阿塞洛米塔尔图巴朗钢铁集团火花发射光谱法优化氮的分析

近年来,钢铁行业需求量快速上升,竞争也变得更加激烈。为了得到性能可靠的产品,钢铁车间在生产工艺方面需要进一步严格控制好产品的生产规格。在这种背景下,为了严格控制好生产工艺的密封性并保证产品所期望的特性（尤其是其韧性和机械加工性方面）,精确地进行氮的分析必不可少。 在过去几年中, 经典燃烧技术（CC）已广泛应用于鉴定钢中的碳、硫和氮元素。而火花发射法因其性能改进、耗材成本（燃烧技术方面）降低以及在分析时间缩短等方面的优势,也在氮分析方面得到了应用,并且应用越来越广泛。本项研究的目的在于通过火花发射光谱仪（OES）对氮分析进行优化。研究是在巴西塞拉的阿塞洛米塔尔图巴朗钢铁集团的化学分析实验室进行,利用 DMAIC方法学（一种用于改进生产工艺质量的研究方法）^［1］,对潜在的影响因素（包括漂移、样品和周期）进行了分析,并确定和优化了影响氮分析精度的根本原因。在实施该计划后,对结果的精度（基于内标物的标准偏差）和准确性（通过与OE认证的参考物质和力可交叉核验结果进行比较）进行了评估。在本研究项目之前,通过光发射法测得的氮元素的标准偏差高达0.004 0%。阿塞洛米塔尔图巴朗钢铁集团利用光谱仪完成所有的研究计划后,试验表明,其测定标准偏差低达0.000 5%,充分展现了该技术的潜力。本研究对这一标准偏差进行了监测,频率为每周一次,监测期为3个月。结果发现,该标准偏差在0.000 3%到0.000 5%之间波动,表明稳定性较好。由此可见,火花发射光谱技术是氮分析的一种较有前途的方法。     

Short communication: Identification of Corynebacterium bovis by MALDI-mass spectrometry

Corynebacterium bovis is a mastitis-causing microorganism responsible for economic losses related to decrease in milk production. The aim of the study was identify Corynebacterium spp. strains recovered from milk samples of subclinical mastitis by using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Samples were collected during a 10-mo mastitis-monitoring program in a high-production dairy farm. In this study, 80 strains were analyzed; from these 54 (67.5%) were identified at species level as Corynebacterium bovis, 24 (31.2%) isolates were identified at the genus level as Corynebacterium spp., and only 1 (1.35%) isolated had unreliable identification. Results demonstrated that MALDI-MS could be an important technique for the identification of Corynebacterium spp. in milk.     

Effect of Aqueous Extract of the Seaweed Gracilaria domingensis on the Physicochemical,Microbiological, and Textural Features of Fermented Milks

The effects of the Gracilaria domingensis seaweed aqueous extract in comparison with gelatin on the physicochemical, microbial, and textural characteristics of fermented milks processed with the mixed culture SAB 440 A, composed of Streptococcus thermophilus, Lactobacillus acidophilus, and Bifidobacterium animalis ssp. lactis, were investigated. The addition of G. domingensis aqueous extract did not affect pH, titratable acidity, and microbial viability of fermented milks when compared with the control (with no texture modifier) and the products with added gelatin. Fermented milk with added the seaweed aqueous extract showed firmness, consistency, cohesiveness, and viscosity index at least 10% higher than those observed for the control product (P < 0.05). At 4 h of fermentation, the fermented milks with only G. domingensis extract showed a texture comparable to that observed for products containing only gelatin. At 5 h of fermentation, firmness and consistency increased significantly (P < 0.05) in products with only seaweed extract added, a behavior not observed in products with the full amount of gelatin, probably due to the differences between the interactions of these ingredients with casein during the development of the gel network throughout the acidification of milk. The G. domingensis aqueous extract appears as a promising gelatin alternative to be used as texture modifier in fermented milks and related dairy products.     

UV-Visible Spectroscopy and Multivariate Classification as a Screening Tool for Determining the Adulteration of Sauces

A rapid, simple, and economic multivariate screening methodology based on UV-visible spectroscopy and multivariate classification is proposed to test for adulteration in sauces. Two classification strategies were evaluated to compare their ability to detect food fraud: untargeted modeling (class modeling) and targeted classification (discriminant analysis). As a case study, the possible adulteration of ketchups and barbecue sauces with the banned Sudan I dye was considered. The classification models were built with a new classification tool for class modeling (partial least squares-density modeling, PLS-DM) and with the classical discriminant partial least squares-discriminant analysis (PLS-DA). Very satisfactory classification results were obtained with both strategies: regarding untargeted modeling, only original samples (class 1) were modeled obtaining a 94.5 % of correct classification and regarding targeted classification, two classes were considered (class 1 original samples and class 2 adulterated samples) with an overall classification rate of 97.3 %. The two strategies are useful and adequate as screening tools for monitoring the quality of sauces especially in situations that require quick responses.