N/S-Co-doped carbon dot-based FRET ratiometric fluorescence aptasensing platform modulated with entropy-driven DNA amplifier for ochratoxin A detection.

This study proposes a nitrogen and sulfur co-doped carbon dot (N/S-CD)-based FRET ratiometric fluorescence aptasensing strategy modulated with entropy-driven DNA amplifier for sensitive and accurate detection of ochratoxin A (OTA). In the strategy, a duplex DNA probe containing OTA aptamer and complementary DNA (cDNA) is designed as a recognition and transformation element. Upon sensing of target OTA, the cDNA was liberated, and triggered a three-chain DNA composite-based entropy-driven DNA circuit amplification, making CuO probes anchor on a magnetic bead (MB). The CuO-encoded MB complex probe is finally turned into abundant Cu2+, which oxidizes o-phenylenediamine (oPD) to generate 2,3-diaminophenazine (DAP) with yellow fluorescence and further triggers FRET between the blue fluorescent N/S-CDs and DAP. The changes in ratiometric fluorescence are related to the OTA concentration. Originating from the synergistic amplifications from the entropy-driven DNA circuits and Cu2+ amplification, the strategy dramatically enhanced detection performance. A limit of detection as low as 0.006 pg/mL of OTA was achieved. Significantly, the aptasensor can visually evaluate the OTA via on-site visual screening. Moreover, the high-confidence quantification of the OTA in real samples with results consistent with that of the LC-MS method indicated that the proposed strategy has practical application prospects for sensitive and accurate quantification in food safety.

Prediction of banana maturity based on the sweetness and color values of different segments during ripening.

To predict the maturity of bananas, the present study used non-destructive methods to analyze changes in the sweetness and color of the stalks, middles, and tips of bananas during ripening. The results indicated that the respective maturation of these three segments did not occur simultaneously, as indicated by the differential enzyme activity and gene expression levels recorded in these segments. A principal component analysis and cluster plots were used to review the classification of banana maturity, highlighting that banana maturation can be divided into six stages. Two distinct maturity prediction algorithms were established using random forest, artificial neural network, and support vector machines, and they also indicated that dividing the maturity of bananas into six stages was adequate. These findings contribute to the development of quality evaluation and of a rapid grading system for processing, which improves the quality and sale of banana fruits and the related processed products.