper proposes a mutton multi-part classification and detection method based on the Swin-Transformer. First, image augmentation techniques are adopted to increase the sample size of the sheep thoracic vertebrae and scapulae to overcome the problems of long-tailed distribution and non-equilibrium of the dataset. Then, the performances of three structural variants of the Swin-Transformer (Swin-T, Swin-B, and Swin-S) are compared through transfer learning, and the optimal model is obtained. On this basis, the robustness, generalization, and anti-occlusion abilities of the model are tested and analyzed using the significant multiscale features of the lumbar vertebrae and thoracic vertebrae, by simulating different lighting environments and occlusion scenarios, respectively. Furthermore, the model is compared with five methods commonly used in object detection tasks, namely Sparser-CNN, YoloV5, RetinaNet, CenterNet, and HRNet, and its real-time performance is tested under the following pixel resolutions: 576 × 576, 672 × 672, and 768 × 768. The results show that the proposed method achieves a mean average precision (mAP) of 0.943, while the mAP for the robustness, generalization, and anti-occlusion tests are 0.913, 0.857, and 0.845, respectively. Moreover, the model outperforms the five aforementioned methods, with mAP values that are higher by 0.009, 0.027, 0.041, 0.050, and 0.113, respectively. The average processing time of a single image with this model is 0.25 s, which meets the production line requirements. In summary, this study presents an efficient and intelligent mutton multi-part classification and detection method, which can provide technical support for the automatic sorting of mutton as well as for the processing of other livestock meat.

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Adulteration discrimination and analysis of fresh and frozen-thawed minced adulterated mutton using hyperspectral images combined with recurrence plot and convolutional neural network

Cited by 23 publications

References 28 publications

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

The rapid detection method of brown tide algae concentration based on unthresholded recurrence plots and improved broad learning system

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll

Research on Automatic Classification and Detection of Mutton Multi-Parts Based on Swin-Transformer

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