Small berry fruit products are gaining an expanded market due to their high nutrition value. However, the authenticity of products is challenged by adulteration and mislabeling. To establish an accurate and robust method for identifying both known and unknown fruit species in small berry fruit products, DNA barcoding technology based on Sanger sequencing was adopted. To overcome the influence of processing conditions on DNA recovery, mini-barcodes of rbcL and ITS and a medium-barcode of psbA-trnH were applied. To identify ingredients in products containing mixed species, plasmid cloning was applied to separate mixed barcodes. The method established in this paper could detect 1% to 10% target species in mixed fruit juice.

The quality of honey is significantly influenced by floral origin. Mislabeling floral species occurs frequently in bee honey products. To protect consumers from economic fraud and maintain a fair market environment, methods to identify floral species in honey are necessary. In our study, real-time PCRs were established, targeting six honey types mainly produced in China (canola, Chinese milkvetch, Chinese chaste tree, locust tree, litchi, and longan). Sensitivity testing on DNA from plant tissues exhibited LODs of about 0.5-5 pg/μL. For DNA extracts of pollen sediments from different honey species, LODs ranged from 13.6 to 403.2 pg/μL. In an experiment to determine the practical LODs of honey in which adulterant honey was spiked in the genuine honey, adulterant honey as low as about 0.1-0.5% was detected in 90-100% in 10 parallel tests. Additionally, pollen was spiked in the honey and stored under various conditions to investigate the migration of pollen DNA into the honey supernatant. Finally, the efficiency of our method was investigated by testing honey samples of unknown compositions from different geographic regions. Of the 159 honey samples that were supposed to be monofloral that had been collected in five provinces, a small portion were found to be contaminated with foreign pollen (7%). The methods proved to be specific, sensitive, and reliable in identifying the six plant species in honey, which would be a useful tool during the market supervision and QC of honey products.

Background: Public interest is growing for small berries in recent years because they are very delicious, low in energy, and full of bioactive compounds with potential health benefits. Similar to other food products, adulteration of small berry fruit products poses economic and safety problems to consumers. Objective: To protect consumers and regulate the small berry fruit products market, it is necessary to establish a robust method for detecting the authenticity. Methods: In this study, TaqMan-based real-time PCR assay was established for species identification of cranberry, raspberry, and blueberry to ensure authenticity of commercial small berry food products (pulp, dried fruit, fruit juice, jam, and puree). Results: Absolute detection limit was 0.1 pg/μL DNA for raspberries, 1 pg/μL DNA for blueberries, and 10 pg/μL DNA for cranberries. Practical LOD was 0.1% (v/v) for fresh juice. For processed juice, practical LOD was 1% for blueberry and red raspberries, 0.1% for black and yellow raspberries, and 5% for cranberry. Conclusions: The method was shown to be functional and effective to detect the raw material composition of cranberry, raspberry, and blueberry for commercial products. Highlights: TaqMan probe-based real-time PCR methods were designed to identify three small berries (blueberry, raspberry, and cranberry) in berry products. Efficient DNA recovery methods and detection strategy were established to ensure correct and sensitive testing of fresh small berries exhibited a detection limit of 0.1 to 10 pg/μL. The practical minimum detection levels were 0.1 to 5% in fresh and processed juice, including pasteurization and HTHP.

No abstract

Objectives For NPC patients, distant metastasis is now the main reason for treatment failure. The patients with distinct metastases need different therapeutic regimen and have distinct prognosis. Radiomics might help us predict the type of metastases effectively. Methods The MRI data of seventy non-metastatic NPC patients who develop distant metastasis within five years after treatment were collected and 4410 radiomics features for each patient were extracted by PyRadiomics. Every radiomics feature were compared among patients with distinct metastases and tested by the receiver operating characteristic curve. Results Twenty features have significant differences between the bone metastases cohort and lung metastases cohort, two features have significant differences between the bone metastases cohort and liver metastases cohort, one feature has significant differences between the bone metastases cohort and multiple metastases cohort and sixty-seven features have significant differences between the lung metastases cohort and liver metastases cohort. Six T2WI features could identified lung metastases from bone metastases and liver metastases effectively (AUC = 0.851 ~ 0.896), two T1WI features and one CE T1WI feature could respectively identified bone metastases from liver metastases and multiple metastases effectively (AUC = 0.779 ~ 0.821). Conclusion The results indicate that the radionics features could reflect some of the characteristics of distinct metastases and have potential to be used as predictor of distinct metastases.