The detection of soluble solid content in Korla fragrant pear is a destructive and time‐consuming endeavor. In effort to remedy this, a nondestructive testing method based on electrical properties and artificial neural network was established in this study. Specifically, variations of electrical properties (e.g., equivalent parallel capacitance, quality factor, loss factor, equivalent parallel resistance, complex impedance, and equivalent parallel inductance) of Korla fragrant pears with accumulated temperature were tested using a workbench developed by ourselves. After that the characteristic variables of electrical properties were constructed by principal component analysis (PCA). In addition, three models were constructed to predict SSC in Korla fragrant pears based on the characteristic variables: general regression neural network (GRNN), back‐propagation neural network (BPNN), and adaptive network fuzzy inference system (ANFIS). The results indicated that the GRNN model has the best prediction effects of SSC (R² = 0.9743, RMSE = 0.2584), superior to that of the BPNN and ANFIS models. Results facilitate a successful, alternative application for rapid assessment of SSC of the maturation stage Korla fragrant pear.

Milk is a nutrient-rich food, and water adulteration in milk can reduce its quality and increase food safety risks. Nondestructive and efficient detection of milk adulteration levels is crucial to addressing this issue. This study employed a portable near-infrared spectrometer to measure and analyze the absorbance of milk samples within the wavelength range of 900 to 1,800 nm. Based on the original spectra, models such as soft independent modeling of class analogy (SIMCA), naive Bayes, k-nearest neighbors, and support vector machine were constructed for the discrimination of water-adulterated milk. Preprocessing methods including Savitzky-Golay convolutional smoothing, Savitzky-Golay filtered derivative, multiplicative scatter correction, standard normal variate, vector normalization (VN), and min-max normalization were applied to the original spectra. Additionally, models such as one-dimensional convolutional neural network, partial least squares regression, and support vector regression (SVR) were established for the prediction of water content in milk. The results showed that the SIMCA model achieved 100% discrimination accuracy = 1, sensitivity = 1, specificity = 1, precision = 1, F₁-score = 1 in the analysis of water-adulterated milk. In the prediction of milk water content, the VN-SVR model performed the best (coefficient of determination for the training set R_C² = 0.9996, root mean square error for the training set = 0.0477%, coefficient of determination for the test set R_P² = 0.9992, root mean square error for the test set [RMSEP] = 0.066%), and also exhibited the best performance in model validation (R_P² = 0.9760, RMSEP = 0.2554%). This study provides theoretical references and technical support for dairy product quality control and food safety regulation.

This study examined the development and trends in non-destructive testing technology for fruit quality. The status of the research field and the application hotspots were investigated to provide a reference for future research in this field. Relevant studies on the non-destructive testing of fruit quality published between 1993 and 2022 were identified in the core database Web of Science. The temporal distribution, spatial distribution, literature features, research progress, and leading research hotspots were quantified and visualised using bibliometrics. The findings revealed that there continues to be active research and publications on non-destructive testing technology for fruit quality, with a good development trend. China and the USA are the major contributors to research on non-destructive testing technology for fruit quality. The major research institutions include Zhejiang University and the United States Department of Agriculture. The major papers are published in Postharvest Biology and Technology and Acta Horticulturae, among others. These studies mainly focus on agriculture, food, and gardening, among other topics. The detection indices mainly concern internal quality, such as sugar degree and soluble solids, and apparent quality, such as hardness. The detection technologies mainly include electronic nose (E-nose) technology, machine vision technology, and spectral detection technology. In the future, technological developments in artificial intelligence and deep learning will further promote the maturation and application of non-destructive testing technologies for fruit quality.

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.

This study provides a method for the rapid, non-destructive testing of the internal quality of Korla fragrant pears. The dielectric constant (ε′) and dielectric loss factor (ε″) of pear samples were tested at 100 frequency points (range = 0.1–26.5 GHz) using a vector network analyzer and coaxial probe. The variations in the dielectric parameters of fragrant pears were analyzed. The linear relationships between the dielectric parameters and internal quality were explored. Internal quality prediction models for Korla fragrant pears were built using partial least squares regression (PLSR), support vector regression (SVR) and particle swarm optimization–least squares support vector regression (PSO-LSSVR). The optimal model was then determined. There was a weak correlation between the dielectric parameters and soluble solid content (SSC) under a single frequency. The model based on PLSR and using ε′ as a variable predicted hardness the best, while the model based on PLSR using ε″ as a variable predicted SSC the best. Its R and MSE values were 0.77 and 0.073 in hardness prediction, respectively, and 0.91 and 0.087 in SSC prediction. This study provides a new method for the non-destructive online testing of the internal quality of Korla fragrant pears.