基于SVM与RF的苹果树冠LAI高光谱估测

叶面积指数(leaf area index,LAI)是反映作物群体大小的较好的动态指标。运用高光谱技术快速、无损地估测苹果树冠叶面积指数,为监测苹果树长势和估产提供参考。以盛果期红富士苹果树为研究对象,采用ASD地物光谱仪和LAI-2200冠层分析仪,在山东省烟台栖霞研究区,连续2年测量了30个果园90棵苹果树冠层光谱反射率及LAI值;通过相关性分析方法构建并筛选出了最优的植被指数;利用支持向量机(support vector machine, SVM)与随机森林(random forests, RF)多元回归分析方法构建了LAI估测模型。新建的GNDVI₅₂₇,NDVI₆₇₆,RVI₆₈₂,FD-NVI₆₅₆和GRVI₅₁₇五个植被指数及前人建立的两个植被指数NDVI₆₇₀和NDVI₇₀₅与LAI的相关性都达到了极显著水平;建立的RF回归模型中,校正集决定系数C-R2和验证集决定系数V-R2为0.920,0.889,分别比SVM回归模型提高了0.045和0.033,校正集均方根误差C-RMSE、验证集均方根误差V-RMSE为0.249,0.236,分别比SVM回归模型降低了0.054和0.058, 校正集相对分析误C-RPD、验证集相对分析误V-RPD达到了3.363和2.520,分别比SVM回归模型提高了0.598和0.262,校正集及验证集的实测值与预测值散点图趋势线的斜率C-S和V-S都接近于1,RF回归模型的估测效果优于SVM。RF多元回归模型适合盛果期红富士苹果树LAI的估测。     

光在苹果组织中传输的光谱特性

用Monte-Carlo方法模拟研究了激光波长632 nm与750 nm的高斯光束在苹果组织中传输的吸收、散射光谱特性。结果表明：高斯光束特殊的能量分布特性对激光在苹果组织中的传播有重要的影响,苹果组织对750 nm激光的反射、吸收、透射都较低,有更多的光子在组织内部与组织相作用,能够更清楚的反映组织内部信息。因此,在生物组织中近红外光波的传输性比较好,便于应用于研究生物组织。     

Protein Enrichment of Apple Pomace and Use in Feed for Nile Tilapia

The purpose of this paper is to investigate the protein enrichment of apple pomace by Gongronella butleri through solid-state cultivation and addition of this material as feed for tilapia fry (Oreochromis niloticus). Factorial experimental design was used for the assessment of culture conditions to determine the effects of the source of nitrogen, initial moisture, and granulometry on the protein enrichment of apple pomace. During culture, the consumption of reducing sugars and the production of soluble protein were determined. The best conditions obtained were with urea (5% w/w), initial moisture of 70% and granulometry in the range from 0.85 to 1.70 mm, producing 19.63% of soluble protein. The fry submitted to the diet containing treated apple pomace presented an increase of 44% in body mass, demonstrating that apple pomace biotransformed may represent an excellent food supplement.     

苹果花期冠层光谱探测的规范化技术方法探讨

2008年和2009年,针对苹果花期冠层光谱进行了不同天气条件、不同探测时间、不同探头高度、不同探测角度条件下的冠层反射光谱探测试验,分析其对苹果花期冠层光谱特性的影响,探索规范化的光谱探测技术方法。结果表明,苹果花期冠层在不同条件下的反射光谱响应呈现出一定的规律性,特别是在760～1 350 nm近红外波段表现尤为明显。研究发现,随着光照强度的减弱,冠层光谱反射率降低,在晴天和少云时探测冠层光谱较理想;风速在2级以下时,冠层光谱较稳定;在10:00～15:00测定冠层光谱差异较小;探测高度以整个苹果树冠层充满光谱仪探头视场时光谱曲线较稳定;探头以垂直或近似垂直观测为最佳。在此基础上,提出了苹果花期冠层光谱探测的规范化技术方法,为苹果冠层光谱探测及信息提取提供了科学依据。     

Proteomic fingerprinting of apple fruit,juice, and cider via combinatorial peptide ligand libraries and MS analysis

Combinatorial peptide ligand libraries coupled to MS was applied to extensively map the proteome of apple fruit, and to detect its presence in commercial apple juice and cider to evaluate their authenticity and genuineness. Using the Uniprot_Malus database, 96 proteins were detected in apples, among which 30 proteins were specifically captured via combinatorial peptide ligand libraries. Next, three proteins, previously recognized in fruits, were found in apple juice, which were involved in cellular metabolism of fruit maturation and in allergenic reactions. On the other hand, only one Malus allergen was identified in cider beads eluate, demonstrating that the industrial processes did not prevent any negative effects in sensitive subjects. Thus, the present study not only increases the knowledge of the apple proteome but also offers a reliable analytical method to assess quality and genuineness of commercial products, which could be also used to inform consumers about the presence of allergens.     

