高能湿法球磨制备鱼胶原肽螯合钙及其理化特征

为了拓展胶原肽螯合钙的制备工艺,以鱼胶原肽和CaCl2为原料,以高能湿法球磨的方法制备了鱼胶原肽螯合钙,并采用FT-IR红外图谱、X-衍射、EDS能谱等技术手段对其理化特性和结构进行分析。结果显示,随着球磨时间从15 min延长到240 min,鱼胶原肽的平均粒径逐渐降低,ZETA电位绝对值、分子量和pH值逐渐增加,鱼胶原肽对钙的螯合率逐渐从74.21%增加到85.42%。鱼胶原肽螯合钙与鱼胶原肽的主体化学结构相似,但是球磨后鱼胶原肽的-COO-1 VAS吸收峰(1 643.89 cm~(-1))向短波数方向移动到1 540～1 555 cm~(-1),而-NH2的特征吸收峰(3 313.9 cm~(-1))向长波数方向移动到3 337.12～3 380.59 cm~(-1)。球磨后鱼胶原在2θ=20°处的特征宽吸收峰消失,而在2θ=26、28、48和58°处出现多个尖峰和弥散峰,表明其从无定形结构向兼具结晶和无定形结构转变。鱼胶原肽螯合钙中钙元素比例逐渐增加,证实鱼胶原肽对钙的螯合率增加。研究表明,高能湿法球磨可应用于鱼胶原肽螯合钙的制备,该方法具有加工工艺简单、无污染和螯合率高等优点。     

4种植物油热氧化同步荧光光谱分析

以亚麻籽油、大豆油、菜籽油与葵花籽油为原料,在不同温度条件下氧化并测定其不同时间的同步荧光光谱,分析同步荧光光谱及荧光物质变化情况。结果表明,4种植物油不同温度下的同步荧光光谱峰变化主要集中在300~415 nm内,且随着加热时间的延长存在波动现象;在50℃和150℃加热氧化条件下,300,330,375,415 nm这4个峰都存在明显的波动现象;二维相关分析表明,这4个峰的荧光强度变化速率不同,不同油变化差异较大;同种油在不同温度下,4个峰的波动情况、相互关系、变化速率均有明显差异。这些峰的变化与油脂氧化过程中荧光物质和油脂氧化程度的变化有关,可以利用同步荧光光谱的变化特性作为监控油脂氧化的依据。     

Changes in wettability of wood surface using electron beam irradiation

ABSTRACT

This study deals with the influence of electron beam irradiation (EBI) on the wettability of Norway spruce surface samples. To evaluate this possible effect in detail, the changes in chemistry and wood structure were analysed using the methods of IR spectroscopy and Scanning Electron Microscopy (SEM). The surface wettability was determined by the contact angle measurement method. The results of infrared spectra show decreased relative numbers of available hydrophilic groups (e.g. hydroxyl and carbonyl groups) in the wood surfaces due to the electron beam irradiation. This behaviour is consistent with the values from the surface wettability measurements. Structural differences of the wood surfaces after electron beam irradiation were not detectable. Based on these findings pre-treatment of wood surfaces for industrial usages can be a potential field of application of this technology.     

Impact of sampling and storage technique,and duration of storage,on the composition of fresh grass when analysed using near‐infrared reflectance spectroscopy

The objective of this study was to examine the effect of sampling technique (pluck or cut), storage duration (immediate analysis, 24‐h or 48‐h), storage temperature (ambient or chilled) and storage conditions (air present, air excluded or breathable) on the composition of fresh grass sampled from a sward managed to simulate grazing. Treatments were repeated across four sampling dates, with grass samples stored in grip seal bags prior to analysis using near‐infrared reflectance spectroscopy. Grass sampled by ‘pluck’ had a higher crude protein and ME content, and a lower acid detergent fibre (ADF) content, compared to that sampled by ‘cut’. Grass stored for 48 h had a lower water soluble carbohydrate (WSC) and ME content and a higher ADF content than for immediate analysis. Samples stored for 24 h did not differ from immediate analysis. Grass stored at ambient temperature had a lower WSC and ME content compared to immediate analysis. Grass stored under ‘breathable’ conditions had a lower ME content and higher ADF content than immediate analysis or samples stored with air present or air excluded. It is recommended that grass for analysis should be sampled by cutting, stored chilled (4°C) in a sealed bag to minimize exposure to oxygen and analysed within 24 h of harvest.     

基于光声光谱和TCA迁移学习的稻种活力检测

种子活力是决定水稻产量的最重要因素之一，但目前水稻种子活力的近红外和高光谱等无损检测方法易受种子表皮颜色影响，且所建模型难以适应新品种。该研究提出基于光声光谱技术的稻种活力无损检测方法并结合迁移学习进行新品种稻种活力检测。首先，对Y两优、龙粳、南粳、宁粳、武运粳、新两优等具有区域代表性的典型6种水稻品种，进行高温高湿人工老化处理，得到0~7 d老化时间的水稻种子；再通过调制频率获得8种不同深度的光声光谱信息，用主成分分析、竞争性自适应重加权算法对光谱降维得到特征光谱后，对Y两优、龙粳、南粳、宁粳、武运粳分别建立偏最小二乘回归（Partial Least Squares Regression，PLSR）、反向传播神经网络（Back Propagation Neural Network，BP）、广义回归神经网络（Generalized Regression Neural Network，GRNN）、支持向量回归模型（Support Vector Regression，SVR）、深度卷积神经网络（Convolutional Netural Network，CNN）的稻种活力预测模型，并选择最优调制频率；最后，通过迁移学习将建立的模型迁移到新两优稻种进行活力预测。结果表明，光声光谱最佳扫描频率为300 Hz，CNN预测模型精度较高，相关系数和均方根误差分别优于0.990 9、低于0.967 5；且经过迁移学习，仅需通过对源域数据的训练，即可直接对新品种稻种的活力进行精确预测；通过TCA迁移学习后，新两优稻种活力预测的相关系数从0.718 5提高到0.990 3。研究表明，采用光声光谱深度扫描技术对不同种类稻种的活力进行高精度检测是可行的，且经过迁移学习，仅需80粒新品种稻种信息即可实现稻种活力的精确预测。     

