Continuous in-line decontamination of food-processing surfaces using cold atmospheric pressure air plasma

This study assessed a continuous in-line decontamination system for food contact surfaces and processing equipment that utilized cold atmospheric pressure plasma (CAP) generated from ambient air. The plasma system was evaluated against two common foodborne pathogens (Salmonella Typhimurium, Listeria monocytogenes) on stainless steel surfaces and against S Typhimurium on commercial poly[ether]-thermoplastic poly[urethane] (PE-TPU) conveyor belts, under simulated conditions of a food-processing facility. A significant level of microbial inactivation was achieved, up to 3.03 ± 0.18 and 2.77 ± 0.71 logCFU/mL reductions of L. monocytogenes and S. Typhimurium respectively within 10 s total treatment on stainless steel surfaces, and a 2.56 ± 0.37 logCFU/mL reduction of S. Typhimurium within 4 s total treatment on the PE-TPU material, according to a procedure based on the well-established EN 13697:2015 industrial protocol. CAP exposure was shown to have a minor impact on the morphology and composition of the treated surfaces. The results indicated that CAP can be applied for effective and continuous disinfection against common foodborne pathogens in food-processing facilities.Industrial relevanceLow temperature plasmas have shown great promise for microbial decontamination, yet industrial uptake of the technology has been limited due to scaling limitations. In this study, a prototype conveyor-based CAP decontamination system was developed and tested under realistic conditions expected within a food-processing facility. The results showed a high level of antimicrobial action against two common foodborne pathogens within a few seconds of CAP exposure, a timescale in line with industrial line processing speeds. Our findings demonstrated that CAP shows great promise for the continuous in-situ decontamination of food contact surfaces, with the potential to mitigate against the costly downtimes incurred in current production line practices implementing chemical disinfectants.     

Characterisation of volatile compounds of farmed soft‐shelled turtle (Pelodiscus sinensis) by solid‐phase microextraction and the influence of matrix pH on the release of volatiles

Although farming and consuming of soft‐shelled turtle has been practised for centuries, the aroma‐impact compounds in the meat have not been determined. Furthermore, matrix pH, usually changing during processing and storage stage, was rarely investigated for the influence on volatile profiles. To these aims, soft‐shelled turtle meat was subjected to different pH conditions and the potential volatile compounds were analysed under different extraction conditions by headspace solid‐phase microextraction (HS‐SPME). A total of forty‐three volatiles were identified; in which, nonanal, (E, E)‐2, 4‐heptadienal, octanal, decanal, hexanal, (E)‐2‐nonenal, heptanal, 1‐octen‐3‐ol and o‐xylene were assigned as aroma‐impact compounds by high relative odour activity value (ROAV). Overly basic or acidic pH significantly (P < 0.05) facilitated the release of volatiles under moderate extraction conditions. The total volatilisation increased by 30.1% to 298% in pH‐shifted samples, while the highest one was found in pH 2 or pH 11. However, the results of principal components analysis (PCA) demonstrated that increased extraction time or temperature hindered the pH enhancement.     

Identification of Broken Rice Kernels Using Image Analysis Techniques Combined with Velocity Representation Method

The percentage of whole kernels remaining after milling is one of the most important physical characteristics of rice quality. A method based on flatbed scanning and image analysis was developed for the identification of broken rice kernels. Velocity representation method was developed for pattern recognition based on the contour characteristics of the rice kernels. The similarity of the boundary features of the image of rice kernel was measured by similarity coefficient, which was used to identify the broken rice kernel by comparing with threshold. High recognition rates for three rice varieties were reached by this method with 96.7% for Thailand rice, 98.73% for Pearl rice, and 97.14% for Changlixiang rice, respectively, and the recognition rate could be improved by the adjustment of the similarity coefficient threshold. Because the comprehensive boundary features were the basis for the classification, this method could be more accurate compared to other methods using the single dimension feature.     

The synthesis and catalytic activity to Li/SOCl2 battery of two new porphyrins

Two new porphyrins (3a, 3b) were synthesized and characterized by IR, UV–vis, ¹H NMR, MS and elementary analysis. The catalytic activity of the synthesized porphyrins to lithium/thionyl chloride (Li/SOCl₂) battery is evaluated by the relative energy of the battery whose electrolyte contains the porphyrins. The results indicate that the energy of Li/SOCl₂ battery catalyzed by porphyrins 3a and 3b is 101, 37% higher, respectively, than that of Li/SOCl₂ battery in the absence of the porphyrins. It can be used as a basis for the synthesis of more porphyrins with improved catalytic activity to Li/SOCl₂ battery in the future.     

Non-destructive evaluation of apple maturity using an electronic nose system

Currently, random and destructive sampling techniques are used to evaluate apple quality. Thus there is need to develop a non-destructive technique to assess apple quality. Maturity indices such as starch index and puncture strength were used to categorize Gala apples into three maturity groups referred to as immature, mature and over mature fruits. Multivariate analysis of variance (MANOVA) of the electronic nose (EN) sensor data indicated that there were different maturity groups (Wilks’ Lambda F = 3.7, P < 0.0001). From the Discriminant analysis (DA), EN could effectively categorize Gala apples into the three maturity groups with the correct classification percentage of 83%.     

传统酸面团中细菌与酵母菌的分离与鉴定

为进一步描述我国传统面食发酵剂的理化性质和菌落组成,收集了北方地区6份发酵剂样品,测定了其酸度和菌落总数,并对分离、纯化、初筛后得到的75株细菌和60株酵母菌进行了测序鉴定。结果显示,样品pH值范围为3.73~5.46,总滴定酸度为8.3~19.8 mL;乳酸菌和酵母菌的计数结果分别为8.35±0.07~9.75±0.12 Log cfu/g,6.31±0.22~8.68±0.04 Log cfu/g。鉴定出包括短乳杆菌(Lactobacillus brevis)、植物乳杆菌(Lactobacillus plantarum)和旧金山乳杆菌(Lactobacillus sanfranciscensis)在内的乳酸菌8种;酵母菌4种,其中优势菌为酿酒酵母(Saccharomyces cerevisiae);其他细菌6种,主要为解淀粉芽孢杆菌(B.amyloliquefaciens)、地衣芽孢杆菌(B.licheniformis)和醋杆菌。结果表明,我国传统面食发酵剂菌相复杂,以酵母菌和乳酸菌为主,还包括芽孢杆菌、醋酸杆菌在内的多种其他细菌,甚至可能含有致病菌。通过比较细菌和酵母在不同酸面团样品中的分布,发现不同来源样品的微生物种类组成存在差异。     

鸡腿菇子实体蛋白提取工艺优化及其特性研究

以鸡腿菇子实体为原料,研究其蛋白的提取工艺及特性。以提取液pH值、提取温度、料液比、提取时间为提取鸡腿菇子实体蛋白的影响因素,采用正交试验优化碱溶酸沉法提取蛋白的工艺条件,结果表明:鸡腿菇子实体蛋白的最佳提取工艺参数为提取液pH11.5,提取温度50℃,料液比1:15,提取时间50min,此时蛋白提取率为24.3%。在pH3.5条件下沉淀蛋白效果最佳。对制备的鸡腿菇子实体蛋白特性的分析表明:其分子质量主要集中在50～90ku,20～35ku和13～17ku等区间内;必需氨基酸(EAA)占氨基酸总量(TAA)的40.57%,必需氨基酸与非必需氨基酸(NEAA)的比值为0.68,为理想蛋白质,且其所含人体必需氨基酸种类齐全,比例均衡,比值系数分(SRC)为80.69;在碱性条件下,鸡腿菇子实体蛋白具有良好的溶解性、乳化性和起泡能力。     

Application of hybrid image features for fast and non-invasive classification of raisin

A new approach for the non-invasive classification of raisins is presented based on the hybrid image features, namely morphological, color and texture features. A total of 74 features (8 morphological, 30 color, and 36 textural) were extracted from RBG images. Seven kinds of models were established based on different feature sets. They were three kinds of models established based on single feature set, three kinds of models established based on the combination of two feature sets, and one kind of model established based on the combination of all feature sets. Five kinds of classifiers, namely partial least squares (PLS), linear discriminant analysis (LDA), soft independent modeling of class analogy (SIMCA), and least squares support vector machine (LS-SVM) with linear and radial basis function (RBF) kernels were used for the model establishment based on different feature sets. The best correct answer rates (CAR) of 99% was obtained when LDA was used to establish the classification model based on the combination of all feature sets, which was higher than those of the models established based on single feature set or the combination of two feature sets. The results show that the feature combination is helpful to improve the accuracy of raisin classification. It was concluded that the varieties of raisin could be accurately classified based on RGB image features and the combination of morphological, color and texture features was an accurate way to improve the accuracy of classification.