Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.     

耐盐乳酸菌和酵母菌强化技术对高盐稀态发酵酱醪品质的影响

分别将耐盐乳酸菌Tetragenococcus halophilus CGMCC 3792、耐盐酵母菌Zygosaccharomyces rouxii CGM-CC 3791和Candida versatilis CGMCC 3790菌悬液接入酱醪中,利用顶空-固相微萃取偶联气质联用技术(HS-SPME/GC-MS)检测了发酵125 d酱醪中挥发性组分的变化。研究结果表明,T.halophilus CGMCC 3792强化的酱醪中酸类、醇类、酮类、呋喃类、吡嗪类和其他组分的相对含量降低,酯类、醛类和酚类组分的相对含量增加,其中苯乙醇、乙酸乙酯、辛酸乙酯、癸酸乙酯和4-乙基愈创木酚等的相对含量显著增加,而Z.rouxii CGMCC 3791、C.versatilis CGMCC 3790共培养强化的酱醪中醇类、酯类、醛类和其他组分的相对含量增加,酸类、酚类、呋喃类和吡嗪类组分的相对含量降低,其中苯乙醇、长链脂肪酸乙酯(月桂酸乙酯、棕榈酸乙酯、亚油酸乙酯等)的相对含量显著增加。此外,本研究中T.halophilus CGMCC 3792强化的酱醪中4-乙基愈创木酚的相对含量大幅增加,且在整个发酵过程中其含量也单调增加,对于T.halophilus CGMCC 3792是否具有分泌4-乙基愈创木酚的能力以及是否冷温工艺赋予该菌株合成4-乙基愈创木酚的能力需进一步探究。     

小麦面筋蛋白盐酸脱酰胺工艺优化及其酶解敏感性

文以小麦面筋蛋白为原料,优化了盐酸对小麦面筋蛋白的脱酰胺工艺,比较了最佳脱酰胺工艺下的小麦面筋蛋白在胰酶、风味蛋白酶和碱性蛋白酶酶解过程中蛋白回收率和水解度的变化,并对其酶解36 h酶解液的自由基吸收能力(ORAC)抗氧化特性进行分析评价。研究结果表明,盐酸脱酰胺的工艺条件是:小麦面筋蛋白浓度为24%,0.30 mol/L的HCl,65℃,24 h脱酰胺;在该工艺条件下,胰酶酶解液蛋白回收率和水解度最高,酶解效果最好;高脱酰胺程度小麦面筋蛋白在胰酶酶解36h后酶解液的蛋白回收率和水解度高于低脱酰胺程度的酶解液;ORAC抗氧化特性分析表明高脱酰胺程度小麦面筋蛋白酶解液的ORAC值高于低脱酰胺程度的酶解液,其ORAC值最高为(689.67±10.22)μmol Trolox/g。     

高压酶解大米蛋白抗氧化活性及其分子质量分布

以大米蛋白为原料,经不同压力(0.1、300、500 MPa)处理后,用Alcalase酶水解.以酶解物的还原力为指标,通过正交试验方法分别优化了3种压力条件下的酶解条件;用高效液相色谱分析了主要酶解产物的分子质量分布.结果表明,3种预处理蛋白在各自优化条件下制备的酶解物在1.0 mg/mL时的最大还原力分别为0.125、0.149、0.158.常压(即0.1MPa)对照组中10～15 ku、1～10 ku和1 ku以下等3段分子质量组分所占比例分别为26.6％、46.9％、26.5％;300 MPa和500 MPa组中的含量分别依次为16.5％、39.3％、44.2％和11.8％、33.1％、55.1％;可见,酶解物还原力大小与其分子质量的分布之间有一定的关联性.     

1000kW变频调速永磁同步电机齿槽转矩分析

基于1000kW变频调速永磁同步电机定子开口槽,采用定子分数槽和定子斜槽相结合的方法降低齿槽转矩,通过有限元仿真验证以及铁芯及线圈实例,证明该方法在实际制造及工艺过程中切实可行.     

漫谈苦瓜啤酒生产工艺及其功效成分的检测

苦瓜以其独有的清热祛火、解毒消暑的功效为人们所熟悉,具有特色的苦瓜啤酒在当今啤酒市场广受青睐。本文简要介绍了苦瓜啤酒的生产工艺以及苦瓜功效成分的检测监控。     

酶添加位置对废纸浆性能的影响

为了改善废纸浆的滤水性能和强度性能,对废纸浆进行了酶处理及PFI磨打浆处理,分析了酶添加位置、酶用量、处理时间以及酶处理时的浆浓等因素对废纸浆性能的影响。同时评估了酶处理浆和PFI磨打浆处理后的成浆性能。实验结果表明,当酶添加位置（即工厂打浆之前或之后）不同时,浆浓及酶用量对浆料滤水性能的影响效果是相反的。未进行工厂打浆而直接进行酶处理,浆料滤水性能和成纸强度性能均得到提高;但经过工厂打浆后再进行酶处理,浆料滤水性能和成纸强度性能变化不大。