Huaping Luo

The online version contains supplementary material available at 10.1038/s41598-026-52412-y.

To reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact of moisture content on the detection model effect of SSC in apricot. This study showed that as moisture content decreased, both the values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, reflecting the progressive weakening of moisture-related spectral characteristics. Furthermore, within the 450–1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Among the four moisture intervals, the optimal moisture range for apricot SSC detection is 30%-48%. The model exhibits an R_p² of 0.9690, an RMSEP of 0.5712°Brix, and an RPD of 5.7989, meeting the requirements for high-precision quantitative analysis. This study reveals variations in the hyperspectral SSC prediction performance across different moisture content ranges during the apricot drying process, providing a theoretical basis and technical reference for subsequent offline quality assessment and process monitoring of dried apricots.

To reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact of moisture content on the detection model effect of SSC in apricot. This study showed that as moisture content decreased, both the absolute values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, indicating a weakening of moisture characteristics. Furthermore, within the 450 nm-1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Under 8-hour apricot drying conditions, the model achieved optimal prediction accuracy with an R _p of 0.9844, RMSEP of 0.5712, and RPD of 5.7989, meeting high-precision quantitative requirements. This study discovered the influence of different moisture intervals on the hyperspectral SSC prediction model of apricot fruits, laying a foundation for the subsequent research on moisture content correction spectra to eliminate the influence of moisture content on spectral detection of SSC content.

Moisture content is one of the key indicators for evaluating the quality of apricots. When moisture levels fluctuate over an excessively wide range, scattering effects and absorption characteristics interfere with each other, making it difficult for a single model to achieve accurate predictions across the entire range. This study investigates precision modeling methods applicable to different moisture intervals based on spectral morphological features. By extracting the spectral morphological features of the water-sensitive regions (peak and valley) and conducting Pearson correlation analysis, the spectral morphological feature parameters with relatively strong correlations were selected, and they were combined with the characteristic bands to construct a segmented model for water content intervals. The results indicate that spectral morphological features of apricots within the 25–40% and 40–55% moisture range exhibit a certain correlation with moisture content. A weak correlation is observed in the 55–70% moisture range. After preliminary fusion modeling of spectral morphological features and characteristic bands for apricots across different moisture ranges, further analysis revealed that moisture content models based on valley morphology features and characteristic bands outperformed those based on peak morphology features and characteristic bands, demonstrating superior representational capability. By establishing a fusion model based on the spectral morphological parameters selected through Pearson’s method and the characteristic bands, the detection accuracy and model stability in the 25–70% moisture content range have been effectively improved. Among all the models covering different moisture content ranges, the model for the 55–70% moisture content range has the best prediction effect. The correlation coefficient of its prediction set reaches 0.8723, and the Ratio of Performance to Interquartile Range (RPIQ) is 2.5220, indicating that this range is the most suitable for establishing a high-precision quantitative moisture content detection model. This research effectively solved the problem of spectral response distortion caused by wide variations in moisture content and improved the prediction accuracy of the moisture content detection model for apricots.

The outdoor spectral detection of winter jujube quality is affected by complex ambient light and surface heterogeneity, resulting in limited inversion accuracy. To address this problem, this study proposes a correction method for outdoor spectral inversion based on the bidirectional polarization reflectance distribution function (BPDF) model. It was used to enhance the detection accuracy of water content and soluble solid (SSC) content of winter jujube. Experimentally, 900–1750 nm hyperspectral data of ripe winter jujube samples were collected at non-polarization and 0°, 45°, 90°, and 135° polarization azimuths. The spectra were inverted using four semi-empirical BPDF models, Nadal–Breon, Litvinov, Maignan and Xie–Cheng, and the corrected spectra were obtained by mean fusion. The quality prediction models are subsequently combined with the competitive adaptive reweighting algorithm (CARS) and partial least squares (PLS). The results showed that the modified spectra significantly optimized the prediction performance. The prediction set correlation coefficients (Rp) of the water content and SSC models were improved by 10–30% compared with the original spectra. The percentage of models with RPIQ values greater than 2 increased from 40% to 60%. Among them, the Litvinov model performs outstandingly in the direction of no polarization and 135° polarization, with the highest Rp of 0.8829 for water content prediction and RPIQ of 2.54. The Xie–Cheng model has an RPIQ of 2.64 for SSC prediction at 90° polarization, which shows the advantage of sensitivity to the deeper constituents. The different models complemented each other in multi-polarization scenarios. The Nadal–Breon model was suitable for epidermal reflection-dominated scenarios, and the Maignan model efficiently coupled epidermal and internal moisture characteristics through the moisture sensitivity index. The study verifies the effectiveness of the spectral correction method based on the BPDF model for outdoor quality detection of winter jujube, which provides a new path for the spectral detection of agricultural products in complex environments. In the future, it is necessary to further optimize the dynamic adjustment mechanism of the model parameters and improve the ability of environmental interference correction by combining multi-source data fusion.

