Comparison of models and visualization of total volatile basic nitrogen content in mutton using hyperspectral imaging and variable selection methods

Qiu, Yuanyuan; Zhu, Rongguang; Fan, Zhongjian; Yao, Xuedong; Lewis, Elfed

doi:10.1080/00387010.2018.1452268

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Based on DFT (at the B3LYP/6-31G* level), 52 the positive frequency, energy gap, and energy of NOR before and after modification were calculated, and used as the stability evaluation index of compound molecules (Table S6†). When the positive frequency is positive, the energy is low, and the energy gap is large, indicating that the structure of the compound is more stable and can exist stably in the environment 68 (detailed data are described in the ESI†).…”

Section: Resultsmentioning

confidence: 99%

Designing and screening of fluoroquinolone substitutes using combined in silico approaches: biological metabolism–bioconcentration bilateral selection and their mechanism analyses

Han

et al. 2022

Green Chem.

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Section: Resultsmentioning

confidence: 99%

Designing and screening of fluoroquinolone substitutes using combined in silico approaches: biological metabolism–bioconcentration bilateral selection and their mechanism analyses

Han

et al. 2022

Green Chem.

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“…It plays a crucial role in machine learning, but their impact on deep learning is unclear. To explore the feasibility of using four algorithms to establish simplified models for bacterial contamination status and species discrimination, the common characteristic wavelength extraction methods genetic algorithm (GA), competitive adaptive reweighting algorithm (CARS) and successive projections algorithm (SPA) (32,33) were used to extract the characteristic wavelengths closely related to the bacterial contamination status and the species of pathogenic bacteria, and the simplified models were established based on the characteristic wavelengths.…”

Section: Characteristic Wavelengths Extractionmentioning

confidence: 99%

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI

Bai,

Du,

Zhu

et al. 2024

Front. Nutr.

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IntroductionRapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers.ObjectivesThe feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored.Materials and methodsThe hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored.Results and discussionThe best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments.ConclusionThe results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

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“…Recently, many late-model techniques, such as computer vision, spectroscopic, electronic nose, and multi-source information fusion, have been widely used to detect meat freshness. Among them, near-infrared spectroscopy (NIRS, 900-2,500 nm) and hyperspectral imaging (HSI, 400-1,000 nm) have been widely used to detect the freshness of chicken (Khulal, Zhao, Hu, & Chen, 2016), mutton (Jiang et al, 2018;Qiu et al, 2018 ), duck (Qiao et al, 2017), pork (Lee, Kim, Lee, & Cho, 2018;Qu et al, 2018), beef (Ma et al, 2012;Crichton et al, 2017), fish (Huang et al, 2014; and other meat products due to their advantages of rapidity, non-destructiveness and no pollution in the process of detecting meat products. In addition, multi-source information fusion technique has attracted great attention of domestic and foreign researchers recently.…”

Section: Introductionmentioning

confidence: 99%

“…It is necessary to analyze the correlation of vacuum packaged chilled mutton freshness indices and storage days, and put forward a comprehensive evaluation criterion. And it can divide mutton freshness into three grades (fresh, sub‐fresh and corrupt), so as to help enterprises and consumers discriminate the freshness of mutton quickly and intuitively (Qiu, Zhu, Fan, Yao, & Lewis, 2018; Zhang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information

Zhu

Bai

Qiu

et al. 2021

J Food Process Engineering

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The discrimination models of mutton freshness grades based on hyperspectral imaging (HSI, 400-1,000 nm), near infrared spectroscopy (NIRS, 900-2,500 nm) and their fusion information were established and compared in this study. Mutton freshness was divided into three grades by the comprehensive evaluation criterion. Then the characteristic variables were screened from preprocessed full-band variables, and discriminant models were established and compared based on the full-band variables, the characteristic variables and fusion information. The results showed that both full-band models of HSI and NIRS showed good performance, and the accuracies of all datasets were higher than 92.6%. The model accuracy decreased gradually with the decrease of the variables number when modeling with characteristic variables. When modeling with fusion information, the extreme learning machine models had better performance, and the accuracies of all datasets were over 92.7%. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor with full-band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low-cost detection devices for mutton freshness grade. Practical Applications Based on the full-band variables, the characteristic variables, and fusion information of HSI (400-1,000 nm) and NIRS (900-2,500 nm), the discrimination models of mutton freshness grades were established and compared. The characteristic variables were screened by the methods of CARS, GA, SPA and their synergic methods. We could come to a conclusion that both full-band models of HSI and NIRS showed good results. Comprehensively considering the accuracy, the cost, the efficiency of data acquisition and analysis, the fusion of HSI and NIRS had no significant effect compared to the single sensor based on the full-band variables, and NIRS was possibly more economical and practical than HSI. This study provides reference and theoretical basis for selection of sensors in future studies and development of simplified, low-cost detection devices for mutton freshness grade.

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Comparison of models and visualization of total volatile basic nitrogen content in mutton using hyperspectral imaging and variable selection methods

Cited by 9 publications

References 36 publications

Designing and screening of fluoroquinolone substitutes using combined in silico approaches: biological metabolism–bioconcentration bilateral selection and their mechanism analyses

Designing and screening of fluoroquinolone substitutes using combined in silico approaches: biological metabolism–bioconcentration bilateral selection and their mechanism analyses

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI

Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information

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