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To produce manno-oligosaccharides from cassia gum, a mutated glycoside hydrolase family 134 β-mannanase gene (mRmMan134A) from Rhizopus microsporus var. rhizopodiformis F518 was expressed in Pichia pastoris and a high expression level (3680 U mL−1) was obtained through high cell density fermentation. mRmMan134A exhibited maximum activity at pH 5.5 and 50 °C. It was then subjected to hydrolyze cassia gum with 70.6% of overall yield of manno-oligosaccharides. From the hydrolysate, seven components (F1–F7) were separated and identified as mannose, mannobiose, galactose, mannotriose, mannotetraose, 61-α- d -galactosyl-β- d -mannobiose, and mannopentaose, respectively. According to in vitro fermentation, the manno-oligosaccharides were able to promote the growth of three Bifidobacterium strains and six Lactobaillus strains with 3.0-fold increment in culture absorbance, and these strains preferred manno-oligosaccharides with degree of polymerization (DP) 2–3 rather than those with DP 4–5. Novel manno-oligosaccharides from cassia gum with promising prebiotic activity were provided in the present study.

More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ = 0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction ( R p ) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar.