Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears. By analyzing absorbance in the 900–1800 nm range, six preprocessing methods—Savitzky–Golay derivative (SGD), standard normal variate (SNV), multiplicative scatter correction (MSC), Savitzky–Golay smoothing (SGS), vector normalization (VN), and min-max normalization (MMN)—were applied to the raw spectral data. uninformative variable elimination (UVE) and successive projections algorithm (SPA) were then used to extract effective wavelengths. Partial least squares regression (PLSR) models were developed for SSC and firmness based on the extracted data. The results showed that all preprocessing and wavelength-extraction methods improved model accuracy. The optimal SSC prediction model was MSC-SPA-PLSR (R = 0.93, RMSE = 0.195), and the best hardness prediction model was MSC-UVE-PLSR (R = 0.83, RMSE = 0.249). This research aids in establishing a non-destructive testing system, offering producers a rapid and accurate quality assessment tool, and provides the food industry with better production control measures to enhance standardization and market competitiveness of Korla pears.

The difficulty in controlling the quality of Korla pears is the main factor limiting their market value. The key to solving this problem is to detect the color of Korla pears quickly and accurately. This study employed near-infrared spectroscopy (NIRS) technology to measure the absorbance of Korla fragrant pears. The full-spectrum data were pre-processed using six methods: Savitzky–Golay convolution smoothing (SGCS), Savitzky–Golay convolution derivative (SGCD), multiplicative scatter correction (MSC), vector normalization (VN), min–max normalization (MMN), and standard normal variate transformation (SNV). The pre-processed spectral data were subjected to characteristic band extraction using the successive projections algorithm (SPA) and uninformative variable elimination (UVE) methods. Subsequently, detection models for the color indices L*, a*, and b* of Korla fragrant pears were established using the partial least squares regression (PLSR) with full-spectrum and characteristic extracted spectral data. The optimal detection models were determined. The results indicated that pre-processing and characteristic extraction improved the accuracy of the PLSR model. The optimal detection model for the color index L* was SGCD-UVE-PLSR (correlation coefficient (R) = 0.80, Root Mean Square Error (RMSE) = 1.19); for the color index a*, it was VN-SPA-PLSR (R = 0.84 and RMSE = 1.28), and for the color index b*, it was MSC-UVE-PLSR (R = 0.84 and RMSE = 1.25). This research provides a theoretical reference for developing color detection instruments for Korla fragrant pears.

To enhance the economic benefits of the Korla fragrant pear industry and reduce fruit loss rates, this study investigates changes in pear fruit quality during storage under different damage types. An adaptive neuro-fuzzy inference system (ANFIS), tested with eight different membership functions, was used to predict the hardness and soluble solids content (SSC) of pears stored under various damage conditions. The results showed that both hardness and SSC of damaged pears decreased with prolonged storage time. During identical storage periods, more severe damage led to faster declines in hardness and SSC, among which impact loads and combined impact-compression loads caused the most rapid reductions in pear hardness and SSC. The ANFIS model with the gauss2mf membership function achieved optimal prediction accuracy for pear hardness (RMSE = 0.2207, R² = 0.9434); the ANFIS model with the trimf membership function yielded the best prediction for pear SSC (RMSE = 0.2016, R² = 0.9701). This study provides a theoretical basis for optimizing warehouse management and quality control of Korla fragrant pears, and for determining the optimal treatment window during storage.

In the postharvest supply chain, directly moving Korla fragrant pears from ice temperature storage to ambient display readily induces quality deterioration, shortens shelf life, and causes substantial economic losses. Using Korla fragrant pears as the study system, we propose a gradual rewarming protocol. We optimised temperature and time parameters with response surface methodology and validated the protocol by comparing changes in shelf-life quality and microstructure under gradual versus direct rewarming. Results indicate that rewarming at 6 °C for 12 h is the optimal condition for maintaining overall postharvest quality. Under these conditions, gradient rewarming significantly improved physical and chemical quality. Compared with direct rewarming, weight loss decreased by 2.6 percent, firmness increased to 4.23 kg/cm², the peak soluble solids content reached 12.5%, the respiratory peak was delayed and the rate slowed, and shelf life was extended by about 80%. Microstructural verification showed a more compact cellular arrangement, reduced intercellular spaces, and the mildest degree of cell collapse in pears subjected to gradient rewarming. The proposed gradient rewarming protocol provides theoretical and practical guidance for optimising temperature control strategies across the postharvest cold chain and retail stages for Korla fragrant pears.