The three-channel spectral sensitivity of a trichromatic camera represents the characteristics of system color space. It is a mapping bridge from the spectral information of a scene to the response value of a camera. In this paper, we propose an estimation method for three-channel spectral sensitivity of a trichromatic camera. It includes calibration experiment by orthogonal test design and the data processing by window filtering. The calibration experiment was first designed by an orthogonal table of the 9-level and 3-factor. A rough estimation model of spectral sensitivity is established on the data pairs of the system input and output in calibration experiments. The data of rough estimation is then modulated by two window filters on frequency and spatial domain. The Luther-Ives condition and the smoothness condition are introduced to design the window, and help to achieve the optimal estimation of the system spectral sensitivity. Finally, the proposed method is verified by some comparison experiments. The results show that the estimated spectral sensitivity is basically consistent with the measured results of the monochromator experiments, the relative full-scale errors of the RGB three-channel is obviously lower than the Wiener filtering method and the Fourier band-limitedness method. The proposed method can estimate the spectral sensitivity of the trichromatic digital camera very well, which is of great significance for the colorimetric characterization and evaluation of imaging systems.

Beef freshness is a critical indicator of meat quality and safety, and its rapid, non-destructive detection is of significant importance for ensuring consumer health and enhancing quality control throughout the meat industry chain. This study developed a novel methodology for non-destructive beef freshness assessment using visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning, explainable artificial intelligence (xAI) techniques, and the SHapley Additive exPlanations (SHAP) framework. An improved hybrid heuristic method, particle swarm optimization–genetic algorithm (PSOGA), was used for feature selection, optimizing the wavelength subset for predicting beef quality indicators, including total volatile basic nitrogen (TVB-N) and color parameters (L*, a*, and b*). The eXtreme Gradient Boosting (XGBoost) was employed for regression modeling, and the results showed that PSOGA significantly outperforms traditional methods, with the PSOGA-XGBoost model achieving a satisfactory prediction accuracy (R²p values of 0.9504 for TVB-N, 0.9540 for L*, 0.8939 for a*, and 0.9416 for b*). The SHAP framework identified the key wavelengths as 1236 nm and 1316 nm for TVB-N, 728 nm for L*, 576 nm for a*, and 604 nm for b*, providing valuable insights into the determination of key wavelengths and enhancing the interpretability of the model. The results demonstrated the effectiveness of PSOGA and SHAP, providing a promising analytical method for monitoring beef freshness.

Colorimetric characterization is the basis of color information management in color imaging systems. In this paper, we propose a colorimetric characterization method based on kernel partial least squares (KPLS) for color imaging systems. This method takes the kernel function expansion of the three-channel response values (RGB) in the device-dependent space of the imaging system as input feature vectors, and CIE-1931 XYZ as output vectors. We first establish a KPLS color-characterization model for color imaging systems. Then we determine the hyperparameters based on nested cross validation and grid search; a color space transformation model is realized. The proposed model is validated with experiments. The CIELAB, CIELUV and CIEDE2000 color differences are used as evaluation metrics. The results of the nested cross validation test for the ColorChecker SG chart show that the proposed model is superior to the weighted nonlinear regression model and the neural network model. The method proposed in this paper has good prediction accuracy.

Two studies were conducted to investigate the impact of fresh pork display lamps on consumers’ sensory responses to pork products. In the first experiment, 63 participants were asked to evaluate pork products with different degrees of freshness under four fresh pork display lights and two LED lights. In the other experiment, 30 subjects participated in the Farnsworth–Munsell 100 hue test under the same lamps, with the aim of showing whether the fresh pork display lamps impaired color discrimination. The results showed that the light source had a significant effect on the color appearance evaluation of the pork products. The ratings for perceived freshness under the fresh pork display lamps were significantly higher than those of ordinary LED lamps, while the color discrimination performance of the subjects under those lamps was poor. It was demonstrated that improper component proportions of the light spectrum could influence observers’ assessment of meat quality and weaken the observers’ ability to distinguish the freshness level. Through this study, the authors would like to remind lamp users and manufacturers to not only be concerned about the improvement in the color appearance of pork, but also the need for consumers to be aware of the authentic freshness of the pork products.

High dynamic range imaging exhibits lower resolution in both highly bright and deeply dark colors, which results in reduced accuracy in the measurement of photometry and colorimetry using color imaging systems. Based on the nonlinear capability of the Gaussian kernel function and the global linear trend of the linear kernel function, a Gaussian-linear fusion kernel is designed. Through multi-dimensional space mapped by the designed fusion kernel, a kernel XGBoost colorimetric characterization model is proposed. Combining fusion kernel and XGBoost, the model possesses efficient feature selection and complex feature interaction capabilities. Model performance was evaluated using 10-fold cross-validation. The proposed model achieves a CIE LAB color difference of 2.71 units and a CIE DE2000 color difference of 2.08 units on average, which outperforms the partial least squares regression, the radial basis function neural network, and so on. The proposed model can capture colorimetric characteristics of a color imaging system more effectively and enhance detail preservation. This research improves the accuracy of colorimetric characterization and can provide higher accuracy in colorimetric measurement for high dynamic range imaging.