Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ², for the same parameters measure (8.67 × 10⁴, 1.21 × 10³), (1.45 × 10⁴, 2.93 × 10⁴), and (2.37 × 10⁵, 3.80 × 10³), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade.

Food safety has recently attracted increasing attention, underscoring the need for timely and accurate on-site testing technologies. Optical detection, among various methods, offers notable advantages, including ease of use and rapid results, making it a promising approach for food safety applications. This paper reviews the fundamental principles of optical inspection for food field examination and explores its practical applications, including techniques such as surface-enhanced Raman scattering, UV–visible absorption spectroscopy, and fluorescence detection. Furthermore, this review discusses the integration of detection technologies with nanotechnology and smartphone-based systems. In addition, this review discusses the current applications, challenges, and potential solutions associated with optical detection in on-site food inspections.