Qualitative identification of Aucklandiae Radix with LLE + SMA classification models based on electronic nose / 中草药

Objective: To establish an odor fingerprint with electronic nose technology to qualitatively identify Aucklandiae Radix odor from different producing areas. Methods: Eight batches of Aucklandiae Radix samples from eight different producing areas were collected. The odor information of each sample was obtained by electronic nose. The LDA algorithm based on Fisher’s identification criterion and the nonlinear dimensionality reduction LLE + SMA algorithm were used to distinguish the Aucklandiae Radix odor of different origins. Results: It was found that the LDA algorithm based on Fisher’s discriminant criterion could not distinguish the Aucklandiae Radix scent of different producing areas. Some of the samples in the place of origin had a lot of overlap, and the LLE + SMA algorithm could distinguish the odor very well. It can completely distinguish eight batches of Aucklandiae Radix samples from eight different producing areas. Conclusion: It is feasible to apply the electronic nose technology to the odor differentiation of Aucklandiae Radix from different producing areas, and provide new ideas and methods for the quality evaluation of Aucklandiae Radix.

Supervised locally linear embedding for magnetic resonance imaging based Alzheimer's disease classification / 生物医学工程学杂志

In order to solve the problem of early classification of Alzheimer's disease (AD), the conventional linear feature extraction algorithm is difficult to extract the most discriminative information from the high-dimensional features to effectively classify unlabeled samples. Therefore, in order to reduce the redundant features and improve the recognition accuracy, this paper used the supervised locally linear embedding (SLLE) algorithm to transform multivariate data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions. The 412 individuals were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including stable mild cognitive impairment (sMCI, = 93), amnestic mild cognitive impairment (aMCI, = 96), AD ( = 86) and cognitive normal controls (CN, = 137). The SLLE algorithm used in this paper is to calculate the nearest neighbors of each sample point by adding the distance correction term, and the locally linear reconstruction weight matrix was obtained from its nearest neighbors, then the low dimensional mapping of the high dimensional data can be calculated. In order to verify the validity of SLLE in the task of classification, the feature extraction algorithms such as principal component analysis (PCA), Neighborhood MinMax Projection (NMMP), locally linear mapping (LLE) and SLLE were respectively combined with support vector machines (SVM) classifier to obtain the accuracy of classification of CN and sMCI, CN and aMCI, CN and AD, sMCI and aMCI, sMCI and AD, and aMCI and AD, respectively. Experimental results showed that our method had improvements (accuracy/sensitivity/specificity: 65.16%/63.33%/67.62%) on the classification of sMCI and aMCI by comparing with the combination algorithm of LLE and SVM (accuracy/sensitivity/specificity: 64.08%/66.14%/62.77%) and SVM (accuracy/sensitivity/specificity: 57.25%/56.28%/58.08%). In detail the accuracy of the combination algorithm of SLLE and SVM is 1.08% higher than the combination algorithm of LLE and SVM, and 7.91% higher than SVM. Thus, the combination of SLLE and SVM is more effective in the early diagnosis of Alzheimer's disease.