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 53 publications

(10 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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“…The most frequently used are support vector machines (SVM) [27] (which are noise-tolerant and high-precision, but not suitable for large data and bear a low processing rate) and neural networks. These include artificial neural networks (ANNs) [28], which reliable, convenient and suitable for processing noisy data and solving complex problems but require large number of training samples and processing time; convolutional neural networks (CNNs) [29]; backpropagation neural networks (BPNNs); and random forest (RF) with its related representative, the decision tree. Special indexes, discriminative analysis, extreme learning machine (ELM), joint sparse representation-based classification (JSRC); pixel-wise (PW); and self-organizing maps (SOM).…”

Section: Fundamentals Of Optical Methods' Utilization For Plant Patho...mentioning

confidence: 99%

Optical Methods for the Detection of Plant Pathogens and Diseases (Review)

Gudkov,

Matveeva,

Sarimov

et al. 2023

AgriEngineering

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Plant diseases of an infectious nature are the reason for major economic losses in agriculture throughout the world. The early, rapid and non-invasive detection of diseases and pathogens is critical for effective control. Optical diagnostic methods have a high speed of analysis and non-invasiveness. The review provides a general description of such methods and also discusses in more detail methods based on the scattering and absorption of light in the UV, Vis, IR and terahertz ranges, Raman scattering and LiDAR technologies. The application of optical methods to all parts of plants, to a large number of groups of pathogens, under various data collection conditions is considered. The review reveals the diversity and achievements of modern optical methods in detecting infectious plant diseases, their development trends and their future potential.

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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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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 53 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

Optical Methods for the Detection of Plant Pathogens and Diseases (Review)

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

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