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BACKGROUND: Taraxacum kok-saghyz Rodin (TKS) is a highly potential source of natural rubber (NR) due to its wide range of suitable planting areas, strong adaptability, and suitability for mechanized planting and harvesting. However, current methods for detecting NR content are relatively cumbersome, necessitating the development of a rapid detection model. This study used near-infrared spectroscopy technology to establish a rapid detection model for NR content in TKS root segments and powder samples. The K445 strain at different growth stages within a year and 129 TKS samples hybridized with dandelion were used to obtain their near-infrared spectral data. The rubber content in the root of the samples was detected using the alkaline boiling method. The Monte Carlo sampling method (MCS) was used to filter abnormal data from the root segments of TKS and powder samples, respectively. The SPXY algorithm was used to divide the training set and validation set in a 3:1 ratio. The original spectrum was preprocessed using moving window smoothing (MWS), standard normalized variate (SNV), multiplicative scatter correction (MSC), and first derivative (FD) algorithms. The competitive adaptive reweighted sampling (CARS) algorithm and the corresponding chemical characteristic bands of NR were used to screen the bands. Partial least squares (PLS), random forest (RF), Lightweight gradient augmentation machine (LightGBM), and convolutional neural network (CNN) algorithms were employed to establish a model using the optimal spectral processing method for three different bands: full band, CARS algorithm, and chemical characteristic bands corresponding to NR. The model with the best predictive performance for high rubber content intervals (rubber content > 15%) was identified. RESULT: The results indicated that the optimal rubber content prediction models for TKS root segments and powder samples were MWS-FD CASR-RF and MWS-FD chemical characteristic band RF, respectively. Their respective R P 2 , RMSEP, and RPDP values were 0.951, 0.979, 1.814, 1.133, 4.498, and 6.845. In the high rubber content range, the model based on the LightGBM algorithm had the best prediction performance, with the RMSEP of the root segments and powder samples being 0.752 and 0.918, respectively. CONCLUSIONS: This research indicates that dried TKS root powder samples are more appropriate for constructing a rubber content prediction model than segmented samples, and the predictive capability of root powder samples is superior to that of root segmented samples. Especially in the elevated rubber content range, the model formulated using the LightGBM algorithm has superior predictive performance, which could offer a theoretical basis for the rapid detection technology of TKS content in the future.
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Bacteriocinogenic Lactobacillus fermentum BZ532 with novel bacteriocin LF-BZ532 was originally isolated from Chinese cereal fermented drink, showing an antimicrobial characteristic during fermentation. This study aimed to explore the in situ antimicrobial activities of L. fermentum BZ532 and co-culturing investigation against key food pathogens, i.e., Staphylococcus aureus and Escherichia coli K-12, was conducted during fresh bozai production. The growth of spoilage bacteria was suppressed and bacterial count was reduced to a significantly low level during 48 h of co-cultures. In situ production of antimicrobial compounds expressed positive activity against S. aureus and E. coli K-12, but negative acitivity against Salmonella sp. D104. The total viable count of bozai BZ-Lf (bozai fermented with BZ532 strain) had a comparatively lower viable count than bozai BZ-C (bozai as an experimental control without BZ532) during storage of 7 days. Titratable acidity of bozai treatments (BZ-C, BZ-Lf) was increased, while pH declined accordingly during storage of 7 days. The organoleptic quality of bozai BZ-C had low sensorial scores as compared with BZ-Lf during storage. In comparison with naturally fermented bozai (BZ-C), L. fermentum BZ532 (BZ-Lf) could significantly reduce the microbial spoilage and extend the shelf-life based on microbiological examination. Conclusively, L. fermentum BZ532 can be used as a bio-protective culture for improving the safety of bozai.
RESUMO
ETHNOPHARMACOLOGICAL RELEVANCE: The beneficial effects of bee pollen on prostate diseases are well known. Clinicians confirm that, in nonbacterial prostate diseases, bee pollen improves the condition of patients effectively. However, there is insufficient evidence to rate effectiveness of bee pollen on prostate cancer. AIM OF THE STUDY: High hydrostatic pressure (HHP), an effective non-thermal technique to improve the nutritional quality and bio-functionality of plant-based foods, was used to increase the anti-proliferative properties of Lotus (Nelumbo nucifera) bee pollen (LBP) in prostate cancer PC-3â¯cells via enhancement of bioactive compounds. MATERIALS AND METHODS: Freeze-dried lotus bee pollen produced from Fu Zhou city, Jiangxi province, China, was processed by high hydrostatic pressure (HHP). The anti-proliferative activities, apoptosis of ethanol and methanol extracts in prostate cancer PC-3â¯cells was evaluated using MTT method and Annexin-V/PI cell apoptosis assay kit, respectively. The changes of metabolites were determined using UPLC-Triple-TOF-MS analysis platform. RESULTS: HHP treatment enhanced anti-proliferative activities, cell apoptosis, cell cycle disruption, glutathione-depletion in prostate cancer PC-3â¯cells. The metabolomics analysis showed that some metabolites such as chaetoglobosin A, glutathione oxidized, cyanidin 3-rutinoside, brassicoside, sophoranone, curcumin II, soyasaponin II were significantly increased (pâ¯<â¯0.05) after the HHP treatment, PCA results shown that these bioactive components have quite correlation with anti-proliferative activities of lotus bee pollen on the PC-3â¯cells. The results indicated that HHP enhances the anti-prostate cancer activity of lotus bee pollen via increased metabolites.
