Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology.

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes' growth stage and color coordinates, providing great significance for designing a tomato quality grading system.

A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging.

The quality of tomatoes is usually predicted by measuring a single index, rather than a comprehensive index. To find a comprehensive index, visible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes, and twelve quality indexes were measured as the reference standards. The changing trends and correlations of different indexes were analyzed, and comprehensive quality index (CQI) was proposed through factor analysis. The characteristic wavelengths were selected by successive projection algorithm (SPA) based on the hyperspectral data, which was used to establish three regression models for CQI prediction. The result indicated that MLR achieved good performance withRV2 = 0.87, RMSEV = 1.33 and RPD = 2.58. After that, spatial distribution map was generated to visualize the CQI in tomato fruit. This study indicated that the comprehensive quality of tomatoes can be predicted non-destructively based on hyperspectral imaging and chemometrics, determining the optimal harvesting period.

The burden of premature ventricular contractions predicts adverse fetal and neonatal outcomes among pregnant women without structural heart disease: A prospective cohort study.

BACKGROUND: Premature ventricular contractions (PVCs) may increase during pregnancy, however, few studies have evaluated the relationship between PVCs and the pregnant outcomes. HYPOTHESIS: PVCs may increase the adverse fetal/neonatal outcomes in pregnant women. METHODS: Six thousand one hundred and forty-eight pregnant women were prospectively enrolled in our center between 2017 and 2019 in the study. The average PVC burden was determined by calculating the number of PVCs in total beats. Those who had a PVC burden >0.5% were divided into two groups based on the presence or absence of adverse fetal or neonatal events. The adverse outcomes were compared between the groups to assess the impact of PVCs on pregnancy. RESULTS: A total of 103 (1.68%) women with a PVC burden >0.5% were recorded. Among them, 17 adverse events (12 cases) were documented, which was significantly higher than that among women without PVCs (11.65% vs. 2.93%, p < .01). The median PVC burden among pregnant women with PVCs was 2.84% (1.02%-6.1%). Furthermore, compared with that of the women without adverse events, the median PVC burden of women with adverse fetal or neonatal outcomes was significantly higher (9.02% vs. 2.30%, p < .01). Multivariate logistic regression analysis demonstrated that not the LVEF, heart rate and bigeminy, but only the PVC burden was associated with adverse fetal or neonatal outcomes among pregnant women with PVCs (OR: 1.34, 95% CI [1.11-1.61], p < .01). CONCLUSIONS: Frequent PVCs have adverse effects on pregnancy, and the PVC burden might be an important factor associated with adverse fetal and neonatal outcomes among pregnant women with PVCs.