Prediction of chemical composition and peroxide value in unground pet foods by near‐infrared spectroscopy

The massive development of the pet food industry in recent years has lead to the formulation of hundreds of canine and feline complete extruded foods with the objective of meeting both the needs of the animals and numerous demands from pet owners. In the meantime, highly variable raw material compositions and the industry's new production techniques oblige manufacturers to monitor all phases of the extrusion process closely in order to ensure the targeted composition and quality of the products. This study aimed at evaluating the potential of infrared technology (visible and near‐infrared spectrophotometer; 570–1842 nm) in predicting the chemical composition and peroxide value (PV) of unground commercial extruded dog foods. Six hundred and forty‐nine commercial extruded dog foods were collected. For each product, an unground aliquot was analysed by infrared instrument while a second aliquot was sent to a laboratory for proximate analysis and PV quantification. The wide range of extruded dog food typologies included in the study was responsible for the wide variability observed within each nutritional trait, especially crude fibre and ash. The mean value of the 208 pet foods sampled for PV quantification was 17.49 mEq O₂/kg fat (min 2.2 and max 94.10 mEq O₂/kg fat). The coefficients of determination in cross‐validation of NIRS prediction models were 0.77, 0.97, 0.83, 0.86, 0.78 and 0.94 for moisture, crude protein, crude fat, crude fibre, ash and nitrogen‐free extract (NFE) respectively. PV prediction was less precise, as demonstrated by the coefficient of determination in cross‐validation (0.66). The results demonstrated the potential of NIRS in predicting chemical composition in unground samples, with lower accuracy for moisture and ash, while PV prediction models suggest use for screening purposes only.     

傅立叶变换近红外光谱法检测白酒总酸和总酯

该文研究白酒总酸和总酯的快速检测技术,通过解析不同白酒样品的近红外光谱图,对光谱数据进行不同的处理,结果表明：用一阶导数预处理光谱,谱区选择6102～5446 cm^-1,利用人工神经网络与傅立叶变换近红外光谱相结合,采用内部交叉验证法建立模型,效果较好。其中,总酸模型的决定系数为96.73%,内部交叉验证均方根差为0.048 g/L;总酯模型的决定系数为99.58%,内部交叉验证均方根差为0.085 g/L;进一步对总酸和总酯的模型进行验证和评价,结果表明总酸模型验证集的相关系数为99.2%,预测标准偏差为0.074 g/L;总酯模型验证集的相关为99.7%,预测标准偏差为0.134 g/L,表明建立的模型可靠,预测效果好,能满足白酒生产中总酸和总酯的快速检测要求。     

城乡共建田园综合体——城市近郊乡村用地规划研究

:在城市快速扩张的冲击下,城市乡村之间整体呈现出割裂关系,不利于社会的健康发展,城市边缘区乡村用地地位于城市农村的腹地,是未来城乡融合发展的桥梁纽带。随着乡村振兴战略的提出,以产业结构转型为代表的田园综合体为未来乡村发展提供了新的思路,本文探讨了在城乡融合背景下城市近郊乡村地区建设田园综合体的可行性、建设原则与建设思路。     

小波变换在土壤有机质含量可见/近红外光谱分析中的应用

使用高光谱仪ASD Field Spec在波长范围400~1 000 nm内采集有机质含量不同的土壤原始光谱;之后,求取土壤原始光谱及其A值的一阶导数(一阶导数光谱);对以上两类一阶导数光谱进行小波去噪处理;并分析这两类一阶光谱在去噪前、后,光谱形态的变化情况,以及它们与有机质含量相关性的改善程度。结果表明:(1)土壤原始光谱及其A值的一阶导数中含有大量噪声,严重影响了对光谱曲线波形轮廓、特征信号点和有机质吸收特征的识别。(2)经小波去噪处理后,土壤原始光谱及其A值的一阶导数中的噪声被有效去除,一阶导数光谱的清晰度明显改善,光谱质量显著提高。(3)去噪后的土壤原始光谱的一阶导数,呈开口向下的"喇叭口"状,波长范围567~598 nm内与有机质含量呈较为稳定、显著的负相关性。(4)去噪后的土壤原始光谱A值的一阶导数,波形呈"凹"状,波长范围524~535 nm内与有机质含量呈比较稳定、显著的正相关性。     

基于可见/近红外光谱技术的毛脚茧的光谱快速鉴别方法研究

基于可见/近红外光谱技术,研究了鲜茧毛脚茧快速鉴别的方法。采用主成分分析法对毛脚茧和成熟茧进行定性分析,再对光谱数据采用最小二乘支持向量机方法建模鉴别,通过连续投影算法从400-1000 nm波段的鲜茧光谱数据中选取了12个特征波长。采用最小二乘支持向量机法基于400 nm,430 nm,487 nm,512 nm,604 nm,616 nm,732 nm,759 nm,784 nm,852 nm,985 nm,999 nm这12个特征波长建模,结果对毛脚茧的判别准确率达到100%。表明可见/近红外光谱可以用于毛脚茧的鉴别检测。     

