(1) Background: Organic food produced in environmentally friendly farming systems has become increasingly popular. (2) Methods: We used a DNA metabarcoding approach to investigate the differences in the microbial community between organic and conventional ‘Huangguan’ pear fruit; and (3) Results: Compared to a conventional orchard, the fruit firmness in the organic orchard had significantly lowered after 30 days of shelf-life storage at 25 °C, and the soluble solids content (SSC), titratable acid (TA), and decay index were higher. There were differences in the microbial diversity between organic and conventional orchards pears. After 30 days of storage, Fusarium and Starmerella became the main epiphytic fungi in organic fruits, while Meyerozyma was dominant in conventional fruits. Gluconobacter, Acetobacter, and Komagataeibacter were dominant epiphytic bacteria on pears from both organic and conventional orchards after a 30-day storage period. Bacteroides, Muribaculaceae, and Nesterenkonia were the main endophytic bacteria throughout storage. There was a negative correlation between fruit firmness and decay index. Moreover, the abundance of Acetobacter and Starmerella were positively correlated with fruit firmness, while Muribaculaceae was negatively correlated, implying that these three microorganisms may be associated with the postharvest decay of organic fruit; (4) Conclusions: The difference in postharvest quality and decay in organic and conventional fruits could potentially be attributed to the variation in the microbial community during storage.

The non-destructive detection of fruit quality is indispensable in the agricultural and food industries. This study aimed to explore the application of hyperspectral imaging (HSI) technology, combined with machine learning, for a quality assessment of pears, so as to provide an efficient technical method. Six varieties of pears were used for inspection, including ‘Sucui No.1’, ‘Zaojinxiang’, ‘Huangguan’, ‘Akizuki’, ‘Yali’, and ‘Hongli No.1’. Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. They could enhance the predictive capability and reduce computational complexity. Furthermore, in order to construct a quality prediction model, integrating hyperspectral data from six pear varieties resulted in an RPD (Ratio of Performance to Deviation) exceeding 2.0 for all the quality parameters, indicating that increasing the fruit sample size and variety number further strengthened the robustness of the model. The Backpropagation Neural Network (BPNN) model could accurately distinguish six distinct pear varieties, achieving prediction accuracies of above 99% for both the calibration and test sets. In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.

The ‘Huangguan’ pear is one of the high-quality pear cultivars produced in China. However, the bagged fruit of the ‘Huangguan’ pear often suffers from peel browning spots after rain during their mature period. In this study, in an effort to discover the impact of bagging treatments on the occurrence of peel browning spots and fruit quality, fruits were covered by single-layer, two-layer, or triple-layer paper bags six weeks after reaching full bloom. The results showed that the bagged fruits were characterized by smooth surfaces and reduced lenticels compared with the unbagged ones. The unbagged and the two-layer bagged fruits had yellow/green peels, while the single- and triple-layer bagged ones had yellow/white peels. Compared with the unbagged fruits, the bagged fruits had higher vitamin C (Vc) contents and values of peel color indexes L and a and lower soluble solid contents (SSCs), titratable acid (TA) contents, absorbance index differences (I_AD), and b values. Additionally, the triple-layer bagged group was superior to other groups in terms of fruit quality, but it also had the maximum incidence of peel browning spots. Before and after the appearance of peel browning spots, the bagged fruits had smoother and thinner cuticles compared with the unbagged ones. Furthermore, the triple-layer bagged fruits had minimum lignin contents and maximum phenolic contents in their peels, with minimum activity of lignin synthesis-related enzymes such as phenylalanine ammonia lyase (PAL), peroxidase (POD), and polyphenol oxidase (PPO), as well as minimum expressions of relevant genes such as cinnamyl alcohol dehydrogenase (CAD), cinnamoyl CoA reductase (CCR), 4-coumarate: coenzyme A ligase (4CL6), and cinnamate 4-hydroxylase (C4H1). It was deduced that POD activity and the relative expressions of CAD9, CCR3, CCR4, and CCR5 may play key roles in the occurrence of peel browning spots. In summary, lignin synthesis affected the incidence of peel browning spots in bagged ‘Huangguan’ pears. This study provides a theoretical basis for understanding the incidence of peel browning spots in ‘Huangguan’ pears.

Mycotoxins are generated by a series of fungal pathogens in postharvest fruit, resulting in serious health threat to consumers and great economic loss to the fruit storage industry. The microbial differences between rotten and healthy fruit during storage and their relationship with mycotoxin production have not been fully studied. In this study, differences in microbial diversity between rotten and healthy fruit after 30 days of storage at ambient temperature were investigated using high-throughput sequencing technology in ‘Huangguan’ pear (Pyrus bretschneideri Rehd cv. Huangguan) harvested from five different producing regions of Hebei province, China. The bacterial genus Gluconobacter was much more abundant in rotten fruit (76.24%) than that in healthy fruit (32.36%). In addition, Komagataeibacter and Acetobacter were also relatively higher in abundance in rotten fruit. In contrast, bacterial genera Pantoea, Alistipes, Muribaculaceae, Lactobacillus, and Ruminococcaceae_UCG were found to be more abundant in healthy fruit. Fungal genera including Botryosphaeria, Colletotrichum, Valsa, Alternaria, Rosellinia, Fusarium, and Trichothecium were found to be abundant in rotten fruit. The results of principal coordinate analysis (PCoA) showed that there were significant differences in the microbial diversity of different regions. PAT (patulin) was detected in all rotten fruit samples, while tenuazonic acid (TeA), alternariol (AOH), and alternariolmonomethyl ether (AME) were only detected in samples collected from one region (Weixian). Canonical correlation analysis (CCA) and Pearson correlation analysis showed that the abundance of Alistipes and Pantoea were negatively correlated with the contents of PAT, suggesting that bacterial genera Alistipes and Pantoea have potential in reducing mycotoxin production in ‘Huangguan’ pear.

As a traditional folk medicine, pear paste has important nutritional and health effects. The physicochemical properties and antioxidant activities of pear pastes prepared from 23 different cultivars were investigated, including color parameters ( L*, a*, b* and h°), transmittance, pH, titratable acidity (TA), soluble sugar content, total phenolics content (TPC), total flavonoids content (TFC), DPPH and ^•OH radical scavenging activity (RSA), and ferric reducing antioxidant power (FRAP). It was demonstrated that the physicochemical properties and antioxidant activities of pear pastes from various cultivars differed significantly. Pear cultivars of “Mantianhong”, “Xiangshui” and “Anli” possessing higher TPC and TFC exhibited excellent antioxidant activity determined by DPPH RSA, ^•OH RSA and FRAP, while the lowest TPC and TFC was observed for the cultivars of "Xueqing", "Nansui", "Hongxiangsu", and “Xinli No. 7”, which also demonstrated the poor antioxidant activity. Multivariate analyses, including factor and cluster analysis, were used for the quality evaluation and separation of pear pastes based on their physicochemical and antioxidant properties. Factor analysis reduced the above thirteen parameters to final four effective ones, i.e. DPPH RSA, color b*, FRAP and TA, and subsequently these four parameters were used to construct the comprehensive evaluation prediction model for evaluating the quality of pear pastes. The pear pastes could be separated into three clusters and differentiated for the diverse of pear cultivars via cluster analysis. Consistently, “Mantianhong”, “Xiangshui” and “Anli” pear with higher quality clustered into one group, in contrast, "Xueqing", "Nansui", "Hongxiangsu", and “Xinli No. 7” with lower quality clustered into the other group. It provided a theoretical method to evaluate the quality of pear paste and may help the fruit processing industry select the more suitable pear cultivars for pear paste making.