The spectral information extracted from hyperspectral images is characterized by redundancy and complexity, while the spectral morphological features extracted from the spectral information help to simplify the data and provide rich information about the material composition. This study is based on using spectral morphological features to quantitatively detect the water content of winter jujubes, and it extends the research scope to the composite effect of spectral morphological features on the basis of previous research. Firstly, a multiple linear regression analysis was carried out on different characteristic bands. Secondly, the multiple regression terms with high significance levels were used as the characteristic variables to be fused with the extracted characteristic wavelength variables for the data fusion. Finally, a partial least squares model was established for the water content of the winter jujubes. The results of the study show that a quantitative relationship can be established between the spectral morphology characteristics and the water content of winter jujubes. The coefficients of determination of the regression equations under the characteristic bands with center wavelengths of 1024 nm, 1146 nm, 1348 nm, and 1405 nm were 0.8449, 0.7944, 0.7479, and 0.9477, respectively. After fusing the spectral morphological features, the partial least squares modeling effects were all improved. The optimal model was the fusion model at a center wavelength of 1146 nm with a correlation coefficient of 0.9942 for the calibration set and 0.8698 for the prediction set. The overall results showed that the wave valley is more reflective of the fruit quality, and the morphological characteristics of the wave valley are more suitable than those of the wave peak for the quantitative detection of the moisture content of winter jujubes.

To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

In this paper, 317 literature in the Web of Science (WoS) related to research on apple by near‐infrared spectroscopy (NIRS) were drawn on the knowledge map of the number of literature, the co‐occurrence network of authors and institutions, the co‐occurrence and clustering of keywords based on CiteSpace. And a related analysis was carried out. Combined with the results of visual analysis and related literature, the research hotspots were sorted out and discussed. This paper provides a certain reference for relevant researchers to study in this field and provides a new method for macroscopically grasping the current status of apple quality detection research, which helps new researchers to quickly integrate into this field and obtain more valuable scientific information.

To establish the on-line near infrared spectral correction model for the jujube quality of Southern Xinjiang, the main influence factors of online testing results were analyzed, and the corresponding parameters were studied. First of all, the collecting conditions of different jujube were set, such as measurement condition, spectral region, and the parameters of the apparatus. With near infrared spectrometer and self-designed jujube batch collecting attachment, the quality spectrum of jujube was obtained, and combining spectral preprocessing and detection precision, condition parameters were selected. Secondly, through PLS spectrum correction with different modeling parameters and two-dimensional correlation spectroscopy analysis, Brix characteristic spectral parameters were selected. The results showed that with sugar degree central wavelength 9116, 9 418 and 10,500 cm(-1), acquisition resolution 16 cm(-1), and scan number 8, the sugar degree relative error was 8%-10%, the size of single grain jujube spectra was reduced to 1/10 of the original, and the time was reduced to 3 seconds. It was concluded that with the experimental parameters, the spectra were compressed, a primary online correction model was established, and the jujube quality online detection with near infrared spectroscopy was basically realized.

In the present paper, both the physical characteristics and spectral signatures of southern Xinjiang jujube were studied. With the methods of repetitive adjustment and stepwise refinement, the analysis diagram of jujube quality subdivision and characteristic factors were obtained. In actual practice, the spectra repeatability and quality can be improved by setting an optimum acquisition parameter according to different experimental requirements. Through experiments the characteristic factors of crack browning, luster and compositions were obtained, which demonstrates a distinguishing downing characteristic spectral line at the wave number of 10 170 cm(-1). Through characteristic analysis of jujube NIR spectra, the correspondence between the jujube spectra and their qualities was established, which lays the foundation for jujube qualities' characteristic coding in the future. The application of near-infrared topology method in the quality analysis of southern Xinjiang jujube is cost saving, which has a broad application prospect in establishing the NIR analytical standard and model database sharing of the jujube's quality in the future.