In this study, a protein-polysaccharide-polyphenol complex was prepared from soybean protein isolate (SPI), high methoxyl citrus pectin (HMCP) and gallic acid (GA). The preparation conditions were optimized by using one-factorat-a-time method and orthogonal array design. Furthermore, a Pickering emulsion was prepared with this complex and characterized for rheological properties, particle size and distribution, zeta potential and emulsion stability. The results showed that the maximum absorbance of 3.082 was observed for the emulsion containing the complex prepared at pH 4.5 and 35 ℃ using 40 mg of gallic acid. Under these conditions, SPI, HMCP and GA were most tightly bound. The Pickering emulsion with an oil volume fraction (φ) of 0.7 had the best elasticity and viscosity, and formed a dense gel network structure. Its zeta potential and average droplet size were (-54.08 ± 2.74) mV and (220.36 ± 7.13) nm, respectively. The Pickering emulsion showed weaker creaming and smaller droplet size at 4 ℃ than 25 ℃, which was more conducive to maintaining the emulsion stability. With the increase of heat treatment temperature, the creaming degree of the emulsion increased gradually. At φ values of 0.7 and 0.8, the droplet size was not affected by temperature. Freezing destroyed the interface of the emulsion. With increasing either φ value or freezing time, the creaming phenomenon became more obvious, greatly reducing the stability of the emulsion. With the increase of pH, the creaming phenomenon became more obvious. When the emulsion system pH was close to 4, the droplet size was the smallest and the droplet size distribution was relatively uniform. High concentration of salt ions destroyed the degree of binding of the complex, caused droplet aggregation and obvious creaming, and reduced the stability of the emulsion.

In this study, phenolic acid-citrus pectin copolymers was prepared by a free radical-mediated grafting method, and their structure and physicochemical properties were analyzed and compared. The results showed that among five phenolic acid-citrus pectin copolymers, the grafting rate was syringic acid-citrus pectin copolymer was the highest, (74.2 ± 1.38) mg/g, followed by gentisic acid-citrus pectin copolymer, (67.24 ± 1.55) mg/g. Compared with pectin, the molecular mass distribution of the graft copolymers was more homogeneous and decreased significantly. The molecular masses of gentisic acid-citrus pectin and syringic acid-citrus pectin copolymers decreased from (109.98 ± 0.05) kDa to (65.11 ± 0.02) and (39.83 ± 0.05) kDa, respectively, the degrees of esterification increased from (51.62 ± 1.46)% to (70.83 ± 1.64)% and (72.73 ± 2.18)%, respectively, the contents of galacturonic acid increased from (39.18 ± 1.08)% to (52.42 ± 1.36)% and (53.88 ± 1.19)%, respectively, and the solubility increased from (39.34 ± 1.08)% to (54.40 ± 1.36)% and (59.87 ± 1.21)%, respectively. In addition, through Fourier transform infrared spectroscopy and scanning electron microscopy, it was found that the monosaccharide type of phenolic acid-citrus pectin copolymer was identical to that of pectin, and phenolic acid was mainly covalently grafted onto the molecular chain of citrus pectin, resulting in a decrease in its thermal stability, and causing the structure of citrus pectin to change from rough, dense, blocky to flaky with a relatively smooth surface.

Food spoilage caused by mold has posed a major threat to grain quality and national food safety. Mold can also produce mycotoxins damage to the nutritional value of cereals, and even be harmful to human health. It is highly required for efficient and reliable detection. Traditional detection of the mold in grains can often involve time-consuming laboratory tests that rely heavily on specialized equipment. It is very necessary to realize the rapid on-site assessments. Alternatively, near-infrared spectroscopy (NIRS) can be expected to rapidly detect the mold of agricultural products, due mainly to the detection speed, non-destructive testing, repeatability, and easy online analysis. The purpose of this study was to establish a mold detection model in rice using NIRS. A systematic investigation was also implemented to rapidly distinguish rice from the different levels of mold. A dataset of 960 samples was taken from four varieties with different mold degrees (2018 Mudanjiang 27, 2019 Mudanjiang 27, Longjing long grain aromatic rice, and Muxiang 1). Qualitative discrimination models were then realized for the different degrees of mold contamination. The first derivative was combined with 9-point smoothing and factorization during preprocessing, in order to obtain the qualitative discriminant model with high accuracy. The mean S value greater than 1 represented the excellent performance to distinguish between mildewed and non-mildewed rice. The accuracy of the model was further verified by a leave-one cross-validation. The accuracy reached 93%. In addition, 300 independent data sets of rice samples were also utilized with the different degrees of mildew. The total number of mold colonies was quantitatively characterized using NIRs. A discrimination model was then established by vector normalization and partial least squares (PLS) method. Some indexes were calculated to evaluate the accuracy of cross-validation root-mean-square error (RMSECV), determination coefficient (R²), and performance deviation ratio (RPD). The prediction root-mean-square error (RMSEP) was also used to evaluate the accuracy of the model. The results showed that the values of RMSECV, R², RPD, and RMSEP were 0.470, 0.904 5, 3.24, and 0.45, respectively. The first three parameters indicated the high precision of the model, while the latter indicated the high accuracy of the model. Therefore, the improved model with high accuracy was achieved to rapidly predict the mold in grains. The mold was the main influencing factor on the variation of the NIR spectrum after optimization. On the contrary, there were relatively small effects of rice variety and harvest year on the spectral characteristics. Therefore, the NIRs can be highlighted to detect mold contamination in rice, regardless of variety or time differences. This finding can provide a strong reference for the rapid prediction of the mildew degree or amount of rice in different transportation using NIR spectroscopy. Online real-time monitoring equipment can also be offered to monitor the grain mildew in containers.