In recent years, the development of probiotic film by incorporating probiotics into edible polymers has attracted significant research attention in the field of active packaging. However, the influence of the external environment substantially reduces the vitality of probiotics, limiting their application. Therefore, to improve the probiotic activity, this study devised a novel nanofiber film incorporating chia mucilage protection solution (CPS), gum arabic (GA), pullulan (PUL), and Lactobacillus bulgaricus (LB). SEM images indicated the successful preparation of the nanofiber film incorporating LB. CPS incorporation significantly improved the survival ability of LB, with a live cell count reaching 7.62 log CFU/g after 28 days of storage at 4 °C — an increase of 1 log CFU/g compared to the fiber film without CPS. The results showed that the fiber film containing LB inhibited Escherichia coli and Staphylococcus aureus. Finally, the novel probiotic nanofiber film was applied to beef. The results showed that the shelf life of the beef during the experiments was extended for 2 days at 4 °C. Therefore, the novel probiotic film containing LB was suitable for meat preservation.

In this study, a bilayer film (BIF) was fabricated to improve the stability of an anthocyanin-based freshness indicator film. The sensor layer consists of gellan gum (GG) and mulberry anthocyanin (MAE) for freshness indication. The oxygen barrier layer was constructed from chitosan (CS), polyvinyl alcohol (PVA), sodium alginate (SA), and pullulan (Pu) to the protection of MAE from oxidation. The highest antioxidant activity of BIF was 91.28 %. BIF was used to monitor the Chinese mitten crab freshness. The total volatile basic nitrogen (TVBN) level was increased to 31.23 mg/100 g on day 8, and the color of the indicator presented a visible change from pink to dark green. The acquired results revealed a good correlation between TVB-N, pH, and color change of the indicator. The research indicated that the BIF was applied for freshness monitoring of Chinese mitten crab and displayed significant color changes that would be effective in commercial environments.

In order to investigate the sensitivity of anthocyanins from different plant origin as indicators for salmon freshness, nine plant anthocyanins were extracted and fabricated into colorimetric sensor arrays to detect NH3, trimethylamine (TMA), dimethylamine (DMA) to indicate salmon freshness. Rosella anthocyanin had the highest sensitivity for amines, ammonia and salmon. HPLC-MSS analysis indicated that Delphinidin-3 glucoside accounted for 75.48 % of the Rosella anthocyanin. UV-visible spectral analysis showed that the maximum absorbance band of Roselle anthocyanins for acid and alkaline forms were located at 525 nm and 625 nm which showed a relatively broader spectrum than other anthocyanins. An indicator film was fabricated by combining Roselle anthocyanin with agar and polyvinyl alcohol (PVA), which showed visible changes from red to green when employed to monitor the freshness of salmon stored at 4 degrees C. The Delta E value of Roselle anthocyanin indicator film was changed from 5.94 to >10. The Delta E value also can predict the chemical quality indicators of salmon effectively, especially with characteristic volatile components, and the predictive correlation coefficient was above 0.98. Therefore, the proposed indicating film showed great potential monitoring salmon freshness.

为了提高天然花青素作为新鲜度指示剂在食品包装中的稳定性,本研究将结冷胶和桑葚花青素作为内层膜用于指示三文鱼新鲜度,壳聚糖-聚乙烯醇形成致密隔氧的外层保护膜,通过逐层组装制备食品新鲜度指示膜,并用于三文鱼的新鲜度可视化检测.结果 表明:当外层膜中壳聚糖和聚乙烯醇的体积比为1∶1时,双层膜的机械性能最好,其中拉伸强度为40.65 MPa,断裂伸长率为54.36％,膜的水蒸气透过系数最低;扫描电子显微镜结果表明,外层膜壳聚糖和聚乙烯醇的体积比为1∶1时,膜的结构相容性最好,该体系下桑葚花青素稳定性最好.将双层膜用于三文鱼新鲜度指示,在4℃贮藏环境下,随着贮藏时间的延长,三文鱼的总挥发性盐基氮含量不断上升,6d后,总挥发性盐基氮含量大于30 mg/100 g,硫代巴比妥酸反应产物值为0.921 mg/kg,pH值为6.75,且通过肉眼可以直观看到膜的颜色由暗红变为淡紫色,最后变成蓝褐色,鱼肉已经腐败.结论:基于壳聚糖-聚乙烯醇/结冷胶花青素组成的双层膜既可降低花青素氧化作用提高其稳定性,又可用于三文鱼新鲜度指示.

