The transmission spectrum of apples is affected by the fruit’s size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the R_p of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the R_p of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

The nondestructive discrimination model based on near-infrared is usually established by detected spectra and chemometric methods. However, the inherent differences between instruments prevent the model from being used universally, and calibration transfer is often used to solve these problems. Standard-sample calibration transfer requires additional standard samples to build a mathematical mapping between instruments. Thus, standard-free calibration transfer is a research hotspot in this field. Based on near-infrared spectroscopy (NIRS), the new combined strategy of wavelength selection and standard-free calibration transfer was proposed to transfer the model between two portable near-infrared spectrometers. Three transfer learning (TL) algorithms—transferred component analysis (TCA), balanced distribution adaptation (BDA), and manifold embedded distribution alignment (MEDA)—were applied to achieve standard-free calibration transfer. Moreover, this paper presents a relative error analysis (REA) method to select wavelength. To select the optimal model, the parameters of accuracy, precision, and recall were examined to evaluate the discriminatory capacities of each model. The findings show that the MEDA-REA model is capable of higher prediction accuracy (accuracy = 94.54%) than the other transferring models (TCA, BDA, MEDA, TCA-REA, and BDA-REA), and it is demonstrated that the new strategy has good transmission performance. Moreover, REA shows the potential to filter wavebands for calibration transfer and simplify the transferable model.

This study aims to comprehensively evaluate the internal quality changes in apples during storage via near-infrared spectroscopy. Specifically, we focus on the performance differences in different apple varieties under diverse storage conditions and construct predictive models to determine the optimal storage period. By using near-infrared spectroscopy technology, 384 samples of four apple varieties (Xinjiang Akesu, Wafangdian Huangyuanshuai, Shandong Fuji, and Luochuan Fuji) were analyzed to monitor the changes in their soluble solid content (SSC) and fruit firmness within 7 weeks. The results indicated that, under cold storage conditions, SSC and firmness gradually decreased after peaking between the third and fifth weeks, while the opposite trend was observed at room temperature. To enhance the predictive accuracy of the model, several pretreatment methods were employed, including standardization, multiplicative scatter correction (MSC), and standard normal variate transformation (SNV). Additionally, competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were utilized for band selection. These pretreatment and selection processes significantly reduced noise and improved model reliability. The best results were achieved with the Normalization-CARS-PLS model for the sugar content at 1 °C, which demonstrated an optimal predictive correlation coefficient (Rp) of 0.904 and a root mean square error of prediction (RMSEP) of 0.67. For firmness at room temperature, the Normalization-CARS-PLS model also showed an excellent performance, with an Rp of 0.823 and an RMSEP of 0.809. The study of the quality of four varieties of apples under three storage conditions in this paper was able to analyze the changes in the internal quality of apples and predict the optimal storage period of different varieties of apples, which is important for guiding the optimal storage period of apples before ripening.

This study investigates spherical methyl silicone resin, a potentially environmentally friendly additive free of sulfur, phosphorus, and chlorine, as a lubricant additive in polyethylene glycol 200 (PEG 200) base oils. We evaluated concentration-response characteristics and tribological performance across PEG base oils containing 0.01–0.05 wt% resin. Tribological testing was conducted with a four-ball wear tester at 98 N and 1450 rpm for 30 min. All tested concentrations demonstrated excellent friction-reduction and anti-wear performance, with an optimal efficacy observed at 0.02 wt%. Surface characterization was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. This friction-reducing and anti-wear performance is attributed to the formation of silicon-oxygen species and graphene-like carbon structures, thereby effectively suppressing direct surface contact and mitigating wear. Consequently, spherical methyl silicone resin demonstrates considerable potential as a green lubricant additive for bearing steel applications.