苹果产地差异对可溶性固形物近红外光谱检测模型影响的研究

为更好地利用近红外光谱预测苹果可溶性固形物含量,减少产地差异对近红外光谱检测模型的影响,以4种不同产地的富士苹果为研究对象,采用基于x-y共生距离的样本划分方法分别对不同产地的苹果选取代表性样本作为校正集,利用偏最小二乘算法,建立和比较单一产地和混合产地下的苹果可溶性固形物近红外光谱检测模型,并结合竞争性自适应重加权算法(CARS)和连续投影算法(SPA)对苹果可溶性固形物的建模变量进行筛选。相比单一产地和其它混合产地模型,混合所有4种苹果产地的校正集样本建立的模型取得了最好的预测结果,另外,结合CARS-SPA筛选的16个特征波长,模型得到了进一步简化,其预测相关系数和预测均方根误差分别为0.978和0.441°Brix。结果表明,利用多个产地的苹果样本建立的混合模型,结合有效特征波长,可提高对苹果可溶性固形物含量的预测精度,减小产地差异对可溶性固形物近红外光谱检测的影响。     

Mapping of Malus domestica allergens by 2-D electrophoresis and IgE-reactivity

The importance of apple allergens has been repeatedly emphasized, and their presence has been confirmed both in pollen and in fruits. In the present study, a combination of proteomic tools have been used to build a complete allergen map of apple. The water-soluble fraction of an apple extract was precipitated using a phenol-based procedure and separated by 2-DE. Initially four previously classified allergens, Mal d 1, Mal d 2, Mal d 3 and Mal d 4, could be identified in Western blots with polyclonal rabbit antibodies directed to the four respective allergens, and subsequently matched to the bands recognized by several patient sera. Further, all four known apple allergens were localized on a 2-DE map and they were matched with spots recognized by sera of patients with different allergic patterns. Moreover, a new, putative allergen could be identified using MS. We evaluated the influence of post-translational modifications and the immunoreactivity under different analytical conditions. The comparison of different visualization methods for 2-DE gels and blots revealed that even very low concentrations of the intact epitopes are detectable by IgEs of patients, and therefore might be sufficient to trigger allergic symptoms in sensitized individuals.     

用遗传区间偏最小二乘法建立苹果糖度近红外光谱模型

为了简化苹果糖度预测模型和提高模型的精度,用遗传区间偏最小二乘法(GA-iPLS)建立苹果近红外光谱预测模型。应用结果表明,整个光谱划分为40个子区间, GA-iPLS选择其中的第4,6,8,11,18号共5个子区间联合建立苹果糖度模型。遗传区间偏最小二乘法所建的模型,其校正时的相关系数r_c和交互验证均方根误差RMSECV分别为0.962和0.334 6,预测时的相关系数r_p和预测均方根误差RMSEP分别为0.932和0.384 2。与全光谱模型相比,该方法建立的模型不论对校正集还是预测集,模型的预测能力都提高了许多,且模型得到了很大的简化：其实际采用的波数点个数比全光谱模型采用的波数点个数大大减少,主因子数也比全光谱少,由此建立的模型更加简洁、数据运算量也更少。     

高效液相色谱法测定苹果中的酚类物质

利用高效液相色谱法分析了红星、秦冠、小国光、黄元帅和富士 5个苹果品种果皮和果肉中小分子酚类物质的分布情况。色谱柱为C1 8柱 ( 1 5 0mm× 4 .6mm ,Φ5 μm) ,流动相为甲醇和水 ,柱温为 30℃ ,流速为 1 .2mL min,线性梯度洗脱 ,紫外检测器波长为 2 80nm。在此色谱条件下 ,各组分均得到良好分离。标准样品的线性范围为 0 .9983～ 0 .9999,平均回收率为 75 %～ 91 %。     

基于遗传算法的苹果糖度近红外光谱分析

遗传算法(GA)应用在偏最小二乘法(PLS)校正模型的波段优化选择中具有显著的效果。将遗传算法用于波段选择, 能更快达到最优解, 有效提高测量精度,减少建模所用变量。文章研究了在近红外苹果糖度无损检测中,遗传算法作为模块进行波段选择,建立了GA-PLS模型;为了说明遗传算法优选波段可行性,另外建立了全谱和经验谱区的PLS定量模型,并评价了模型的稳健性。首先对傅里叶变换近红外光谱进行多元散射校正、Savitky-Golay卷积平滑后,用遗传算法优选波段(R-SGA),参与建模数据点从原始1 550减少到434个。然后采用一阶导数光谱建立GA-PLS模型,相比全谱PLS(1 550个数据点)和经验谱区PLS(717个数据点)模型具有更高的预测精度,其建模结果为R_C=0.966,RMSEC=0.469,R_P=0.954,RMSEP=0.797。结果表明, 遗传算法可用于PLS法建立苹果糖度校正模型前的数据优化筛选, 有效提高测量精度, 并减少建模变量。