Starch food is easy to retrograde during processing,transportation and storage,and the degree of retrogradation seriously affects the nutritional value and shelf-life of starch food.Soretrogradation degree is really expected to determine rapidly and non-destructively during storage,that is near-infrared and mid-infrared spectroscopy.The near-infrared and mid-infrared spectra of starch in different storage times (0 d,1 d,2 d,3 d,4 d,5 d,10 d,15 d and 20 d) were collected.There was a certain associations between spectra data and chemical reference detected by spectrophotometry,then chemometrics (partial least squares,PLS) were used to establish the prediction model of starch retrogradation with near-infrared,mid-infrared and fusion data,the best one that had higher correlation coefficient and lower error was chosen.The results showed that the backward interval partial least squares (biPLS) prediction model of fusion technology was the best one,the root mean square error of crossvalidation (RMSECV) and root mean square error of prediction (RMSEP) were 6.79％ and 9.52％,and the calibration and prediction correlation coefficient were 0.965 5 and O.931 3,respectively.The results indicated that the fusion spectroscopy was superior to any single spectral technique,which could provide more accurately information of starch.Hence,the infrared spectroscopy could detect the retrogradation degree of corn starch rapidly and non-destructively,provide guidance for the processing of starchy food,and ensure the quality and safety of starchy food.

Retrogradation behavior is an important physicochemical property of starch during storage. A fast and sensitive method was developed for determining the retrogradation degree (RD) in corn starch by mid-infrared (MIR), Raman spectroscopy, and combination of MIR and Raman. MIR and Raman spectra were collected from different retrogradation starch and then processed by partial least squares (PLS), interval PLS (iPLS), synergy interval PLS (siPLS), and backward interval PLS (biPLS). Two different levels of fusion data extracted from MIR and Raman spectra were analyzed by PLS. The developed models demonstrated that both MIR and Raman techniques combined with chemometrics can be used to determine the RD in starch. The PLS model built by medium-level fusion approach achieved the most satisfied performance with a correlation coefficient of 0.9658. Integrating MIR and Raman technique combined with chemometrics improved the prediction performance of RD in comparison with a single technique.

Objective To establish a method for determination of collagen content of sea cucumber from different geographical origins by near-infrared spectroscopy (NIR). Methods Forty-three sea cucumber samples were collected from Dalian, Fujian, Lianyungang and Shandong. Near-infrared spectra of the samples were collected and pretreated by standard normal variables (SNV). Then, the geographical origin of the sea cucumber was determined by using qualitative discriminant models based on the pretreated spectra. The collagen content of sea cucumber was determined with UV spectrophotometry. Partial least squares (PLS), interval partial least squares (iPLS), backwards interval partial least squares (BiPLS) and synergy interval partial least squares (SiPLS) were used to build quantitive prediction models for the collagen content. Results The optimal qualitative discriminant model for the geographical origins of sea cucumber was least-squares support vector machine regression (LS-SVM), and its recognition rates of calibration set and prediction set were 100% and 95.35%, respectively. The best quantitive prediction model for collagen content was BiPLS, and its calibration coefficient (Rc) and prediction coefficient (Rp) were 0.9002 and 0.8517, respectively. Conclusion Near-infrared spectroscopy can be used to rapidly determine the geographical origins and the collagen content of sea cucumber.