Revealing the Effect of Heat Treatment on the Spectral Pattern of Unifloral Honeys Using Aquaphotomics

In this study we aimed to investigate the effect of heat treatment on the spectral pattern of honey using near infrared spectroscopy (NIRS). For the research, sunflower, bastard indigo, and acacia honeys were collected from entrusted beekeepers. The honeys were not subject to any treatment before. Samples were treated at 40 °C, 60 °C, 80 °C, and 100 °C for 60, 120, 180, and 240 min. This resulted in 17 levels, including the untreated control samples. The 5-hydroxymethylfurfural (HMF) content of the honeys was determined using the Winkler method. NIRS spectra were recorded using a handheld instrument. Data analysis was performed using ANOVA for the HMF content and multivariate analysis for the NIRS data. For the latter, PCA, PCA-LDA, and PLSR models were built (using the 1300–1600 nm spectral range) and the wavelengths presenting the greatest change induced by the perturbations of temperature and time intervals were collected systematically, based on the difference spectra and the weights of the models. The most contributing wavelengths were used to visualize the spectral pattern changes on the aquagrams in the specific water matrix coordinates. Our results showed that the heat treatment highly contributed to the formation of free or less bonded water, however, the changes in the spectral pattern highly depended on the crystallization phase and the honey type.     

唾液表面增强拉曼光谱用于肺癌的诊断

表面增强拉曼光谱已经展示了其在低浓度体液的检测方面的优势。对21例肺癌患者和22例正常人的唾液进行了检测和区分。肺癌患者在多处波数位的峰强较正常人有所下降,只有少数峰强度上升且幅度较小。这些峰主要归属为蛋白质和核酸,表明肺癌患者唾液中这些成分的含量较正常人为少。主成分分析法(PCA)和线性辨别分析(LDA)被用于两组数据的降维和区分,所得结果准确度为84%,灵敏度为94%,特异性为81%。     

线性辨别分析与人工神经网络用于血清光谱对于肝癌、肝硬化的诊断

本文探测了人体血清的自体荧光-拉曼光谱,采用多元统计方法中的主元分析法(PCA)对光谱进行分析,并利用线性辨别分析(LDA)作为诊断算法,与此同时,用人工神经网络进行交叉认证。PCA-LDA的灵敏度和特异性分别为88.00%和79.14%,PCA-ANN的为89.29%和94.74%。     

NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R²CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio.     

红外光谱结合PCA-LDA对嘉峪关戏台文物建筑彩画胶料种类的判别

为了判别嘉峪关戏台文物建筑彩画的胶料种类,采用皮胶、鱼鳔胶、蛋清、蛋黄、牛奶为参考样品,使用傅里叶红外光谱仪采集了参考样品及三件文物样品胶料的红外吸收光谱,利用主成分分析结合线性判别分析(PCA-LDA)构建胶料种类判别的数学模型,并应用于文物样品.发现参考样品红外光谱在1800～1000 cm-1区间包含了丰富的分子...     

Vis-NIR光谱模式识别结合SG平滑用于转基因甘蔗育种筛查

以Savitzky-Golay (SG)平滑筛选,主成分分析(PCA)分别结合有监督的线性判别分析(LDA)、无监督的系统聚类分析(HCA),应用于转基因甘蔗育种筛查的可见-近红外(Vis-NIR)无损检测。提出兼顾随机性、稳定性的定标、预测、检验框架;取田间种植处于伸长期甘蔗叶样品456个,具有Bt基因和Bar基因的转基因样品(阳)306个,非转基因样品(阴)150个;随机选取156个为检验集(阴性50、阳性106),余下为建模集(阴性100、阳性200,共300),建模集再随机划分为定标集(阴性50、阳性100,共150)、预测集(阴性50、阳性100,共150)共50次;扩充SG平滑点数,同时删除绝对值偏小的高阶导数模式,共264个平滑模式用于模型筛选;采用前3个主成分两两组合,再根据模型效果选出最优主成分组合;基于所有定标、预测集划分和SG平滑模式,建立SG-PCA-LDA和SG-PCA-HCA模型,根据平均预测效果优选参数,使模型具有稳定性;最后用检验集进行模型检验。经SG平滑后,PCA-LDA和PCA-HCA的建模精度、稳定性均显著改善;最优SG-PCA-LDA模型阳性、阴性样品检验识别率分别达到94.3%和96.0%;最优SG-PCA-HCA模型阳性、阴性样品检验识别率分别达到92.5%和98.0%。结果表明：Vis-NIR光谱模式识别结合SG平滑可用于转基因甘蔗叶的准确识别,提供了一种简便的转基因甘蔗育种筛查方法。