Rapid assessment of wood chemical properties and pulp yield of Eucalyptus camaldulensis in Thailand tree plantations by near infrared spectroscopy for improving wood selection for high quality pulp

Near-infrared (NIR) spectroscopy has been demonstrated as a means for rapid nondestructive determination of the chemical composition and final pulp yield of Eucalyptus camaldulensis in Thailand tree plantations. Multiple linear regression (MLR) analysis and partial least squares (PLS) analysis were introduced to develop statistical models in terms of calibration equations for total pulp yield, screened pulp yield, and contents of -cellulose, pentosans, and lignin in wood. In MLR analysis, a reasonably good calibration equation was found only for pentosans (standard error of prediction (SEP): 0.98%). The PLS analysis improved the accuracy of prediction for every criterion variable, especially for pentosans (SEP: 0.91%) and lignin (SEP: 0.52%). Also, in the case of screened pulp yield, we were able to use such a statistical result as an indicator of the characteristics of the pulp and paper. Thus, NIR spectroscopy could be satisfactorily used as an effective assessment technique for Eucalyptus camaldulensis plantation trees.     

基于硅光电池S1087的温室大棚光强控制系统研究

研究了一套用于调节温室大棚光照强度的自动控制系统。以MSP430作为主要控制器件,设计的I/V转化电路可将硅光电池输出的微弱电流信号转化为电压信号;采用两个S1087组成差分放大电路,以有效抑制温度漂移;通过NRF905SE红外无线传输模块,可以准确地将现场测量数据发送给监测中心,并接收来自监测中心的控制命令。试验结果表明,项目设计的控制系统运行稳定,抑制温度漂移能力好,光照强度测量精度高、量程大,能够准确地远程传送数据,从而实现远程在线控制,具有较高的应用价值。     

Integration of independent component analysis with near infrared spectroscopy for evaluation of rice freshness

The storage time and conditions of rice has an enormous effect on its appearance, flavor, and quality of the nutrients; and the acidity of rice usually increases with prolonged storage. Therefore, evaluation of freshness is an important issue for rice quality. In this study, the NIR (near infrared) spectra combined with independent component analysis (ICA) technique was used to evaluate the rice freshness. A total of 180 white rice samples were collected from 6 crop seasons for the purpose of developing an ICA-NIR based procedure for rice freshness as quantified by pH values. Values of pH were determined by a BTB-MR (bromothymol blue – methyl red) method. The best calibration model of white rice was developed using the smoothed first derivative spectra, five ICs and cross-validation; the results indicated that r² (coefficient of determination) = 0.924, and in units of pH, SEC (standard error of calibration) = 0.145, SEP (standard error of prediction) = 0.146, bias = 0.001, and RPD (residual predictive deviation) = 3.65. Freshness of white rice could be distinguished either visually by a 3-dimensional diagram composed from ICs 2, 3 and 4, or statistically by a calibration model. The results show that ICA with NIR has the potential to be adopted as an effective method for evaluating rice freshness.     

Amino acid profile of the quinoa (Chenopodium quinoa Willd.) using near infrared spectroscopy and chemometric techniques

The high content of amino acids of the quinoa, especially essential amino acids (higher than other cereals) makes a food increasingly demanded by consumers. A total of twelve amino acids (arginine, cystine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine) were analyzed in quinoa samples from Chile by near infrared spectroscopy (NIR) with direct application to the samples of a remote fiber-optic reflectance probe. The calibration results using modified partial least squares (MPLS) regression satisfactorily allowed the determination of the concentrations of this amino acid group with high multiple correlation coefficients (RSQ = 0.97–0.71) and low standard prediction errors (SEPC = 0.07–0.20). The prediction capacity (RPD) for the arginine, the cystine, the isoleucine, the lysine, the serine, the threonine, the tryptophan, the tyrosine and the valine ranged between 2.6 and 5.2, for the rest of amino acids were higher to 1.8, indicating that the NIRS equations obtained were applicable to unknown samples. It has confirmed that NIRS technology is a method that may be useful to replace the traditional methods for routine analysis of some amino acids.     

红外光谱结合聚类分析鉴别青海枸杞产地

采用傅立叶变换红外光谱（FTIR）对枸杞样品产地模式进行鉴别.用常规预处理方法和小波变换法对红外光谱原始数据进行了预处理,利用聚类分析对已预处理后的红外光谱数据进行分析.聚类分析将12份样品聚为枸杞主产地和非主产地两大类,取得了较满意的分类效果.红外光谱法结合聚类分析法可作为枸杞产域分类鉴别的一种新的适用方法.     

基于近红外光谱苗期玉米叶片叶绿素含量的无损检测方法

试验以42片未经处理的苗期玉米叶片为试材,采用近红外光谱检测技术和SNV+Detrending的预处理方法,应用偏最小二乘回归分析方法建立定量分析模型,通过测定近红外光谱图,研究3 300~10 000 cm-1范围内苗期玉米叶片的光谱特性。结果表明,测定苗期玉米叶片叶绿素含量的决定系数R2为0.989,残差均方根RMSE为0.047,采用近红外光谱快速检测苗期玉米叶片叶绿素含量是可行的。     

基于稳态空间分辨光谱的猪肉肌红蛋白检测方法

The objective of this work was to determine the concentration of myoglobin in fresh pork meat and establish a fast non-invasive method using steady spatially-resolved spectroscopy (SRS). The concentration of myoglobin was calculated based on absorbance coefficients at 640, 760, 800, 850 nm. The results showed that the value of myoglobin using SRS was significantly correlated (r2=0.955) to traditional methods with the accuracy of 3.6%F.S. The results showed that the concentration of myoglobin can be measured by using near infrared steady spatially-resolved spectroscopy.     

便携式豆类品质监控系统研究

传统的破坏性检测方法已难以满足豆类品质快速检测的需求。现有的无损检测设备存在稳定性及准确性不高等问题,为提高豆类品质含量检测装置的性能,基于近红外光谱技术研发了豆类品质无损检测装置,体积小、便于携带,能够适用于现场检测。基于所研发的装置,各取30个黄豆、绿豆、红豆、黑豆样本,通过旋转静态采集多次光谱求平均值与采集1次光谱的方式,对同一样品重复测量20次,得出随着采集次数的增加,光谱反射率变异系数平均值逐渐减小直至平缓,选取最佳豆类采集次数分别为16、8、14、16,对应的光谱变异系数平均值为2.9%、2.435%、2.763%、3.019%。以黄豆为例,选取80个样品,使用不同的预处理方法,分别建立黄豆蛋白质、粗脂肪和淀粉含量的偏最小二乘预测模型,结果表明,蛋白质、粗脂肪、淀粉质量分数预测的最优模型预处理方式分别为SG-MSC、SNV、SNV,其预测集相关系数R_p分别为0.974 6、0.950 5、0.960 7,均方根误差分别为0.249%、0.572%、0.623%。取40个黄豆样本对装置模型进行试验验证,蛋白质、粗脂肪、淀粉质量分数的独立验证相关系数R     

基于近红外光谱定量分析技术对大米年际的研究

大米是我国居民膳食的重要来源,其新陈度直接影响到大米的营养成分。为了探索大米新陈度的高效、安全、快速检测方法,利用近红外光谱技术对150份大米进行检测研究。结果表明,采用偏最小二乘法建立的定量分析模型对不同年际大米的正确鉴别率高达94.1%。因此,使用近红外光谱对大米进行快速新陈度鉴别可取得较高精确和稳定的检测结果。