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Facile synthesis of thermal responsive fluorescent poly(imino ether sulfone): Nondestructive detection of Tg and erasable thermal imaging
摘要： A novel heat-resistant fluorescent polymer poly(imino ether sulfone) (PIES) as thermally erasable and writable imaging material has been synthesized via a facile nucleophilic substitution polycondensation reaction. Taking advantage of the tenability of the 'push-pull' π-electron mode by changing temperatures, the Tg of PIES film can be 'naked eye' nondestructively detected by taking advantage of the visual fluorescence quenching.
关键词： Nondestructive detection,Poly(imino ether sulfone),High performance polymer,Glass transition temperature
更新于2025-09-23 15:23:52
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Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics
摘要： Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push-broom” system to acquire hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels.
关键词： moisture content,nondestructive detection,peanut kernels,chemometrics,hyperspectral imaging technology
更新于2025-09-19 17:13:59
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Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
摘要： Mid-infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c-means (PFCM) clustering with a fuzzy covariance matrix. The mid-infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR-7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid-infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c-means (FCM) clustering, the allied fuzzy c-means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS-DA) algorithm (33.3%). It is sufficiently demonstrated that the mid-infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties.
关键词： possibilistic fuzzy c-means clustering,tea varieties,Mid-infrared diffuse reflectance spectroscopy,fuzzy covariance matrix,nondestructive detection
更新于2025-09-11 14:15:04