Luo Yifeng, Jiang Xu, Fu Xiaping
Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Foods. 2021 Sep 11;10(9):2151. doi: 10.3390/foods10092151.
Spatial frequency domain imaging (SFDI) is a non-contact wide-field optical imaging technique for optical property detection. This study aimed to establish an SFDI system and investigate the effects of system calibration, error analysis and correction on the measurement of optical properties. Optical parameter characteristic measurements of normal pears with three different damage types were performed using the calibrated system. The obtained absorption coefficient and the reduced scattering coefficient were used for discriminating pears with different surface damage using a linear discriminant analysis model. The results showed that at 527 nm and 675 nm, the pears' quadruple classification (normal, bruised, scratched and abraded) accuracy using the SFDI technique was 92.5% and 83.8%, respectively, which has an advantage compared with the conventional planar light classification results of 82.5% and 77.5%. The three-way classification (normal, minor damage and serious damage) SFDI technique was as high as 100% and 98.8% at 527 nm and 675 nm, respectively, while the classification accuracy of conventional planar light was 93.8% and 93.8%, respectively. The results of this study indicated that SFDI has the potential to detect different damage types in fruit and that the SFDI technique has a promising future in agricultural product quality inspection in further research.
空间频域成像（SFDI）是一种用于光学特性检测的非接触式宽场光学成像技术。本研究旨在建立一个SFDI系统，并研究系统校准、误差分析和校正对光学特性测量的影响。使用校准后的系统对具有三种不同损伤类型的正常梨进行光学参数特征测量。利用线性判别分析模型，将获得的吸收系数和约化散射系数用于区分具有不同表面损伤的梨。结果表明，在527nm和675nm波长下，使用SFDI技术对梨进行四重分类（正常、瘀伤、划伤和磨损）的准确率分别为92.5%和83.8%，与传统平面光分类结果的82.5%和77.5%相比具有优势。在527nm和675nm波长下，SFDI技术的三元分类（正常、轻度损伤和重度损伤）分别高达100%和98.8%，而传统平面光的分类准确率分别为93.8%和93.8%。本研究结果表明，SFDI有潜力检测水果中的不同损伤类型，并且在进一步研究中，SFDI技术在农产品质量检测方面具有广阔的前景。
