A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery’s state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies.

The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

Non-destructive testing (NDT) technology is pivotal in the quality assessment of agricultural products. In contrast to traditional manual testing, which is fraught with subjectivity, inefficiency, and the potential for sample damage, NDT technology has gained widespread application due to its advantages of objectivity, speed, and accuracy, and it has injected significant momentum into the intelligent development of the food industry and agriculture. Over the years, technological advancements have led to the development of NDT systems predicated on machine vision, spectral analysis, and bionic sensors. However, during practical application, these systems can be compromised by external environmental factors, the test samples themselves, or by the degradation and noise interference inherent in the testing equipment, leading to instability in the detection process. This instability severely impacts the accuracy and efficiency of the testing. Consequently, refining the detection methods and enhancing system stability have emerged as key focal points for research endeavors. This manuscript presents an overview of various prevalent non-destructive testing methodologies, summarizes how sample properties, external environments, and instrumentation factors affect the stability of testing in practical applications, organizes and analyzes solutions to enhance the stability of non-destructive testing of agricultural product quality based on current research, and offers recommendations for future investigations into the non-destructive testing technology of agricultural products.

The purpose of this paper was to explore the utility of an electronic nose to detect the quality of litchi fruit stored in different environments. In this study, a PEN3 electronic nose was adopted to test the storage time and hardness of litchi that were stored in three different types of environment (room temperature, refrigerator and controlled-atmosphere). After acquiring data about the hardness of the sample and from the electronic nose, linear discriminant analysis (LDA), canonical correlation analysis (CCA), BP neural network (BPNN) and BP neural network-partial least squares regression (BPNN-PLSR), were employed for data processing. The experimental results showed that the hardness of litchi fruits stored in all three environments decreased during storage. The litchi stored at room temperature had the fastest rate of decrease in hardness, followed by those stored in a refrigerator environment and under a controlled-atmosphere. LDA has a poor ability to classify the storage time of the three environments in which litchi was stored. BPNN can effectively recognize the storage time of litchi stored in a refrigerator and a controlled-atmosphere environment. However, the BPNN classification of the effect of room temperature storage on litchi was poor. CCA results show a significant correlation between electronic nose data and hardness data under the room temperature, and the correlation is more obvious for those under the refrigerator environment and controlled-atmosphere environment. The BPNN-PLSR can effectively predict the hardness of litchi under refrigerator storage conditions and a controlled-atmosphere environment. However, the BPNN-PLSR prediction of the effect of room temperature storage on litchi and global environment storage on litchi were poor. Thus, this experiment proved that an electronic nose can detect the quality of litchi under refrigeratored storage and a controlled-atmosphere environment. These results provide a useful reference for future studies on nondestructive and intelligent monitoring of fruit quality.

The objective of this study was to detect and monitor the flavor of tomatoes, as impacted by different postharvest handlings, including chilling storage (CS) and blanching treatment (BT). CS tomatoes were stored in a refrigerator at 5 °C and tested at storage day 0, 3, and 7. BT tomatoes were dipped in 50 or 100 °C water for 1 min, and tested immediately. The taste, mouth feel, and aroma of tomatoes were evaluated by testing the total soluble solid content (TSS), titratable acidity (TA), ratio of TSS and TA (TSS/TA), firmness, and electronic nose (E-nose) response to tomatoes. The experimental results showed that the CS can prevent taste and firmness loss to a certain extent, but the sensory results indicated that CS accelerated flavor loss due to the TSS/TA of CS tomatoes increasing slower than control. The taste and firmness of tomatoes were impacted slightly by 50 °C BT, and were significantly impacted by 100 °C BT. Based on physicochemical parameters, different postharvest handling treatments for tomatoes could not be classified except for the 100 °C BT treated tomatoes, which were significantly impacted in terms of taste and mouth feel. The E-nose is an efficient way to detect differences in postharvest handling treatments for tomatoes, and indicated significant aroma changes for CS and BT treated tomato fruit. The classification of tomatoes after different postharvest handling treatments, based on comprehensive flavor (physicochemical parameters and E-nose combined data), is better than that based on single physicochemical parameters or E-nose, and the comprehensive flavor of 100 °C BT tomatoes changed the most. Even so, the tomato flavor change during postharvest handlings is suggested to be detected and monitored by single E-nose data. The E-nose has also been proved as a feasible way to predict the TSS and firmness of tomato fruit rather than TA or TSS/TA, during the postharvest handing process.