Classification of early mechanical damage over time in pears based on hyperspectral imaging and transfer learning.

Mechanical damage of fresh fruit caused by compression and collision during harvesting and transportation is an urgent problem in the agricultural industry. The purpose of this work was to detect early mechanical damage of pears using hyperspectral imaging technology and advanced modeling techniques of transfer learning and convolutional neural networks. The visible/near-infrared hyperspectral imaging system was applied to obtain the intact and damaged pears at three time points (2, 12, and 24 h) after compression or collision damage. After the hyperspectral images were preprocessed and feature-extracted, ImageNet was used to pre-train ConvNeXt network, and then, transfer learning strategy was applied from compression damage to collision damage to build the T_ConvNeXt model for classification. The results showed that the test set accuracy of fine-tuned ConvNeXt model was 96.88% for compression damage time. For the classification of collision damage time, the test set accuracy of T_ConvNeXt network reached 96.61% and was 3.64% higher than the fine-tuned ConvNeXt network. The number of training samples was proportionally reduced to verify the superiority of the T_ConvNeXt model, and the model was compared with conventional machine learning algorithms. This study achieved the classification of mechanical damage over time and achieved a generalized model for different damage types. The accurate prediction of pear damage time is crucial for determining proper storage conditions and shelf-life time. PRACTICAL APPLICATION: The T_ConvNeXt model proposed in this paper transferred from compression damage to collision damage effectively promoted the generality of the damage time classification model. Guidelines for choosing an effective shelf life from a commercial aspect were presented.

Non-destructive sugar content assessment of multiple cultivars of melons by dielectric properties.

BACKGROUND: Non-destructive determination of the internal quality of fruit with a thick rind and of a large size is always difficult and challenging. To investigate the feasibility of the dielectric spectroscopy technique with respect to determining the sugar content of melons during the postharvest stage, three cultivars of melon samples (160 melons for each cultivar) were used to acquire dielectric spectra over the frequency range 20-4500 MHz. The three cultivars of melons were divided separately into a calibration set and a prediction set in a ratio of 3:1 by a joint x-y distance algorithm. Partial least squares (PLS) and extreme learning machine (ELM) methods were applied to develop individual-cultivar and multi-cultivar models based on full frequencies (FFs) and effective dielectric frequencies (EDFs) selected by the successive projection algorithm (SPA). RESULTS: The results showed that ELM models demonstrated a better performance than PLS models for the same input dielectric variables. Most of the models built based on the EDFs selected by SPA had a slightly worse performance compared to those based on FFs. For both PLS and ELM methods, the models for multi-cultivars demonstrated a worse calibration and prediction performance compared to those for individual cultivars. When individual-cultivar and multi-cultivar samples were used to build sugar content determination models, the best model was FFs-ELM (Rp  = 0.887, RMSEP = 0.986), FFs-ELM (Rp  = 0.870, RMSEP = 1.028), FFs-PLS (Rp  = 0.882, RMSEP = 1.010) and FFs-ELM (Rp  = 0.849, RMSEP = 1.085) for 'Hongyanliang', 'Xinzaomi', 'Manao' and multi-cultivar melons, respectively. CONCLUSION: The present study indicates that it is possible to develop both individual-cultivar and multi-cultivar models for determining the sugar content of melons based on the dielectric spectroscopy technique. © 2021 Society of Chemical Industry.

Prophylactic use of levosimendan in pediatric patients undergoing cardiac surgery: a prospective randomized controlled trial.

BACKGROUND: The administration of levosimendan prophylactically to patients undergoing cardiac surgery remains a controversial practice, and few studies have specifically assessed the value of this approach in pediatric patients. This study therefore sought to explore the safety and efficacy of prophylactic levosimendan administration to pediatric patients as a means of preventing low cardiac output syndrome (LCOS) based upon hemodynamic, biomarker, and pharmacokinetic readouts. METHODS: This was a single-center, double-blind, randomized, placebo-controlled trial. Patients ≤ 48 months old were enrolled between July 2018 and April 2019 and were randomly assigned to groups that received either placebo or levosimendan infusions for 48 h post-surgery, along with all other standard methods of care. LCOS incidence was the primary outcome of this study. RESULTS: A total of 187 patients were enrolled, of whom 94 and 93 received levosimendan and placebo, respectively. LCOS incidence did not differ significantly between the levosimendan and placebo groups (10 [10.6%] versus 18 [19.4%] patients, respectively; 95% confidence interval [CI] 0.19-1.13; p = 0.090) nor did 90-day mortality (3 [3.2%] versus 4 [4.3%] patients, CI 0.14-3.69, p = 0.693), duration of mechanical ventilation (median, 47.5 h and 39.5 h, respectively; p = 0.532), ICU stay (median, 114.5 h and 118 h, respectively; p = 0.442), and hospital stay (median, 20 days and 20 days, respectively; p = 0.806). The incidence of hypotension and cardiac arrhythmia did not differ significantly between the groups. Levels of levosimendan fell rapidly without any plateau in plasma concentrations during infusion. A multiple logistic regression indicated that randomization to the levosimendan group was a predictor of LCOS. CONCLUSIONS: Prophylactic levosimendan administration was safe in pediatric patients and had some benefit to postoperative hemodynamic parameters, but failed to provide significant benefit with respect to LCOS or 90-day mortality relative to placebo. TRIAL REGISTRATION: Name of the registry: Safety evaluation and therapeutic effect of levosimendan on the low cardiac output syndrome in patients after cardiopulmonary bypass. TRIAL REGISTRATION NUMBER: ChiCTR1800016594. Date of registration: 11 June 2018. URL of trial registry record: http://www.chictr.org.cn/index.aspx.