Protein-polysaccharide based double network microbeads improves stability of Bifidobacterium infantis ATCC 15697 in a gastro-Intestinal tract model (TIM-1).

Microencapsulation of probiotics is a main technique employed to improve cell survival in gastrointestinal tract (GIT). The present study investigated the impact of utilizing proteins i.e. Whey Protein Isolates (WPI), Pea Protein Isolates (PPI) or (WPI + PPI) complex based microbeads as encapsulating agents on the encapsulation efficiency (EE), diameter, morphology along with the survival and viability of Bifidobacterium infantis ATCC 15697. Results revealed that WPI + PPI combination had the highest EE% of the probiotics up to 94.09 % and the smoothest surface with less visible holes. WPI based beads revealed lower EE% and smaller size than PPI based ones. In addition, WPI based beads showed rough surface with visible signs of cracks, while PPI beads showed dense surfaces with pores and depressions. In contrast, the combination of the two proteins resulted in compact and smooth beads with less visible pores/wrinkles. The survival in gastrointestinal tract (GIT) was observed through TNO in-vitro gastrointestinal model (TIM-1) and results illustrated that all microbeads shrank in gastric phase while swelled in intestinal phase. In addition, in-vitro survival rate of free cells was very low in gastric phase (18.2 %) and intestinal phase (27.5 %). The free cells lost their viability after 28 days of storage (2.66 CFU/mL) with a maximum log reduction of 6.76, while all the encapsulated probiotic showed more than 106-7 log CFU/g viable cell. It was concluded that encapsulation improved the viability of probiotics in GIT and utilization of WPI + PPI in combination provided better protection to probiotics.

Effects of Light Shading, Fertilization, and Cultivar Type on the Stable Isotope Distribution of Hybrid Rice.

The effect of fertilizer supply and light intensity on the distribution of elemental contents (%C and %N) and light stable isotopes (C, N, H, and O) in different rice fractions (rice husk, brown rice, and polished rice) of two hybrid rice cultivars (maintainer lines You-1B and Zhong-9B) were investigated. Significant variations were observed for δ13C (-31.3 to -28.3‱), δ15N (2.4 to 2.7‱), δ2H (-125.7 to -84.7‱), and δ18O (15.1‱ to 23.7‱) values in different rice fractions among different cultivars. Fertilizer treatments showed a strong association with %N, δ15N, δ2H, and δ18O values while it did not impart any significant variation for the %C and δ13C values. Light intensity levels also showed a significant influence on the isotopic values of different rice fractions. The δ13C values showed a positive correlation with irradiance. The δ2H and δ15N values decreased with an increase in the irradiance. The light intensity levels did not show any significant change for δ18O values in rice fractions. Multivariate ANOVA showed a significant interaction effect of different factors (light intensity, fertilizer concentration, and rice variety) on the isotopic composition of rice fractions. It is concluded that all environmental and cultivation factors mentioned above significantly influenced the isotopic values and should be considered when addressing the authenticity and origin of rice. Furthermore, care should be taken when selecting rice fractions for traceability and authenticity studies since isotopic signatures vary considerably among different rice fractions.

A review of recent compound-specific isotope analysis studies applied to food authentication.

Compound-specific stable isotope analysis (CSIA) of food products is a relatively new and novel technique used to authenticate food and detect adulteration. This paper provides a review of recent on-line and off-line CSIA applications of plant and animal origin foods, essential oils and plant extracts. Different food discrimination techniques, applications, scope, and recent studies are discussed. CSIA δ13C values are widely used to verify geographical origin, organic production, and adulteration. The δ15N values of individual amino acids and nitrate fertilizers have proven effective to authenticate organic foods, while δ2H and δ18O values are useful to link food products with local precipitation for geographical origin verification. Most CSIA techniques focus on fatty acids, amino acids, monosaccharides, disaccharides, organic acids, and volatile compounds enabling more selective and detailed origin and authentication information than bulk isotope analyses.. In conclusion, CSIA has a stronger analytical advantage for the authentication of food compared to bulk stable isotope analysis, especially for honey, beverages, essential oils, and processed foods.

Wheat authentication：An overview on different techniques and chemometric methods.

Wheat (Triticum aestivum L.) is one of the most important cereal crops and is consumed as a staple food around the globe. Wheat authentication has become a crucial issue over the last decades. Recently, many techniques have been applied in wheat authentication including the authentication of wheat geographical origin, wheat variety, organic wheat, and wheat flour from other cereals. This paper collected related literature in the last ten years, and attempted to highlight the recent studies on the discrimination and authentication of wheat using different determination techniques and chemometric methods. The stable isotope analysis and elemental profile of wheat are promising tools to obtain information regarding the origin, and variety, and to differentiate organic from conventional farming of wheat. Image analysis, genetic parameters, and omics analysis can provide solutions for wheat variety, organic wheat, and wheat adulteration. Vibrational spectroscopy analyses, such as NIR, FTIR, and HIS, in combination with multivariate data analysis methods, such as PCA, LDA, and PLS-DA, show great potential in wheat authenticity and offer many advantages such as user-friendly, cost-effective, time-saving, and environment friendly. In conclusion, analytical techniques combining with appropriate multivariate analysis are very effective to discriminate geographical origin, cultivar classification, and adulterant detection of wheat.

