Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling

Mansuri, Shekh Mukhtar; Chakraborty, Subhasish; Mahanti, Naveen Kumar; Pandiselvam, R.

doi:10.1016/j.foodcont.2022.109077

Cited by 38 publications

(4 citation statements)

References 42 publications

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“…As a supervised discriminant analysis method used in data classification and regression of metabolic groups, PLS-DA can ignore random errors and make data analysis more concentrated and accurate [ 14 , 15 ]. To further classify rice from different production areas, the PLS-DA model was used in this study to analyze the metabolic information of the four rice samples (DH, HD, SJ, and CL).…”

Section: Resultsmentioning

confidence: 99%

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Tan

Deng

et al. 2022

Foods

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Many factors, such as soil, climate, and water source in the planting area, can affect rice taste and quality. Adulterated rice is common in the market, which seriously damages the production and sales of high-quality rice. Traceability analysis of rice has become one of the important research fields of food safety management. In this study, LC–MS-based non-targeted metabolomics technology was used to trace four rice samples from Heilongjiang and Jiangsu Provinces, namely, Daohuaxiang (DH), Huaidao No. 5 (HD), Songjing (SJ), and Changlixiang (CL). Results showed that the discrimination accuracy of the partial least squares discriminant analysis (PLS-DA) model was as high as 100% with satisfactory prediction ability. A total of 328 differential metabolites were screened, indicating significant differences in rice metabolites from different origins. Pathway enrichment analysis was carried out on the four rice samples based on the KEGG database to determine the three metabolic pathways with the highest enrichment degree. The main biochemical metabolic pathways and signal transduction pathways involved in differential metabolites in rice were obtained. This study provides theoretical support for the geographical origins of rice and elucidates the change mechanism of rice metabolic pathways, which can shed light on improving rice quality control.

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Section: Resultsmentioning

confidence: 99%

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Tan

Deng

et al. 2022

Foods

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“…In recent years, NIR spectroscopy has garnered significant attention from numerous research teams due to its rapid, non-destructive, and environmentally friendly characteristics in the identification of crop varieties and origins [8]. Shekh et al [9] established NIR spectroscopy datasets for various parts of maize and trained them using a one-dimensional convolutional neural network (1D-CNN), partial least squares regression (PLSR), and artificial neural network to differentiate between different maize varieties. Arena et al [10] analyzed the fatty acids in pistachio seeds using near-infrared spectroscopy and successfully distinguished their origins by combining multivariate analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

Xu,

Fu,

Gao

et al. 2024

Agriculture

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The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

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“…Among them, convolutional neural network (CNN) is a typical feed-forward neural network, which can automatically extract deep features from the original data through convolution and pooling structure (10, 21). In the aspect of spectral data processing, there are numerous researches using one-dimensional convolutional neural network (1D-CNN) (22)(23)(24)(25)(26). At present, the application of deep learning in the field of rapid detection of pathogenic bacteria is mainly based on the microscopic scale, and there are few studies on the macroscopic scale.…”

Section: Introductionmentioning

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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Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling

Cited by 38 publications

References 42 publications

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Geographical Origin Differentiation of Rice by LC–MS-Based Non-Targeted Metabolomics

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

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

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