Yan Jun,Huang Xiao-Ping,Wu Ye-Yu,Du Fang-Kai,Tan Xue-Cai,Wang Qi,Zhu Wei-Wei
Abstract Near-infrared (NIR) diffuse reflectance spectroscopy has become an attractive technique for quality assurance of fruits, drugs, vegetables, and foods. In this work, the information on chemical composition of Siraitia grosvenorii was acquired by NIR, and then a calibration model for the prediction of total sugar content (TSC) was developed using chemometric techniques. One hundred and fifty samples were used as calibration set to develop model, while 40 unknown samples were used for prediction, using phenol-concentrated sulphuric acid assay as reference value. Several spectra preprocessing methods and wavelength selection techniques combined with partial least squares regression were compared, and then standard normal variate coupled with Savitzky–Golay derivative was selected for spectra processing and competitive adaptive reweighted sampling method was applied to extract the informative wavelengths related to the prediction of TSC. The performance of the obtained model has been evaluated by external validation, the square of correlation coefficient (R 2 ) are 0.9097 for a separate blind set consists of 40 unknown samples. Additionally, the result of paired t test ( p = 0.3608) illustrated that there is no significant differences between NIR and wet chemistry analysis. These results demonstrated that NIR spectroscopy has the potential to be a time saving and cost-effective method for the determination of the TSC in S. grosvenorii . Graphic abstract
The identification of cancer cells and tumors significantly advances the accurate diagnosis of cancer and improves the patient survivor rates. However, it remains challenging to achieve the rapid and label-free differentiation of cancer cells from their normal counterparts. Herein, we developed a rapid and effective method for the synthesis of Ag nanorods (NRs) with high uniformity and yield. The surface-enhanced Raman scattering (SERS) substrates demonstrated uniformity and reliability, with a low relative standard deviation (RSD) of 10% and an enhancement factor (EF) of 3.32 × 105. The optimized AgNR SERS substrates were then applied in the quantitative analysis of creatinine and furthermore the bioanalysis of cell lysates for cell classification. Specifically, a high sensitivity with a low detection limit (LOD) of 5.3 μM and a limit of quantification (LOQ) of 17.68 μM was achieved for the detection of creatinine. More importantly, label-free SERS analysis showed the distinct spectral features of the cell lysates for normal breast cell strains (MCF-10A) and breast cancer cell strains (MCF-7, MDA-MB-231). Statistical analysis, such as t-test, principal component analysis (PCA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) aided in the clear discrimination between the normal and cancer groups and further in differentiating the two cancer groups. These results demonstrate the great potential of the combination of a high-quality AgNR SERS substrate and statistical analysis for the identification of cancers.
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