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In this paper, using hyperspectral imaging technology combined with feature variable optimization algorithm, the method of rapid detection of potato starch content was explored. The spectral pretreatment uses 10 methods such as de-trend and first derivative, second derivative and so on. Through comparative analysis, it is determined that the combination of de-trend and first derivative is the best. The spectral data preprocessed by the de-trend method combined with first derivative method were used to optimize the characteristic wavelength variables by four algorithms: Competitive adaptive reweighted sampling (CARS), Random-frog (RF), Iterative reserved information variable (IRIV), and Interval combin optimization (ICO). The partial least squares regression models CARS-PLS, RF-PLS, IRIV-PLS and ICO-PLS were established one by one for the selected characteristic variables. The results show that the number of characteristic wavelength variables optimized by ICO algorithm is at least 16, the prediction accuracy of ICO-PLS model is the highest, the determinant coefficients of prediction set is 0.8255, and the error is 0.1697. ICO algorithm is a new algorithm to improve the accuracy of spectral detection prediction model. It provides theoretical methods and technical support for the study of the detection accuracy of potato starch content.

A new method of combined steam flash-explosion and alkaline treatment (SFE-AT) was developed to extract cellulose fibers from bark of cotton stalks, and lightweight composites with favorable properties were made from the obtained fibers and polypropylene (PP). It was reported that the cellulose fibers from bark of cotton stalks had higher mechanical properties than those from most lignocellulosic byproducts. However, conventional method of using strong alkaline condition (30–100 g/L NaOH) for extraction of cellulose fibers from lignocellulosic byproducts led to environmental pollution and high cost. In this study, bark of cotton stalks were treated using three methods, including alkaline treatment (AT), steam flash-explosion (SFE) and SFE-AT. The different cotton stalk fibers were blended with PP fibers and compression-molded into composites under optimized conditions (temperature of 170 °C, pressure of 3 MPa, 4 min). The results showed that PP composites reinforced by cotton stalk fibers from SFE-AT had the best mechanical properties and stability to water, attributing to the largest surface area and highest cellulose content of cotton stalk fibers by SFE-AT. SFE could separate bark of cotton stalks with high efficiency and subsequent mild AT could sufficiently remove the non-cellulose impurities by using NaOH solution with concentration as low as 5 g/L. Ultra-lightweight PP composites with the bulk density of 0.27 g/cm3 could be prepared with cotton stalk fibers by SFE-AT. Overall, the method of SFE-AT with high efficiency and low cost could be very useful in exploring industrial applications of bark of cotton stalks and other lignocellulosic byproducts.