Qinyi Yang’s research while affiliated with Zhejiang University and other places

Identifying the geographic origin of peaches will not only help producers obtain higher economic benefits, but also enable consumers to buy the most satisfactory fruits. In this study, the feasibility of distinguishing the geographic origin of four traditional famous peaches in China by visible-near infrared spectroscopy, fluorescence spectroscopy and image processing technology was explored. Visible-near infrared spectra and fluorescence spectra of 397 nm–1175 nm and color characteristics extracted from images were used to establish the support vector machine, k-nearest neighbor, random forest and extreme learning machine classification models. The factors most related to the geographic origin were found by decision tree analysis. The results showed that the support vector machine models had the highest classification accuracy, some reaching 100%. In order to improve the calculation speed, the spectral principal components were used, resulting in the accuracy of support vector machine, k-nearest neighbor and random forest models more than 95%. The decision tree showed that R value, the first principal component of fluorescence spectra and H value played a decisive role in identifying the geographic origin, leading to the accuracy of support vector machine, k-nearest neighbor and random forest models more than 95%. This study compared the advantages and disadvantages of visible-near infrared spectroscopy, fluorescence spectroscopy and image processing technology in identifying geographic origin, and found that the combination of these three methods could effectively distinguished the geographic origin of peaches.

The purpose of this study was to compare the accuracy and robustness of the detection models based on bulk optical properties (BOP) with that based on conventional spectroscopy in kiwifruit quality evaluation. 81 kiwifruit were selected as experimental samples in this study. A single integrating sphere system was built to estimate the bulk absorption coefficient (μa) and bulk reduced scattering coefficient (μs′) of samples and a self-designed online system was used to obtain transmission spectra. The relationship of μa and μs′ with SSC and flesh firmness was analyzed, and detection models were established using partial least squares regression (PLSR). Competitive adaptive reweighted sampling (CARS) was also used to eliminate the variables in the original spectra that do not contribute to the improvement of the model performance. Results showed that μa at 670 nm decreased with the increase of SSC, μa at 720–900 nm and 950–1000 nm increased with the increase of SSC, and spectra of μs′ decreased with the decreasing firmness. CARS-PLSR models were developed, based on μa, μs′, μa×μs′, μeff, μt′, and transmission spectra. The accuracy of the model based on BOP in predicting internal quality was better than that based on transmission spectra. The model based on μa was the best for SSC (Rp2 = 0.97, RMSEP = 0.25%), and the model based on μa×μs′ was the best for flesh firmness (Rp2 = 0.97, RMSEP = 0.02 N). 20 kiwifruit that differed from the experimental samples in planting orchard and harvest time were used to compare the robustness and portability of the models. Results showed that all SSC models and the firmness model based on μa×μs′ had good robustness and portability. However, the model based on transmission spectra had a poor performance in predicting the firmness of samples from the external validation set. This study provides an effective reference for the prediction of firmness and SSC based on BOP and transmission spectra of kiwifruit.