Himmelsbach David S, Hellgeth John W, McAlister David D
Quality Assessment Research Unit, Richard B. Russell Research Center, Agricultural Research Service, US Department of Agriculture, PO Box 5677, Athens, Georgia 30605-5677, USA.
J Agric Food Chem. 2006 Oct 4;54(20):7405-12. doi: 10.1021/jf052949g.
The presence of foreign matter in cotton seriously affects the cotton grade and thus the price per bale paid by the spinner to the grower, the efficiency of the spinning and ginning operations, and the quality of the final woven product. Rapid identification of the nature of the extraneous matter in cotton at each stage of cleaning and processing is necessary to permit actions to eliminate or reduce its presence and improve efficiency and quality. Although several instruments are being successfully employed for the measurement of contamination in cotton fibers based on particle size/weight, no commercial instrument is capable of accurate qualitative identification of contaminants. To this end, ATR/FT-IR spectra of retrieved foreign matter were collected and subsequently rapidly matched to an authentic spectrum in a spectral database. The database includes contaminants typically classified as "trash", cotton plant parts (hull, shale, seed-coat fragments, bract, cacyx, leaf, bark, sticks, and stems) and grass plant parts (leaf and stem); "foreign objects and materials", synthetic materials (plastic bags, film, rubber, bale wrapping and strapping); organic materials (other fibers, yarns, paper, feathers, and leather); plus entomological and physiological sugars and inorganic materials (sand and rust). The spectral matching resulted in consistently high-score identification of the foreign matter based on chemical composition, irrespective of its particle size. The method is envisioned to be employed with stand-alone rugged infrared instrumentation to provide specific identification of extraneous materials in cotton as opposed to only general classification of the type by particle size or shape.
棉花中存在杂质会严重影响棉花等级,进而影响纺纱厂向种植者支付的每包棉花价格、纺纱和轧花操作的效率以及最终织物产品的质量。在棉花清洁和加工的每个阶段快速识别杂质的性质,对于采取行动消除或减少其存在并提高效率和质量是必要的。尽管有几种仪器已成功用于基于颗粒大小/重量测量棉花纤维中的污染物,但尚无商业仪器能够准确地对污染物进行定性识别。为此,收集了回收的杂质的衰减全反射/傅里叶变换红外光谱(ATR/FT-IR),随后将其与光谱数据库中的真实光谱进行快速匹配。该数据库包括通常归类为“杂质”的污染物、棉花植株部分(棉籽壳、页岩、种皮碎片、苞片、花萼、叶子、树皮、枝条和茎)以及草类植株部分(叶子和茎);“异物和材料”,合成材料(塑料袋、薄膜、橡胶、棉包包装带);有机材料(其他纤维、纱线、纸张、羽毛和皮革);以及昆虫学和生理学糖类和无机材料(沙子和铁锈)。光谱匹配能够基于化学成分一致地对杂质进行高分识别识别,而不论其颗粒大小如何。设想该方法将与独立的坚固型红外仪器一起使用,以提供对棉花中杂质材料的具体识别,而不仅仅是按颗粒大小或形状对类型进行一般分类。