This study investigated the feasibility of using hyperspectral imaging (HSI) technology to detect Panax notoginseng powder grades. The hyperspectral images of 240 Panax notoginseng powder samples were collected in the spectral range of 450–1,000 nm. Savitzky–golay (SG) and multiplicative scatter correction (MSC) were used to preprocess the original spectra. A method of combing competitive adaptive reweighted sampling (CARS) and principal component analysis (PCA) was used to analyze the spectral data and eliminate the influence caused by the randomness of Monte Carlo (MC) sampling. The least‐squares support vector machine (LSSVM) modeling results showed that CARS‐PCA had better spectral information extraction performance than PCA and CARS, and two principal components were extracted for modeling. The average classification accuracy of PCA‐LSSVM, CARS‐LSSVM, and PCA‐CARS‐LSSVM was 88.33, 90.93, and 92.5%, respectively. To further improve the modeling accuracy, a marine predators algorithm least squares support vector machine (MPA‐LSSVM) model was proposed to identify the grades of Panax notoginseng powder. The result indicated that MPA‐LSSVM had higher modeling accuracy and stronger robustness than the other compared models, and the classification accuracy of the training set and test set was 96.67 and 95%, respectively. The results illustrated the potential of HSI technology as an effective tool in Panax notoginseng powder grades detection. Practical Application This study verified the feasibility of using HSI technology to detect the grades of Panax notoginseng powder. Two principal components were extracted, which were used for simplifying the LSSVM model based on full wavelengths. The simplified model achieved high classification accuracy (95%), which provides a basis for the design of a multispectral imaging system.
To identify the varieties of red jujube rapidly and nondestructively, hyperspectral imaging (HSI) technology was applied in this article. Hyperspectral data of 480 samples with four different varieties were acquired in the range of 400.68-1001.61 nm.First, Savitzky-Golay and standard normal variable were utilized to process raw spectra. Afterward, a novel method combining competitive adaptive reweighted sampling and iterative retained information variable (CARS-IRIV) was proposed to select feature wavelengths. The support vector machine (SVM) modeling results indicated that CARS-IRIV had better information extraction performance and simplified the model.Finally, to further improve the accuracy, sparrow search algorithm (SSA) was adopted to optimize the parameters (c, g) of SVM. The results showed that SSA-SVM exhibited greater accuracy than other compared models, and the accuracy of training and test sets were 100% and 96.68%, respectively. It confirmed that HSI technology coupled with CARS-IRIV-SSA-SVM can effectively identify varieties of red jujube. Practical ApplicationsThe traditional ways of classifying red jujube varieties are destructive and laborious. Therefore, HSI technology was adopted to overcome the above shortcomings. In this article, the best identification performance was based on the CARS-IRIV-SSA-SVM model with an identification accuracy of 96.68%. This study is helpful for the identification of other agricultural product varieties by HSI technology.
Background: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves.Results: Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg −1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. Conclusion:The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive.
In order to the quickly and nondestructively detect whether starch is adulterated in minced chicken meat, a novel method combining hyperspectral imaging (HSI) technique with transfer learning was proposed in this study. First of all, hyperspectral images of minced chicken meat with different mass fractions of starch were collected and spectral information of the samples in the range of 400.89-1000.19 nm was extracted. Then, the hyperspectral data was preprocessed via continuous wavelet transform (CWT), which transformed the pixel-level hyperspectral data into twodimensional spectrograms. Furthermore, classification model for identifying starch in minced chicken meat was constructed based on the GoogLeNet network pretrained on ImageNet data set. Finally, the support vector machine (SVM) model and the convolutional neural network (CNN) model without transfer learning were established for comparison. The results indicated that the model based on GoogLeNet network had a higher classification accuracy, up to 98.6%. Therefore, this study demonstrates the feasibility of the detection of starch in minced chicken meat based on HSI technique and transfer learning.Practical applications: The identification of starch in the minced chicken meat is of particular significance for maintaining the market order and safeguarding the human health. An innovative method based on hyperspectral imaging technique and transfer learning to identify whether the minced chicken meat mixed with starch was proposed in this study. The method achieved quickly, nondestructively and accurately detection of starch in minced chicken meat.
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