Robust regression techniques A useful alternative for the detection of outlier data in chemical analysis

The validation of an analytical procedure means the evaluation of some performance criteria such as accuracy, sensitivity, linear range, capability of detection, selectivity, calibration curve, etc. This implies the use of different statistical methodologies, some of them related with statistical regression techniques, which may be robust or not. The presence of outlier data has a significant effect on the determination of sensitivity, linear range or capability of detection amongst others, when these figures of merit are evaluated with non-robust methodologies.In this paper some of the robust methods used for calibration in analytical chemistry are reviewed: the Huber M-estimator; the Andrews, Tukey and Welsh GM-estimators; the fuzzy estimators; the constrained M-estimators, CM; the least trimmed squares, LTS. The paper also shows that the mathematical properties of the least median squares (LMS) regression can be of great interest in the detection of outlier data in chemical analysis. A comparative analysis is made of the results obtained by applying these regression methods to synthetic and real data. There is also a review of some applications where this robust regression works in a suitable and simple way that proves very useful to secure an objective detection of outliers. The use of a robust regression is recommended in ISO 5725-5.     

Ambiguous Discrimination of General Quantum Operations

We consider the problem of discriminating general quantum operations. Using the definition of mapping operator to vector, and by some calculating skills, we derive an explicit formulation as a new bound on the minimum-error probability for ambiguous discrimination between arbitrary m quantum operations. This formulation consists only of Kraus-operators, the dimension, and the priori probabilities of the discriminated quantum operations, and is independent of input states. To some extent, we further generalize the bounds on the minimum-error probability for discriminating mixed states to quantum operations.     

鉴别玉米杂交种纯度的近红外光谱分析技术研究

以不同产地和年份的农华101(NH101)玉米杂交种和母本种子为对象,研究了鉴别玉米杂交种子纯度的近红外光谱分析方法。 光谱采集时间跨度达10个月,运用傅里叶变换(FT)近红外光谱仪器,在不同季节用23天(分五个时间段)采集了这些样品共920条玉米单子粒近红外漫反射光谱。 全部原始光谱用移动窗口平均、 一阶差分导数和矢量归一化进行预处理,使用主成分分析(PCA)方法和线性判别分析(LDA)方法降维,采用仿生模式识别(BPR)方法建立模型。 通过对光谱预处理校正光谱失真,使样品光谱集在特征空间分布的范围收缩,相对距离增大了近70倍,实现了母本和杂交种子的鉴别。 通过代表性样品的选择,提高了模型对光谱采集时间、 地点、 环境等条件变动的应变能力,也提高了模型对样品种子制种时间与地点变动的应变能力,增强了模型的稳健性,使测试集玉米单子粒杂交种和母本种子的平均正确识别率达到95%以上,而平均正确拒识率也达到85%以上。     

基于DS算法的玉米近红外定性分析光谱校正方法研究

从校正的角度出发,研究了近红外定性分析中模型稳定性问题。以13个玉米品种为研究对象,针对数据采集时间不同带来的模型失效问题,借鉴近红外光谱定量分析中两台仪器间模型传递的思想,将直接模型传递(Direct Standardization)算法用于校正同一仪器不同时间采集的光谱, 使得一次建立的品种鉴别模型,能用于其余时间测试数据的鉴别。首先采用Kennard/Stone算法在主光谱集中选取校正样品集,按照对应的编号从从光谱集中取出对应的数据,然后对校正样品集采用DS算法求取两组数据间的变换关系,再对剩余的从光谱集进行相应的变换得到适用于模型的光谱。实验中对比了校正样本数和模型校正位置对校正结果的影响,分别从品种定性鉴别准确性和校正前后主光谱数据和从光谱数据分布距离两方面分析了实验结果。结果表明,该方法能有效地解决同一仪器随着采样时间推移产生的光谱偏移现象,对采样时间不同的测试集均得到较高的识别率,提高了模型的鲁棒性和适用范围,由实验结果可见,校正位置处于特征提取之后时,校正效果最佳。     

On the exact solutions to the long–short-wave interaction system

The complete discrimination system for polynomial method is applied to the long-short-wave interaction system to obtain the classifications of single traveling wave solutions. Compared with the solutions given by the （G~/G）-expansion method, we gain some new solutions.     