Geographical origin classification of peanuts and processed fractions using stable isotopes.

This study investigates the use of stable isotopes (C, N, H, and O) to characterize the geographical origin of peanuts along with different peanut fractions including whole peanut kernel, peanut shell, delipidized peanuts and peanut oil. Peanut samples were procured in 2017 from three distinctive growing regions (Shandong, Jilin, and Jiangsu) in China. Peanut processing significantly influenced the δ 13C, δ 2H, and δ 18O values of different peanut fractions, whereas δ 15N values were consistent across all fractions and unaffected by peanut processing. Geographical differences of peanut kernels and associated peanut fractions showed a maximum variance for δ 15N and δ 18O values which indicated their strong potential to discriminate origin. Different geographical classification models (SVM, LDA, and k-NN) were tested for peanut kernels and associated peanut fractions. LDA achieved the highest classification percentage, both on the training and validation sets. Delipidized peanuts had the best classification rate compared to the other fractions.

Geographical discrimination of Chinese winter wheat using volatile compound analysis by HS-SPME/GC-MS coupled with multivariate statistical analysis.

This study aimed to develop a potential analytical method to discriminate the Chinese winter wheat according to geographical origin and cultivars. A total of 90 wheat samples of 10 different wheat cultivars among three regions were examined by headspace solid phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). The peak areas of 32 main volatile compounds were selected and subjected to statistical analysis, which revealed significant differences among different regions and cultivars. Multivariate analysis of variance showed a significant influence of regions, wheat genotypes, and their interaction on the volatile composition of wheat. Principal component analysis of the aromatic profile showed better visualization for wheat geographical origins. Finally, a classification model based on the linear discriminant analysis was successfully constructed for the discrimination of regions and cultivars with the correct classification percentages of 90 and 100%, respectively.

Geographical traceability of wheat and its products using multielement light stable isotopes coupled with chemometrics.

The present study was aimed to investigate the variation of stable isotopic ratios of carbon, nitrogen, hydrogen, and oxygen in wheat kernel along with different processed fractions from three geographical origins across 5 years using isotope ratio mass spectrometry (IRMS). Multiway ANOVA revealed significant differences among region, harvest year, processing, and their interactions for all isotopes. The region contributed the major variability in the δ13 C ‰, δ2 H ‰, δ15 N ‰, and δ18 O‰ values of wheat. Variation of δ13 C ‰, δ15 N ‰, and δ18 O ‰ between wheat whole kernel and its products (break, reduction, noodles, and cooked noodles) were ˂0.7‰, and no significant difference was observed, suggesting the reliability of these isotope fingerprints in geographical traceability of wheat-processed fractions and foods. A significant influence of wheat processing was observed for δ2 H values. By applying linear discriminant analysis (LDA) to the whole dataset, the generated model correctly classified over 91% of the samples according to the geographical origin. The application of these parameters will assist in the development of an analytical control procedure that can be utilized to control the mislabeling regarding geographical origin of wheat kernel and its products.

Source identification and spatial distribution of arsenic and heavy metals in agricultural soil around Hunan industrial estate by positive matrix factorization model, principle components analysis and geo statistical analysis.

Characterizing the distribution and defining potential sources of arsenic and heavy metals are the basic preconditions for reducing the contamination of heavy metals and metalloids. 71 topsoil samples and 61 subsoil samples were collected by grid method to measure the concentration of cadmium (Cd), arsenic (As), lead (Pb), copper (Cu), zinc (Zn), nickel (Ni) and chromium (Cr). Principle components analysis (PCA), GIS-based geo-statistical methods and Positive Matrix Factorization (PMF) were applied. The results showed that the mean concentrations were 9.59 mg kg-1, 51.28 mg kg-1, 202.07 mg kg-1, 81.32 mg kg-1 and 771.22 mg kg-1 for Cd, As, Pb, Cu and Zn, respectively, higher than the guideline values of Chinese Environmental Quality Standard for Soils; while the concentrations of Ni and Cr were very close to recommended value (50 mg kg-1, 200 mg kg-1), and some site were higher than guideline values. The soil was polluted by As and heavy metals in different degree, which had harmful impact on human health. The results from principle components analysis methods extracted three components, namely industrial sources (Cd, Zn and Pb), agricultural sources (As and Cu) and nature sources (Cr and Ni). GIS-based geo-statistical combined with local conditions further apportioned the sources of these trace elements. To better identify pollution sources of As and heavy metals in soil, the PMF was applied. The results of PMF demonstrated that the enrichment of Zn, Cd and Pb were attributed to industrial activities and their contribution was 24.9%; As was closely related to agricultural activities and its contribution was 19.1%; Cr, a part of Cu and Ni were related to subsoil and their contribution was 30.1%; Cu and Pb came from industry and traffic emission and their contribution was 25.9%.