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.     

Comparison of multivariate models and variable selection algorithms for rapid analysis of the chemical composition of field crops

This study evaluates the use of visible and near-infrared spectroscopy for rapid prediction of total carbon, total nitrogen, and total phosphorus concentrations in field crop samples. Two multivariate models (partial least squares regression and support vector machine regression) were compared. In addition, four spectral variable selection algorithms (competitive adaptive reweighted sampling, genetic algorithm, uninformative variable elimination, and variable importance for projection) were applied with support vector machine regression to determine the most accurate predictions. The results showed that support vector machine regression performed better than partial least squares regression for predicting the three chemical compositions. The combination of competitive adaptive reweighted sampling and support vector machine regression outperformed the other models for the predictions of total carbon and total nitrogen with high coefficients of determination of 0.91 and 0.90, respectively. For the determination of total phosphorus, the prediction accuracy of competitive adaptive reweighted sampling was comparable with the best result obtained from genetic algorithm with the coefficients of determination of 0.73 and 0.77, respectively. In conclusion, the support vector machine regression combined with competitive adaptive reweighted sampling has great potential to accurately determine the chemical composition of field crops using the visible and near-infrared spectroscopy.     

Detection of Frozen-Thawed Cycles for Frozen Tilapia (Oreochromis) Fillets Using Near Infrared Spectroscopy

Due to the ambient temperature fluctuation during the process of storage, transportation, and sale, frozen fillets may go through frozen-thawed cycles that result in quality changes. In order to detect the frozen-thawed cycles in frozen tilapia fillets, near infrared spectroscopy was applied. The thawing loss, cooking loss, moisture content, total volatile basic nitrogen, and texture profile of fillets in seven frozen-thawed cycles were analyzed to reflect the change of quality. A total of 60 fillets were used to collect the spectra on both dorsal and belly flesh in frozen and thawed state from the first to the seventh cycle. The results showed that there is a better discrimination among the once and the repeated frozen-thawed samples in the frozen state than the thawed state. Frozen samples were still conducive to the differentiation and had higher classification accuracy, ranging from 80% to 93.33%. Dorsal flesh was more beneficial for the differentiation than belly flesh and had the highest accuracy, ranging from 86.67% to 93.33% in the frozen state. These results showed that the nondestructive and fast detection of repeated frozen-thawed cycles in frozen tilapia fillets can be accomplished by near infrared spectroscopy, which has enormous potential for practical application.     

基于近红外与中红外光谱技术的淀粉回生度检测

淀粉食品在加工、运输及储藏过程中会逐渐出现回生,其回生程度是影响淀粉食品品质的重要因素。利用近红外和中红外光谱技术快速、无损检测淀粉回生度。首先采集了储存不同时间淀粉的近红外和中红外光谱,分别利用近红外、中红外以及两者融合的光谱数据结合化学计量学方法(偏最小二乘法(PLS、iPLS、biPLS、siPLS))建立淀粉回生度检测模型。结果显示,近红外和中红外融合光谱技术的biPLS检测模型最佳,校正集和预测集相关系数分别为0.965 5和0.931 3。研究结果表明,红外光谱技术可以快速、无损检测玉米淀粉回生度,保障了富含淀粉食品的质量与安全。     

基于近红外光谱的猪肉水分在线检测与分级

基于近红外光谱法,优化光纤探头检测距离并通过检测距离调节系统和多点同时检测,设计了猪肉水分在线检测分级系统。首先,为确定光纤探头与生鲜猪肉样品表面间的最佳检测距离,在13个不同检测距离下(5~29 mm)采集了54个样品的光谱,采用多元散射校正方法对原始光谱进行预处理,分别建立了第1波段(349~1 435 nm)、第2波段(1 037~1 761 nm)和双波段结合3种情况的含水率偏最小二乘回归模型,分析了不同检测距离和不同波段的模型,确认19 mm为在线检测分级装备的最佳检测距离。然后,通过检测距离实时调节系统动态固定最佳检测距离,设计了双波段多点同时检测系统,采集45个猪肉样品在静态条件和在线条件下的光谱,通过比较分析,两种情况下预测结果相近,从而证实了所设计的在线系统能够预测猪肉水分,并且双波段融合建模效果优于单波段,预测结果为:校正集相关系数和校正均方根误差分别为0.906和0.598,验证集相关系数和预测均方根误差分别为0.836和0.402。最后,利用独立的21个猪肉样品验证猪肉预测分级模型精度及稳定性,结果判断正确率为90.48%,表明可见近红外光谱法结合多点检测能有效地在线检测猪肉水分并分级。     

机器学习在近红外光谱法判别鲍鱼品种研究中的应用

为解决市场上鲍鱼产品缺乏科学分类方法的问题,利用近红外光谱分析技术结合机器学习方法对鲍鱼快速分类进行研究,使用MicroNIRTM1700便携式近红外光谱仪采集3种鲍鱼,即绿盘鲍(25只)、红壳鲍(31只)、皱纹盘鲍(35只)的光谱数据,采用CART算法建立鲍鱼分类决策树模型,以模型对测试集样本的预测准确率衡量决策树模型优劣,分裂策略为在每个节点处选择Gini不纯度最大的方式进行分裂,通过交叉验证控制决策树深度。结果表明,对训练集180条光谱建立模型,采用5折交叉验证,模型准确率为90.00%,对测试集93条光谱的预测准确率为90.32%。本研究方法可以很好地区分绿盘鲍、红壳鲍和皱纹盘鲍,满足鲍鱼现场快速分类的需求。     

基于近红外漫反射光谱的云芝提取物多糖含量快速检测研究

为探索快速无损测定云芝提取物中多糖含量的方法,通过采集粉末状云芝提取物近红外光谱,经预处理和波段选择,结合间隔偏最小二乘法(iPLS)和反向区间偏最小二乘法(Bi-PLS),建立并优化云芝提取物多糖含量检测模型。结果表明,光谱区间为9 365.92~8 918.76 cm~(-1)和5 341.48~4 894.32cm~(-1),二阶导数(SD)预处理后,建立的反向区间偏最小二乘法模型更优,其校正决定系数(R_(cal))、校正均方根差(RMSECV)、验证决定系数(R_(val))和验证均方根差(RMSEP)可分别达到0.9089、0.00781、0.9879和0.00292。该模型可以更有效地优选建模所需波段,降低模型复杂度,降低多糖含量的检测成本,提高检测效率,实现云芝提取物多糖含量的快速、无损检测。