Maximum confidence measurements via probabilistic quantum cloning

Probabilistic quantum cloning(PQC) cannot copy a set of linearly dependent quantum states.In this paper,we show that if incorrect copies are allowed to be produced,linearly dependent quantum states may also be cloned by the PQC.By exploiting this kind of PQC to clone a special set of three linearly dependent quantum states,we derive the upper bound of the maximum confidence measure of a set.An explicit transformation of the maximum confidence measure is presented.     

流动注射-石英晶体微天平中血清蛋白对扁桃酸的手性识别

采用流动注射-石英晶体微天平(FIA-QCM)和紫外光谱法研究了牛/羊血清蛋白(BSA和GSA)与扁桃酸的手性识别。 在QCM表面,BSA和GSA的浓度分别为4.9×10-12和8.8×10-12 mol/cm2。 血清蛋白与R-扁桃酸(R-MA)作用引起QCM振动频率变化值比S-扁桃酸(S-MA)大;相应紫外结合常数也比与S-MA大,说明血清蛋白对R-MA的作用强于与S-对映体。 从识别因子分析,GSA对扁桃酸的手性识别能力优于BSA。     

基于电子舌技术对不同年份的化橘红的识别

不同年份的化橘红功效和价格均存在差异,为了快速识别未知橘红样品的年份,采用电子舌对2001～2010年的10个样品和一个盲样进行了分析.对原始数据采用主成分分析和判别因子分析.结果表明:主成分分析可以将不同年份的样品进行有效的区分,判别因子分析可以更好地区分不同年份的样品,并能进行盲样识别.在模式识别中,判别因子分析法的识别结果均优于主成分分析法,前者2个判别因子的累计方差贡献量达到了99.929%.电子舌可以较好地区分不同年份的化橘红,改进后有望应用于化橘红的快速检测.     

玉米品种近红外光谱鉴别技术中的参数漂移问题研究

以13个玉米品种鉴别为研究对象,提出了一种解决光谱仪参数漂移问题的有效方法。使用同一台光谱仪分不同时间重复采集数据,用一天数据建模,其余测试,发现不同时间采集的数据有较大偏移,严重时正确识别率仅为7.69%。为此,提出一种有监督学习特征提取的多天联合建模方法,首先挑选具有代表性的多个时间段样本数据共同组成建模集,其次采用PLS+LDA特征提取算法,提取出与仪器参数漂移无关的品种特征信息, 然后采用BPR方法建立品种鉴别模型。实验结果表明,该方法对于不同时间数据的偏移均能有较好的校正效果,得到较高的识别率和稳定性。     

Tissue-specific δ13C in ancient and modern tropical seabirds and flying fish in the Xisha Islands,South China Sea

We analyzed ¹³C characteristics in samples of bird bones, feathers, eggshell carbonate and membrane from modern specimens of red-footed booby (Sula sula) as well as fish muscle, scales, and bones from its predominant food source, flying fish (Exocoetus volitans), and muscle from its secondary food source squid (Loligo chinensis), as well as in ancient sub-fossil samples of seabird and flying fish at the Xisha Islands, South China Sea. δ¹³C is tissue-specific in both seabirds and flying fish due to the variance in turn-over among the tissues and differences in the type and content of amino acids across a diverse range of tissues. The δ¹³C discrimination factors also differed significantly among the various tissues between tropical seabirds and their prey. A Suess effect, caused by fossil fuel combustion and the emission of carbon with fewer ¹³C isotopes, was observed in the bird and fish tissue from ancient to modern time. Our study provides a multiple variability index for δ¹³C in organisms along a food chain, and verifies that tissue-specific ¹³C analysis is essential to identify diet and species and thus is a valuable tool for research on tropical seabird ecology.