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Pumpkin seeds are nutritious and have some medicinal value. However, the mold and sprouting are produced during the storage of pumpkin seeds. Food safety and quality problems may be caused if they are not removed in time for processing. The traditional testing methods are cumbersome to operate, complex, and destructive in sample preparation. Therefore, terahertz time‐domain spectroscopy (THz‐TDS) technology was proposed to achieve the detection of the internal quality of pumpkin seeds. Firstly, samples of pumpkin seeds of different qualities were crafted, and they were moldy for 3 days, moldy for 6 days, sprouted and moldy, sprouted and normal pumpkin seeds, respectively. Then, the pumpkin seeds of different qualities were dried, ground, and pressed, and their spectral data were collected. The terahertz spectra of the five types of samples were significantly different. The support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) qualitative discriminant models were established with the raw absorbance spectral data, the preprocessed absorbance spectral data, and the preprocessed and band‐screened absorbance spectral data, respectively, where the CNN model based on the raw spectral data has the highest classification accuracy of 96%. The CNN models do not require advance spectral data processing, simplifying the spectral analysis process. And it achieves best classification results in the accuracy of detection compared to traditional chemometric models. The CNN combined with THz‐TDS method has great potential for application in the detection of agricultural products. It provides a new detection method for the field of quality detection of agricultural products. In this paper, a terahertz time‐domain spectroscopy system is utilized to detect pumpkin seeds. Firstly, the collected terahertz spectral data are preprocessed and band filtered. Then, traditional machine learning methods SVM, RF and deep learning method CNN are used to build the model respectively. Among them, the CNN model built based on the original data has the best classification effect, with a classification accuracy of 96%. The CNN model achieves better classification results compared with the traditional chemometrics model. [ABSTRACT FROM AUTHOR]

In order to reduce the low accuracy of the prediction model of soluble solid content caused by the size difference of gongpear, a general size model was proposed. Near-infrared diffuse reflection spectroscopy and partial least squares regression algorithm modeling method of the near-infrared spectrum were adopted, and the theoretical analysis and experimental verification were respectively carried out. Gongpear soluble solids content of data were obtained by a local size prediction model of the small fruit, middle fruit and big fruit size grades and a universal size model with different size grade. The results show that, the local size model is good at predicting the soluble solid content of gongpears with its own grade, but poor at predicting other grades. The correlation coefficient were 0.892, 0.937, 0.889 and root mean square error of small fruit, medium fruit and large fruit predicted by the general model were 0.524, 0.417, 0.551. The general size model could reduce the adverse effects of size difference and was suitable for the determination of soluble solid content of gongpear with different size grades. [ABSTRACT FROM AUTHOR]

Background and objectives Skin defects are one of the primary problems that occur in post-harvest grading and processing of loquats. Skin defects lead to the loquat being easily destroyed during transportation and storage, which causes the risk of other loquats being infected, affecting the selling price. Materials and Methods In this paper, a method combining band radio image with an improved three-phase level set segmentation algorithm (ITPLSSM) is proposed to achieve high accuracy, rapid, and non-destructive detection of skin defects of loquats. Principal component analysis (PCA) was used to find the characteristic wavelength and PC images to distinguish four types of skin defects. The best band ratio image based on characteristic wavelength was determined. Results The band ratio image (Q782/944) based on PC2 image is the best segmented image. Based on pseudo-color image enhancement, morphological processing, and local clustering criteria, the band ratio image (Q782/944) has better contrast between defective and normal areas in loquat. Finally, the ITPLSSM was used to segment the processing band ratio image (Q782/944), with an accuracy of 95.28%. Conclusions The proposed ITPLSSM method is effective in distinguishing four types of skin defects. Meanwhile, it also effectively segments images with intensity inhomogeneities. [ABSTRACT FROM AUTHOR]