Nondestructive identification and classification of starch types based on multispectral techniques coupled with chemometrics

Starch is the main source of energy and nutrition. Therefore, some merchants often illegally add cheaper starches to other types of starches or package cheaper starches as higher priced starches to raise the price. In this study, 159 samples of commercially available wheat starch, potato starch, corn starch and sweet potato starch were selected for the identification and classification based on multispectral techniques, including near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy combined with chemometrics, including pretreatment methods, characteristic wavelength selection methods and classification algorithms. The results indicate that all three spectral techniques can be used to discriminate starch types. The Raman spectroscopy demonstrated superior performance compared to that of NIR and MIR spectroscopy. The accuracy of the models after characteristic wavelength selection is generally superior to that of the full spectrum, and two-dimensional correlation spectroscopy (2D-COS) achieves better model performance than other wavelength selection methods. Among the four classification methods, convolutional neural network (CNN) exhibited the best prediction performance, achieving accuracies of 99.74 %, 97.57 % and 98.65 % in NIR, MIR and Raman spectra, respectively, without pretreatment or characteristic wavelength selection.

Molecular mechanisms of polystyrene nanoplastics and alpha-amylase interactions and their binding model: A multidimensional analysis

Plastic fragments are widely distributed in different environmental media and has recently drawn special attention due to its difficulty in degradation and serious health and environmental problems. Among, nanoplastics (NPs) are smaller in size, larger in surface/volume ratio, and more likely to easily adsorb ambient pollutants than macro plastic particles. Moreover, NPs can be easily absorbed by wide variety of organisms and accumulate in multiple tissues/organs and cells, thus posing a more serious threat to living organisms. Alpha-amylase (α-amylase) is a hydrolase, which can be derived from various sources such as animals, plants, and microorganisms. Currently, no studies have concentrated on the binding of NPs with α-amylase and their interaction mechanisms by employing a multidimensional strategy. Hence, we explored the interaction mechanisms of polystyrene nanoplastics (PS-NPs) with α-amylase by means of multispectral analysis, in vitro enzymatic activity analysis, and molecular simulation techniques under in vitro conditions. The findings showed that PS-NPs had the capability to bind with the intrinsic fluorescence chromophores, leading to fluorescence changes of these specific amino acids. This interaction also caused the alterations in the micro-environment of the fluorophore residues mainly tryptophan (TRP) and tyrosine (TYR) residues of α-amylase. PS-NPs interaction promoted the unfolding and partial expansion of polypeptide chains and the loosening of protein skeletons, and destroyed the secondary structure (increased random coil contents and decreased α-helical contents) of this protein, forming a larger particle size of the PS-NPs-α-amylase complex. Moreover, the enzymatic activity of α-amylase in vitro was found to be inhibited in a concentration dependent manner, thereby impairing its physiological functions. Further molecular simulation found that PS-NPs had a higher tendency to bind to the active site of α-amylase, which is the cause for its structural and functional changes. Additionally, the hydrophobic force played a major role in mediating the binding interactions between PS-NPs and α-amylase. Taken together, our study indicated that PS-NPs interaction can initiate the abnormal physiological functions of α-amylase through PS-NPs-induced structural and conformational alternations.