Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon

Tang, Shuqi; Chen, Shenghui; Tian, Xingguo; Zhong, Nan

doi:10.1016/j.foodcont.2022.109573

Cited by 29 publications

(13 citation statements)

References 56 publications

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“…Finally, with the deepening comprehension of the intrinsic mechanisms of CNNs, research combining CNNs and spectroscopy for food quality evaluation has been gaining momentum. These studies indicate that CNNs still hold strong potential even in small sample scenarios and, in many cases, outperform traditional modeling methods (Zhang C. et al, 2020;Zhang et al, 2021;Li et al, 2023), consistent with the findings of this study. Therefore, further development of the combination of CNNs and spectroscopy, leveraging the strengths of CNNs in processing images and high- The regression results for the reference and predicted values of the four isoflavones and starch contents are displayed in (A-E), illustrating the prediction of puerarin, puerarin apioside, daidzin, daidzein, and starch contents using the effective wavelengths and 1DCNN.…”

Section: Discussionsupporting

confidence: 89%

“…One-dimensional convolutional neural networks (1DCNN) are becoming increasingly popular in HSI due to their remarkable ability to accurately predict the concentrations of chemical components in a sample (Mishra and Passos, 2021;Li et al, 2023). Essentially, 1DCNN is a type of neural network that utilizes convolutional layers to extract features from spectral data, which is treated as a one-dimensional sequence of data points, followed by fully connected layers to make precise predictions.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

“…Notably, the 1DCNN model outperformed the other methods, demonstrating superior detection accuracy (Mansuri et al, 2022). Similarly, Li et al (2023) proposed a CNN model utilizing HSI to accurately identify adulteration in Atlantic salmon (Li et al, 2023). Cai et al (2023) leveraged HSI and deep fusion learning approaches to determine the geographical origins of Radix Paeoniae Alba (Cai et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Hu,

Wang,

Wei

et al. 2023

Front. Plant Sci.

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Accurate assessment of isoflavone and starch content in Puerariae Thomsonii Radix (PTR) is crucial for ensuring its quality. However, conventional measurement methods often suffer from time-consuming and labor-intensive procedures. In this study, we propose an innovative and efficient approach that harnesses hyperspectral imaging (HSI) technology and deep learning (DL) to predict the content of isoflavones (puerarin, puerarin apioside, daidzin, daidzein) and starch in PTR. Specifically, we develop a one-dimensional convolutional neural network (1DCNN) model and compare its predictive performance with traditional methods, including partial least squares regression (PLSR), support vector regression (SVR), and CatBoost. To optimize the prediction process, we employ various spectral preprocessing techniques and wavelength selection algorithms. Experimental results unequivocally demonstrate the superior performance of the DL model, achieving exceptional performance with mean coefficient of determination (R2) values surpassing 0.9 for all components. This research underscores the potential of integrating HSI technology with DL methods, thereby establishing the feasibility of HSI as an efficient and non-destructive tool for predicting the content of isoflavones and starch in PTR. Moreover, this methodology holds great promise for enhancing efficiency in quality control within the food industry.

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Section: Discussionsupporting

confidence: 89%

Section: Deep Learning Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Hu,

Wang,

Wei

et al. 2023

Front. Plant Sci.

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“…As the cost of components decreases, it has potential for use in food and agriculture. Currently, this approach is being progressively used for studies on the quick detection and distribution of physicochemical markers in a range of agricultural and food goods, such as fruits, fish, and meat (Cheng, Sun, Yao, et al, 2023; Dai et al, 2023; Li, Tang, et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

Xu,

Huang,

Tian

et al. 2024

J Food Process Engineering

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In order to realize the evaluation of salted duck eggs quality, this study constructed salts‐sensitive colorimetric sensor array (CSA) based on silver nanoprisms (AgNPRs) etching principle and utilized hyperspectral imaging (HSI) system to capture the reflectance spectral of CSA and section of salted duck eggs. To minimize the noise in spectral signals, three preprocessing methods including Savitzky–Golay Smoothing (SG), standard normal variate (SNV) transformation, and multiplicative scatter correction were used. The competitive adaptive reweighted sampling and uninformative variables elimination were used to select the optimal wavelengths, and then partial least squares regression was used to predict moisture, lipid, and salt content (SC). Results demonstrated coefficients of determination of 0.8200, 0.8969, 0.8709, and 0.9239 for protein moisture, yolk moisture, yolk lipid, and protein SC. Visualization distribution maps successfully display the distribution of moisture and lipid content during salting periods. The results indicated that the CSA based on AgNPRs etching principle can be an effective tool in quantitative measurement of salt and HSI combining CSA can be applied to evaluate the quality of salted duck eggs comprehensively. The study offers a rapid detection method for the production and processing of salted duck eggs in the industry.Practical applicationA novel salts‐sensitive CSA was constructed to evaluate quality of salted duck eggs based on AgNPRs etching. This study designed and developed a portable sample collection device using 3D printing technology to acquire the information of samples and CSA at the same time. This study combined with HSI system and colorimetric sensor technology to evaluate the quality of salted duck eggs during the salting process.

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“…When only synthetic data was used, the highest success rate was achieved by kNN, with an accuracy rate of 95.4%. Li and Liu (2023), in their study, aim to detect food fraud to ensure the quality and safety of milk. For this purpose, hyperspectral images of pure and adulterated milk samples were collected using a hyperspectral imaging system (400-1000 nm).…”

Section: Introductionmentioning

confidence: 99%

Innovation in the dairy industry: forecasting cow cheese production with machine learning and deep learning models

Gür

2024

International Journal of Agriculture Environment and Food Sciences

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This study focuses on the use of deep learning and machine learning models to forecast cow cheese production in Turkey. In particular, our research utilizes the LSTM (long short-term memory) model to forecast cow cheese production for the next 12 months by extensively utilizing deep learning and machine learning techniques that have not been applied in this field before. In addition to LSTM, models such as GRU (Gated Recurrent Unit), MLP (Multi-Layer Perceptron), SVR (Support Vector Regression), and KNN (K-Nearest Neighbors) were also tested, and their performances were compared using RMSE (Root Mean Square Error), MSE (Mean Squared Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and (Coefficient of Determination) metrics. The findings revealed that the LSTM model performed significantly better than the other models in terms of RMSE, MSE, MAE, and MAPE values. This result indicates that the LSTM model provides high accuracy and reliability in forecasting cow cheese production. This achievement of the model offers important applications in areas such as supply chain management, inventory optimization, and demand forecasting in the dairy industry.

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Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon

Cited by 29 publications

References 56 publications

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Non-destructive prediction of isoflavone and starch by hyperspectral imaging and deep learning in Puerariae Thomsonii Radix

Application of hyperspectral imaging and colorimetric sensor array coupled with multivariate analysis for quality detection during salted duck eggs processing

Innovation in the dairy industry: forecasting cow cheese production with machine learning and deep learning models

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