玉米粉淀粉含量近红外模型建立与优化

以230份玉米自交系为样本，采用旋光法与一阶导数及去一条直线的光谱预处理法，构建玉米粉样淀粉含量的近红外分析（NIRS）模型。研究标明，该模型可显著提高子粒淀粉含量预测的准确性。该模型的定标标准偏差（RMSEE）、交叉验证标准偏差（RMSECV）、外部验证标准偏差（RMSEP）、定标相关系数（R_cal²）、交叉验证相关系数（R_cv²）、外部验证相关系数（R_cv²）分别为0.609、0.722、0.738、0.909、0.864和0.854。建立的玉米粉样NIRS模型可将预测值与化学值偏差控制在1.7%内，能够准确定量分析玉米子粒淀粉含量，应用于育种材料早期筛选及群体水平粗淀粉分析。     

FT-Raman and FT-IR studies of the gluten structure as a result of model dough supplementation with chosen oil pomaces

The pomaces obtained after cold pressing oil production from oil seeds can be regarded as a source of many valuable nutrients such as unsaturated fatty acids, dietary fibre and antioxidants. The aim of the research was to determine the effect of five oil pomaces from black seed, hemp, pumpkin, milk thistle and primrose on the structure of gluten proteins. The model dough was supplemented with 3%, 6%, and 9% of the pomaces. Structural changes in gluten proteins were studied using Fourier transform Raman spectroscopy (FT-Raman) and Fourier transform infrared spectroscopy (FT-IR). Additionally, chemical composition of the pomaces was determined. Changes observed in the gluten structure resulting from the model dough supplementation with oil pomaces allow the pomace to be divided into two groups depending on the type and amount of fatty acids present in the pomaces. If the pomace contains a low number of fatty acids (black seed and pumpkin), pseudo-β-sheets are formed from β-turns and antiparallel-β-sheets in the gluten network. If the pomace contains a high number of fatty acids, non-aggregated β-structures are observed. Although the pomaces contain considerable amount of dietary fibre and polyphenols, comparison of the observed structural changes in gluten network with pomaces’ chemical composition indicates that the changes are connected with presence of fatty acids.     

Growing Degree Day: Noninvasive Remotely Sensed Method to Monitor Diet Crude Protein in Free-Ranging Cattle

The daily nutritional balance of free-ranging cattle is the net result of intake from available forage biomass and nutritive value weighed against the nutritional requirements of the animal. Plant phenology influences nutritive value. Plant phenology is dictated by time of year and an accumulation of photosynthetically active days. Growing degree day (GDD) is a concept that quantifies this relationship and has been used to predict nutritive value in perennial range grasses. GDD could be substituted for chemical analysis to inform grazing animal nutritional monitoring efforts. We hypothesized that in C4 grass-dominated rangelands, a cumulative GDD calculation would correlate with diet crude protein (CP) predictions obtained by fecal near infrared spectroscopy (FNIRS) from free-ranging cattle. Therefore, the objectives of our research were to evaluate the effectiveness of GDD to predict FNIRS-derived determinations of grazing cattle diet CP in 1) two groups of three individual animals grazing a small native pasture and 2) large commercial-scale herds grazing expansive rangelands. For the first objective, cumulative GDD and FNIRS-predicted diet CP were strongly correlated (r² = 0.76; P < 0.01). Relationships between cumulative GDD and FNIRS-predicted diet CP for the second objective varied considerably among ranches, ranging from a low r² of 0.05 (P = 0.871) to a high r² of 0.78 (P < 0.049). Similar values for individual ranch/year combinations were stronger; ranging from a minimum r² of 0.44 (P = 0.556) to a maximum of 0.95 (P = 0.051). The aggregate relationship between GDD and FNIRS-predicted CP for all ranch/year combinations was highly significant (r² = 0.37; P < 0.001), but the standard error was 1.86% CP. The noninvasive remotely sensed grazing animal nutritional monitoring method described here was accurate enough to inform tactical rangeland diet quality assessments but was not accurate enough to inform operational-scale grazing management decisions.     

大麦籽粒抗性淀粉含量快速测定方法研究

为探索快速高效测定大麦籽粒中抗性淀粉含量的方法，利用衰减全反射中红外（attenuated total reflection mid-infrared spectroscopy,ATR-MIR）和近红外（near-infrared spectroscopy,NIR）光谱技术，分别用3种不同方法进行预处理，建立大麦样品的抗性淀粉含量快速测定红外模型，通过不同预处理预测模型的校正和内部交叉验证结果的比较，依据决定系数（r）和均方根误差（RMSE）筛选出基于ATR-MIR和NIR光谱的最佳预测模型，再对最佳预测模型进行外部验证。结果表明，经基线位移校正+范围归一化（BOC+RN）预处理后的PLS模型为最佳ATR-MIR预测模型；经标准正态变换+Savitzky-Golay法一阶求导（SNV+1thD）的预处理模型为最佳NIR预测模型。用验证集材料对BOC+RN和SNV+1thD最佳预测模型的预测效果进行外部验证，光谱预测值与化学测定值之间没有显著差异，说明两种方法均可以用于大麦籽粒抗性淀粉含量测定；ATR-MIR光谱比NIR光谱具有更好的预测能力。