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To understand the effect of 1-(2-chloropyridin-4-yl)-3-phenylurea (CPPU) on the optical parameters and internal quality of ‘Xuxiang’ kiwifruit during growth and the relationship between optical parameters and internal quality. Growing ‘Xuxiang’ kiwifruits treated with 0, 10 and 20 mg/L CPPU solutions were measured for absorption coefficient μa and reduced scattering coefficient μs’ using single integrating sphere system (950–1 650 nm). The internal quality indices, such as soluble solid content (SSC), moisture content and firmness, were also measured. Partial least squares regression (PLSR) was used to build an internal quality prediction model based on optical parameters. The results showed that CPPU reduced the firmness and increased the moisture content, but had no significant effect on the SSC of kiwifruits (P > 0.05). CPPU led to changes in the optical parameters of kiwifruits. μa and μs’ showed different correlations with the same internal quality attribute of kiwifruits, the correlation coefficients changed with wavelength, and there was a good correlation in a certain band. The PLSR model based on μa had the best prediction effect on the SSC and moisture content with correlation coefficient of prediction (Rp) of 0.709 and 0.790 and root mean square error of prediction (RMSEP) of 0.579% and 0.408%, respectively, while the model based on μa + μs’ had the best performance in predicting the hardness (Rp = 0.796, RMSEP = 7.890 N). This study provides a theoretical basis for predicting the internal quality of kiwifruits based on spectral technology.

This study introduced a novel dual fixation method for the pulmonary vasculature and lung tissue in pulmonary hypertension (PH) rats, addressing the limitations of traditional fixation methods that failed to accurately preserve the in vivo status of pulmonary vascular morphology. The modified method involved a dual fixation process, combining individualized ventilation support and vascular perfusion to simulate the respiratory motion, pulmonary artery pressure and right ventricular output of the rat under in vivo conditions. Utilizing a monocrotaline-induced PH rat model, this study compared the dual fixation with the traditional immersion fixation, focusing on the quantitative assessment of alveolar expansion degree, capillary patency, endothelial cell quantity and wall thickness of pulmonary vein and artery. The results demonstrated that the dual fixation is superior in maintaining the authenticity and integrity of lung tissue and more sensitive in the evaluation of pulmonary artery hypertrophy, providing a more reliable representation of pulmonary vascular remodeling associated with PH. [ABSTRACT FROM AUTHOR]

BACKGROUND: Milk somatic cell count (SCC) is an international standard for identifying mastitis in dairy cows and measuring raw milk quality. Milk SCC can be predicted based on dielectric relaxation parameters (DRPs). We noted a high correlation between DRPs and the milk composition content (MCC), and so we hypothesized that combining DRPs with MCC could improve the prediction accuracy of milk SCC. The present study aimed to analyze the relationship between milk SCC, DRPs and MCC, as well as to investigate the potential of combining DRPs with MCC to improve the prediction accuracy of milk SCC. RESULTS: The dielectric spectra (20–4500 MHz) of 276 milk samples were measured, and their DRPs (εl, εh, Δε, τ and σ) were solved by the modified Debye equation. The SCC prediction models were developed using dielectric full spectra, DRPs and DRPs combined with MCC. The results showed the correlations between DRPs (εl, εh, Δε and σ) and MCC (fat, protein, lactose and total solids) were high, and SCC exhibited a non‐linear relationship with DRPs and MCC. The 5DRPs + MCC‐generalized regression neural network model had the best prediction, with a standard error of prediction for prediction of 0.143 log SCC mL−1 and residual of the prediction bias of 2.870, which was superior to the models based on full spectra, DRPs and near‐infrared or visible/near‐infrared. CONCLUSION: The present study has improved the prediction accuracy of milk SCC based on the DRPs combing MCC and provides a new method for dairy farming and milk quality assessment. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A terahertz photonic crystal fiber with two sensing channels was designed. Graphene coated on the micro-grooves in the cladding was used as plasma material to introduce tunability. The dispersion relation, mode coupling, and sensing characteristics of the fiber were studied using the finite element method. Ultrahigh sensitivity of 2.014 THz/RIU and 0.734 GHz/°C were obtained for analytes with refractive index in the range of 1.33 to 1.4 and environment temperature in the range of 10–60 °C, respectively. Refractive index resolution can reach the order of 10−5. The dual parameter simultaneous detection, dynamic tunable characteristics, and working in the low-frequency range of terahertz enable the designed photonic crystal fiber to have application prospects in the field of biosensing. [ABSTRACT FROM AUTHOR]

Summary: During harvesting, transportation and storage of kiwifruit, the flesh is often bruised by collision or compression. However, the bruises in kiwifruit are extremely difficult to recognise by naked eyes and are called hidden bruises. Accordingly, a fast method for detecting hidden bruises in kiwifruit was developed in this study based on hyperspectral imaging (HSI) coupled with deep learning. The spectral range (924–1277 nm) and feature wavelengths (928.19, 1051.03 and 1190.47 nm) sensitive to hidden bruises in kiwifruit were selected using the principal component analysis (PCA). Subsequently, three‐channel images (Dataset 1), grayscale images (Dataset 2) and pseudo‐colour images (Dataset 3) were generated according to the images of feature wavelengths of the kiwifruit. The YOLOv5s model for detecting the hidden bruised areas of the kiwifruit was developed using these three datasets. The results showed that the YOLOv5s detection model performed best at Dataset 1, and the values of Precision, Recall, F1, mAP and FNR of this model were 98.25%, 97.50%, 97.87%, 99.12% and 2.50% respectively. The study showed that HSI technology combined with the YOLOv5s model can effectively detect hidden bruises in kiwifruit, providing references for detecting hidden bruises in other fruit. [ABSTRACT FROM AUTHOR]