An appropriate drying method is crucial for producing high-quality dried apricots. In this study, the effects of four drying methods, hot air drying (HAD), infrared drying (IRD), pulse vacuum drying (PVD), and vacuum freeze-drying (VFD), on the drying kinetics and physical and nutritional characteristics of apricot slices were evaluated. PVD required the shortest time (16.25 h), followed by IRD (17.54 h), HAD (21.39 h), and VFD (34.64 h). VFD resulted in the best quality of apricot slices, with the smallest color difference (ΔE = 13.64), lowest water activity (0.312 ± 0.015) and browning degree (0.35), highest color saturation (62.84), lowest hardness (8.35 ± 0.47 N) and shrinkage (9.13 ± 0.65%), strongest rehydration ability (3.58 ± 0.11 g/g), a good microstructure, and high nutrient-retention rates (ascorbic acid content: 53.31 ± 0.58 mg/100 g, total phenolic content: 12.64 ± 0.50 mg GAE/g, and carotenoid content: 24.23 ± 0.58 mg/100 g) and antioxidant activity (DPPH: 21.10 ± 0.99 mmol Trolox/g and FRAP: 34.10 ± 0.81 mmol Trolox/g). The quality of PVD-treated apricot slices was second-best, and the quality of HAD-treated apricot slices was the worst. However, the energy consumption required for VFD was relatively high, while that required for PVD was lower. The results of this study provide a scientific basis for the large-scale industrial production of dried apricots.

Impact of agricultural residual plastic film on the growth and yield of drip-irrigated cotton in arid region of Xinjiang, China

The presence of microplastics in soil has become a significant global concern, posing a threat to environmental, agricultural, and human health. However, the use of various detection methods has led to significant discrepancies in the statistics reported for the abundance of soil microplastics across the available literature, resulting in poor comparability across the available literature. Studies have shown that plastic film residue is among the main primary sources of microplastics in farmland soils. Therefore, we manufactured and selected polyethylene microplastics (PE-MPs) from plastic film and combined them with soil samples after density separation treatment, forming a spiked test soil sample, which was subjected to density separation and extraction using a ZnCl₂ solution. The experiment used visual inspection by stereo microscopy, micro-Fourier transform infrared spectroscopy, micro-Raman spectrometry, laser direct infrared imaging, and pyrolysis-gas chromatography–mass spectroscopy for the quantitative analysis of spiked soil. This study systematically assessed and compared these five common soil microplastic detection methods. The results revealed different quantitative detection accuracies across various particle size ranges. Micro-Fourier transform infrared spectroscopy showed a broader applicable particle size range (20–5000 μm) and higher detection rate (91%), while the detection range (>50 μm) and rate (70%) of visual inspection through stereo microscopy were lower. For the identification of microplastic morphology, laser direct infrared imaging performed better, whereas micro-Fourier transform infrared spectroscopy had the advantage in determining the types of polymers present over micro-Raman spectrometry. The advantages, disadvantages, and detection rates of the above five detection methods were clarified during the detection process. Considering these findings and similar studies, we suggest three combinations of the evaluated methods to optimize the detection of PE-MPs.

Customers pay significant attention to the organoleptic and physicochemical attributes of their food with the improvement of their living standards. In this work, near infrared hyperspectral technology was used to evaluate the one-color parameter, a*, firmness, and soluble solid content (SSC) of Korla fragrant pears. Moreover, iteratively retaining informative variables (IRIV) and least square support vector machine (LS-SVM) were applied together to construct evaluating models for their quality parameters. A set of 200 samples was chosen and its hyperspectral data were acquired by using a hyperspectral imaging system. Optimal spectral preprocessing methods were selected to obtain out partial least square regression models (PLSRs). The results show that the combination of multiplicative scatter correction (MSC) and Savitsky-Golay (S-G) is the most effective spectral preprocessing method to evaluate the quality parameters of the fruit. Different characteristic wavelengths were selected to evaluate the a* value, the firmness, and the SSC of the Korla fragrant pears, respectively, after the 6 iterations. These values were obtained via IRIV and the reverse elimination method. The correlation coefficients of the validation set of the a* value, the firmness, and the SSC measure 0.927, 0.948, and 0.953, respectively. Furthermore, the values of the regression error weight, γ, and the kernel function parameter, σ², for the same parameters measure (8.67 × 10⁴, 1.21 × 10³), (1.45 × 10⁴, 2.93 × 10⁴), and (2.37 × 10⁵, 3.80 × 10³), respectively. This study demonstrates that the combination of LS-SVM and IRIV can be used to evaluate the a* value, the firmness, and the SSC of Korla fragrant pears to define their grade.