Meng Qinglong, Tan Tao, Feng Shunan, Wen Qingchun, Shang Jing
School of Food Science and Engineering, Guiyang University, Guiyang, China.
Research Center of Nondestructive Testing for Agricultural Products of Guizhou Province, Guiyang, China.
Front Nutr. 2024 Mar 14;11:1364274. doi: 10.3389/fnut.2024.1364274. eCollection 2024.
Soluble solid content (SSC), firmness, and color (, , and ) are important physicochemical indices for assessing the quality and maturity of kiwifruits. Therefore, this research aimed to realize the nondestructive detection and visualization map for the physicochemical indices of kiwifruits at different maturity stages by hyperspectral imaging coupled with the chemometrics. To further improve the detection accuracy and working efficiency of the models, competitive adaptive reweighted sampling (CARS) and successive projection algorithm were employed to choose feature wavelengths for predicting the physicochemical indices of kiwifruits. Multiple linear regression (MLR) was designed to develop simplified detection models based on feature wavelengths for determining the physicochemical indices of kiwifruits. The results showed that 32, 18, 26, 29, and 32 feature wavelengths were extracted from 256 full wavelengths to predict the SSC, firmness, , , and , respectively, with the CARS algorithm. Not only was the working efficiency of the CARS-MLR model improved, but the prediction accuracy of the CARS-MLR model for determining the physicochemical indices was also at its relative best. The residual predictive deviations of the CARS-MLR model for determining the SSC, firmness, , , and were 3.09, 2.90, 2.32, 2.74, and 2.91, respectively, which were all above 2.3. Compared with the model based on the full spectra, the CARS-MLR model could be used to predict the physicochemical indices of kiwifruits. Finally, the visualization map for the physicochemical indices of kiwifruits at different maturity stages was generated by calculating the spectral response of each pixel on the kiwifruit samples with the CARS-MLR model. This made the detection for the physicochemical indices of kiwifruits more intuitive. This study demonstrates that hyperspectral imaging coupled with the chemometrics is promising for the nondestructive detection and visualization map for the physicochemical indices of kiwifruits, and also provides a novel theoretical basis for the nondestructive detection of kiwifruit quality.
可溶性固形物含量（SSC）、硬度和颜色（、和）是评估猕猴桃品质和成熟度的重要理化指标。因此，本研究旨在通过高光谱成像结合化学计量学实现不同成熟阶段猕猴桃理化指标的无损检测和可视化图谱。为进一步提高模型的检测精度和工作效率，采用竞争性自适应重加权采样（CARS）和连续投影算法选择用于预测猕猴桃理化指标的特征波长。设计多元线性回归（MLR）基于特征波长建立简化检测模型以测定猕猴桃的理化指标。结果表明，使用CARS算法从256个全波长中分别提取了32、18、26、29和32个特征波长来预测SSC、硬度、、和。不仅提高了CARS-MLR模型的工作效率，而且该模型测定理化指标的预测精度也处于相对最佳状态。CARS-MLR模型测定SSC、硬度、、和的剩余预测偏差分别为3.09、2.90、2.32、2.74和2.91，均高于2.3。与基于全光谱的模型相比，CARS-MLR模型可用于预测猕猴桃的理化指标。最后，利用CARS-MLR模型计算猕猴桃样品上每个像素的光谱响应，生成了不同成熟阶段猕猴桃理化指标的可视化图谱。这使得猕猴桃理化指标的检测更加直观。本研究表明，高光谱成像结合化学计量学在猕猴桃理化指标的无损检测和可视化图谱方面具有广阔前景，也为猕猴桃品质的无损检测提供了新的理论依据。
