To quickly and accurately identify the quality of tomatoes, a method was proposed to predict the total soluble solid content (SSC), total titratable acidity (TA), and vitamin C (VC) content of tomatoes based on a multiregion combined model of the visible–near‐infrared spectrum. The results show that the competitive adaptive re‐weighted sampling algorithm combined with the partial least squares regression (CARS‐PLSR) model has the best prediction effect on SSC, TA, and VC content in “stem + equator”, “stem + bottom” and “stem + bottom” combinations. The prediction accuracy is 97.2%, 96.7%, and 97.7%, respectively, and the relative percent deviation (RPD) value is 5.870, 5.401, and 5.942, respectively. Practical Application This indicates that the CARS‐PLSR model based on the multiregion combination of visible–near‐infrared spectroscopy is reliable for predicting tomatoes' SSC, TA, and VC content. The results provide a theoretical basis for developing a portable fruit quality detector.

Rapid nondestructive testing of fruit quality is one of the hotspots in food industry research. This paper studied the quality detection of different grades of cream strawberries. Preprocessing methods such as detrending, moving average smoothing (MA), and standard normal variables (SNV) were used to eliminate spectral data errors. The competitive adaptive reweighted sampling algorithm (CARS), successive projection algorithm (SPA), the combination of the above two algorithms (CARS+SPA), and the self‐programming algorithm based on the Laida criterion (collectively referred to as the Laida algorithm) were used to reduce the dimension of data, and a partial least squares regression prediction model was established. The results show that when the Laida algorithm predicted the soluble solid content (SSC), total acid (TA), and vitamin C (VC) content of strawberries, the prediction set correlation coefficients were 0.919, 0.931, and 0.907, respectively, the values of RPD were 3.15, 4.00, and 3.44, respectively, the relative errors of the predicted values and the measured values were 3.93%, 5.74%, and 3.69%, respectively. Practical applications This shows that it is feasible to use the Laida algorithm to extract the characteristic wavelengths and establish a prediction model for the SSC, TA, and VC content in cream strawberries. This study can provide a theoretical basis for the development of a rapid detection instrument for the quality of creamy strawberries.

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The aim of this work is to investigate the adsorption and release mechanism of 6-thioguanine (6TG) on transition metal (Fe,Co,Ni)-doped C60 and C60 fullerene nanomaterials by density functional theory (DFT) and time-dependent density functional theory (TD-DFT) to better understand the targeted drug delivery performance of fullerene to 6TG anticancer drug. The adsorption energy, solvation energy, and related chemical properties were calculated.According to thermodynamic analysis, the interaction between 6TG drug and fullerene nanocarriers is exothermic and spontaneous. Density of state (DOS) and natural bonding orbital (NBO) analyses showed that during the adsorption process of 6TG drug on the surface of fullerene, 6TG was the charge donor and fullerene was the charge acceptor. Atoms in molecule (AIM) and independent gradient model based on Hirshfeld partition (IGMH) analyses revealed Van der Waals and hydrogen bond interactions between the 6TG drug and fullerenes. In addition, fullerenes doped with transition metals can increase the solvation effect of the 6TG drug and shorten its release time from fullerenes. These results indicate that transition metal (Fe,Co,Ni)-doped C60 fullerene can be the promising anticancer drug delivery system for 6TG.

In order to improve the therapeutic efficacy and enhance targeted delivery of the 5-fluorouracil (5Fu) anticancer drug, a kind of potential carrier MB11N12 (M = B, Al, Ga) nanocage was designed.