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[Advances in Food and Nutrition Research] || Advanced Analysis of Roots and Tubers by Hyperspectral Techniques
摘要： Hyperspectral techniques in terms of spectroscopy and hyperspectral imaging have become reliable analytical tools to effectively describe quality attributes of roots and tubers (such as potato, sweet potato, cassava, yam, taro, and sugar beet). In addition to the ability for obtaining rapid information about food external or internal defects including sprout, bruise, and hollow heart, and identifying different grades of food quality, such techniques have also been implemented to determine physical properties (such as color, texture, and specific gravity) and chemical constituents (such as protein, vitamins, and carotenoids) in root and tuber products with avoidance of extensive sample preparation. Developments of related quality evaluation systems based on hyperspectral data that determine food quality parameters would bring about economic and technical values to the food industry. Consequently, a comprehensive review of hyperspectral literature is carried out in this chapter. The spectral data acquired, the multivariate statistical methods used, and the main breakthroughs of recent studies on quality determinations of root and tuber products are discussed and summarized. The conclusion elaborates the promise of how hyperspectral techniques can be applied for non-invasive and rapid evaluations of tuber quality properties.
关键词： Gradation,Physical properties,Chemometric analyses,Multivariate statistics,Hyperspectral imaging,Chemical constituents,Authentication,Vibrational spectroscopy,Potato tuber,Food quality
更新于2025-09-19 17:15:36
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Spatially resolved polymer classification using laser induced breakdown spectroscopy (LIBS) and multivariate statistics
摘要： Synthetic polymers and plastics have become one of the most important materials in our modern world and everyday life with all kinds of applications mainly due to their wide range of excellent and tuneable properties. Several novel materials consisting of multiple different synthetic polymers or composite materials like natural-fiber-reinforced polymer composites have already been reported in literature. Additionally, materials consisting of multiple synthetic polymers already found their way in our daily lives (e.g. double-sided adhesive tape). With emerging materials consisting of different structured synthetic polymers, the need for analytical methods characterizing these kinds of sample also arises. Conventionally, analytical techniques such as FT-IR or Raman spectroscopy are used for polymer classification. Although, these techniques offer laterally resolved investigations they lack the possibility of analyzing depth profiles. In this work, we present laser induced breakdown spectroscopy (LIBS) as a novel and powerful analytical method for spatially resolved polymer classification. As a feasibility study, two exemplary structured synthetic polymer samples (2D structured and multilayer system) have been analyzed using LIBS and the spatial distribution of 5 different synthetic polymers, namely acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene (PE), polyacrylate (PAK) and polyvinylchloride (PVC) have been successfully classified using multivariate statistical approaches (principal component analysis (PCA) and k-means clustering). Spatially resolved classification results were validated by comparing the obtained distribution of the 2D structured sample to the elemental distribution of a contamination present in one of the synthetic polymers. Classification of the polymeric multilayer system was validated by comparing the obtained results to a microscopic cross-section. It was shown that LIBS cannot only be used to investigate 2D structured polymer samples but also for direct analysis of depth profiles. Besides synthetic polymer classification, LIBS provides simultaneous analysis of the elemental composition of the sample, which increases the total amount of information accessible with only one measurement.
关键词： Polymer classification,Depth profiling,Spatially resolved analysis,Multivariate statistics,Laser induced breakdown spectroscopy (LIBS)
更新于2025-09-11 14:15:04