Feasibility analysis of NIR for detecting sweet corn seeds vigor

This paper explores the feasibility of particle-based detection and grading of seed vigor based on a self-built seed single-granulation device using near infrared spectroscopy (NIRS). Sweet corn with uniform kernel size was used for this study. The seed samples were divided into three types, they were normal seeds, artificially aged seeds and heat-damaged seeds. A 2-part spectral acquisition of each seed were performed, one for the collection of seeds that fall into the detection zone within the separation pipe, another was on the static platform, whose collection was performed on 5 faces of each seed. Partial least squares discriminant analysis (PLS-DA) was used to classify the original data of the seeds. In the 2 parts, the discriminant results of the unprocessed normal seeds and the artificial accelerated aging seeds, the untreated normal seeds and the heat-damaged seeds showed that classification accuracy was higher than 98%. The research indicates that the spectral data of different positions of seeds can reflect their activity information, and it is feasible to detect and classify seeds in real time in the detection area of the separation pipeline.     

基于NIR定量模型快速预测发酵麸皮中还原糖和可溶性蛋白成分的含量

为建立一种能快速预测发酵麦麸还原性糖和可溶性蛋白含量的定量分析模型,本研究以发酵麦麸为样本,采用3,5-二硝基水杨酸比色法（3,5-dinitrosalicylic acid, DNS）和BCA蛋白浓度测定法分别测定样品的还原性糖和可溶性蛋白含量;利用近红外光谱技术（Near infrared spectrum instrument, NIR）结合偏最小二乘法（Partial least squares regression, PLS）,比较预处理方法、最佳波长及主成分因子的决定系数R²,建立发酵麦麸还原性糖和可溶性蛋白含量的NIR快速检测定量模型。结果表明：1）发酵麦麸还原性糖定量模型的预处理方法使用一阶导数 （First derivative, FD）+二阶导数 （Second derivative, SD）+标准正态变换 （Standard normal variate, SNV）,光谱范围为908~1 670 nm ,主因子数为7时,模型效果最优,其决定系数Rc²为0.904 8,校正均方根误差（Square error corrected, SEC）为1.576 1,相对分析误差（Relative percentage difference, RPD）为3.240 8;外部验证集决定系数Rp²为0.954 9且还原糖活性成分的验证集样本的测定值与NIR光谱预测值的P值为0.959 5>0.05。2）发酵麦麸可溶性蛋白定量模型的预处理方法使用一阶导数 （First derivative, FD）+二阶导数 （Second derivative, SD）+标准正态变换 （Standard normal variate, SNV）,光谱范围为908~1 670 nm ,主因子数为10时,模型效果最优,其决定系数Rc²为0.938 2,校正均方根误差（Square error corrected, SEC）为2.003,相对分析误差（Relative percentage difference, RPD）为4.021 9;外部验证集决定系数Rp²为0.994 4,且可溶性蛋白活性成分的验证集样本测定值与NIR光谱预测值的P值为0.901 9>0.05。综上,建立的NIR光谱定量模型稳定性和准确性较好,且预测准确度良好,可用于快速预测发酵麦麸样品的还原性糖和可溶性蛋白含量。     

苹果可溶性固形物含量的检测方法——基于可见光近红外光谱技术

为了快速检测苹果的可溶性固形物(SSC)含量,采用可见光近红外光谱技术,结合主成分分析(PCA)和BP神经网络技术,来建立苹果SSC的预测模型.获取苹果样本在345～1039 nm波段的漫反射光谱,采用DPS数据处理系统对其进行主成分分析,并提取出累计可信度大于95 %的5个新主成分.建立一个3层的BP神经网络模型,并将这5个新的主成分作为BP神经网络模型的输入量,其结果是98 %以上预测样本的预测相对误差在5 %以下.该研究表明,采用近红外光谱技术来建立苹果可溶性固形物的预测模型是可行的.     

Loss of and damage to scallops due to storms in the Sea of Okhotsk

In the coastal zone of the Sea of Okhotsk off Hokkaido, Japan, the giant ezo scallop ( Patinopecten yessoensis ) is widely cultivated on the sea bed, making its fishery a major industry. However, the area experiences intense cyclones, including typhoons, about once a decade and the scallop resources appear to have received severe damage from the storms. Such an event occurred on 29 October 1988. The storm caused 12-m-high wind waves, which appeared to be fully developed sea waves. The water above the sea bed repeatedly moved back and forth at speeds of 1∼2 m s^-1 causing damage to the scallops due to collisions and rubbing with gravel and other scallops. From a comparison of wind data with the annual catch of scallops, intense cyclones seem to play an important role on the year-to-year fluctuations in the scallop fishery.